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  • Wavelet Integrated Alternating Sparse Dictionary Matrix Decomposition in Thermal Imaging CFRP Defect Detection
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-16
    Junaid Ahmed; B. Gao; Wai Lok Woo

    With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP during the manufacturing process as well as the in-service procedure. Most common types of defects in the CFRP are debonds and delaminations. It is difficult to detect the inner defects on a complex shaped specimen by using conventional Nondestructive Testing (NDT) methods. In this paper, an unsupervised machine learning method based on wavelet integrated alternating sparse dictionary matrix decomposition (WIASDMD) is proposed to extract the weaker and deeper defects information for CFRP by using the optical pulse thermography (OPT) system. We propose to model the low rank and sparse decomposition jointly in an alternating manner. By incorporating the low rank information into the sparse matrix and vice versa, the weaker defects will be more efficiently extracted from noise and background. In addition, the integrated of wavelet analysis with dictionary factorization enables an efficient time-frequency mining of information and significantly removes the high frequency noise as well as boosts the speed of computations. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped CFRP specimens. A comparative analysis has also been undertaken to study the proposed method against the general OPT nondestructive testing (OPTNDT) methods.

  • Detection of stator short-circuit faults in induction motors using the concept of instantaneous frequency
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-16
    Reza Sadeghi; Haidar Samet; Teymoor Ghanbari

    Because of considerable importance of induction motors (IMs) in different industries, fault detection of these motors has gained importance in literature. One of the attractive approaches in this regard is motor current signature analysis (MCSA). Thanks to simplicity and its lower cost, this method has been preferred compared to the other methods. In this paper, first a model in which stator turn fault was taken into account is presented. Then, by stator current analysis using empirical mode decomposition (EMD), intrinsic-mode functions (IMFs) of the signals are derived. By selection of calculated IMF in the first row of the EMD, its instantaneous frequency (IF) is estimated using adaptive IF estimation. Variation of the estimated IF is utilized for detection of different stator turn faults. The method is evaluated using plenty of simulations and some experiments.

  • Networked Microgrids: State-of-the-art and Future Prospectives
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-15
    Mahamad Nabab Alam; Saikat Chakrabarti; Arindam Ghosh

    The operation of multiple microgrids (MGs) in coordination with distribution system enables high penetration of locally available distributed energy resources (DERs). This approach enhances the reliability and resiliency of the power supply significantly. Also, the overall cost of energy gets reduced because of the integration of cost-free power from photovoltaic panels and wind turbines. The most effective utilization of DERs can be achieved through networked MGs. However, the implementation of the concepts of networked MGs requires extensive research. This paper presents a comprehensive literature review of the most important research works on networked MGs. Major benefits and challenges related to this new and highly exploring area have been analyzed. Also, some of the most important research areas related to networked MGs have been highlighted and discussed as the future prospectives.

  • Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-15
    Sidra Aleem; Bin Sheng; Ping Li; Po Yang; David Dagan Feng

    The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

  • Multi-Task Policy Adversarial Learning for Human-Level Control with Large State Spaces
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-14
    Jun Ping Wang; YouKang Shi; Wen Sheng Zhang; Ian Thomas; Shihui Duan

    The sequential decision making problem with largescale state spaces is an important and challenging topic for multitask reinforcement learning(MTRL). Training near-optimality policies across tasks suffers from prior knowledge deficiency in discrete-time nonlinear environment, especially for continuous task variations, requiring scalability approaches to transfer prior knowledge among new tasks when considering large number of tasks. This paper proposes a multi-task policy adversarial learning (MTPAL) method for learning a nonlinear feedback policy that generalizes across multiple tasks, making cognizance ability of robot much close to human-level decision-making. The key idea is to construct a parametrized policy model directly from large high-dimensional observation by deep function approximators, and then trains optimal of sequential decision policy for each new task by adversarial process, in which simultaneously train two models: a multi-task policy generator transforms samples drawn from a prior distribution to samples from a complex data distribution with higher dimensionality, and a multi-task policy discriminator decides whether the given sample is prior distribution from human-level empirically derived or from the generator. All the related human-level empirically derived are integrated into the sequential decision policy, transferring humanlevel policy at every layer in a deep policy network. Extensive experimental testing result on four different WeiChai Power manufacturing data sets, shows that our approach can surpass human performance simultaneously from cart-pole to production assembly control.

  • Compressive Sensing and Morphology Singular Entropy-Based Real-time Secondary Voltage Control of Multi-area Power Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-13
    Irfan Khan; Yinliang Xu; Soummya Kar; Mo-Yuen Chow; Vikram Bhattacharjee

    This paper presents an improved secondary voltage control (SVC) methodology incorporating compressive sensing (CS) for a multi-area power system. SVC minimizes the voltage deviation of the load buses while CS deals with the problem of the limited bandwidth capacity of the communication channel by reducing the size of massive data output from the phasor measurement unit (PMU) based monitoring system. The proposed strategy further incorporates the application of a Morphological Median Filter (MMF) to reduce noise from the output of the PMUs. To keep the control area secure and protected locally, Mathematical Singular Entropy (MSE) based fault identification approach is utilized for fast discovery of faults in the control area. Simulation results with 27-bus and 486-bus power systems show that CS can reduce the data size up to 1/10th while the MSE based fault identification technique can accurately distinguish between fault and steady state conditions.

  • A multi-parameter numerical modeling and simulation of the dipping process in microelectronics packaging
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 
    Junhui Li; Haoliang Zhang; Can Zhou; Zhuo Chen; Xinxin Chen; Zhili Long; Xiaohe Liu; Wenhui Zhu

    In order to simulate the flux dipping process of micro-bump flip-chip bonding, multi-parameter experiments are designed. The dipping process of micro-bumps is captured by the high-speed camera, so that we can obtain the quantitative relationship between the dipping quantity and its viscosity, dipping speed, dipping depth, and dipping time by virtue of image processing. Furthermore, corresponding mathematical models are established by curve fitting. Finally, all of the numerical models are integrated; numerical simulation is carried out by MATLAB-Simulink and GUI; and a new simulation software is developed, thus providing an important basis and guidance for flux dipping application and parameter selection.

  • Online Energy Management for Multimode Plug-in Hybrid Electric Vehicles
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 
    Teng Liu; Huilong Yu; Hongyan Guo; Yechen Qin; Yuan Zou

    An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time ap-plication. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corre-sponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the glob-ally optimal method, dynamic programming (DP) and is superi-or to the charge depleting/charge sustaining (CD/CS) technique. Also, hardware in the loop (HIL) experiment is built to validate the real-time characteristic of the proposed strategy.

  • Secure real-time control through Fog computation
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 
    Kazuhiro Sato; Shun-ichi Azuma

    We consider an asymptotic stabilization problem with a specified confidential level for an IoT system composed of the Cloud, Fog, and a controlled device. A sequence of concealed states, which is a sequence of the sum of the true state of a controlled device and artificial noise, termed the security input, is transferred to the Fog. Because the method for concealing the true state is quite simple, it enables us to achieve rapid real-time communication between a controlled device and the Fog. The level of confidentiality is measured by using the mutual information between the true and concealed states. As a solution to our problem, we obtain Gaussian type security inputs and a convex optimization problem for calculating the feedback gains. Finally, we demonstrate the feasibility of our proposed method by solving the problem of tracking the reference signal of storage batteries in smart grids.

  • Family-based Big Medical-Level Data Acquisition System
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-09
    Jie Xu; Xiangdong Jian; Li Wang; Yunfeng Shen; Kaifen Yuan; Yue Nie; Yingxuan Tian; Xing Ma; Jinhong Guo

    At present, the market of network based family health management is undergoing a fast growth and predicted to be extremely huge in the near future. Integration between traditional digital device in a family and common healthcare products can lead to a new strategy for family health management. Hereby, we imbed blood glucose monitoring function into a normal TV Remote Controller, which serve as a glucometer. In addition to normal function of TV remote controller, the proposed device can also serve as a medical device-glucometer. The buried electrochemical module in TV remote controller is capable of resolving the current generated by a disposable glucose test strip. The retrieved blood glucose information is transmitted and displayed on TV, which can be further transferred to online personal health management center via network. Comparative study on reliability and accuracy between the integrated device and clinical biochemical analyzer shows good agreement. Taking account of rising concerns on daily healthcare, the proposed device holds great potentials in medical data acquisition for family based health management.

  • Enhanced Frequency Response From Industrial Heating Loads for Electric Power Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-09
    Yue Zhou; Meng Cheng; Jianzhong Wu

    Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced Frequency Response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. In this paper, a method was proposed to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks, and a decentralized control scheme was devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high and low frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia.

  • Fault-tolerant Oriented Hierarchical Control and Configuration of Modular Multilevel Converter for Shipboard MVDC System
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-07
    Lerong Hong; Qianming Xu; Zhixing He; Fu Jun Ma; An Luo; Josep M. Ge Guerrero

    Medium-voltage DC (MVDC) distribution system is considered as the promising power architecture of future shipboard power system. Fault-tolerance ability is of great importance for power system on ships. In this paper, zonal DC-DC conversion system based on modular multilevel converter (MMC) and three-phase bridge rectifier are proposed with its hierarchical fault-tolerant scheme. The topology configurations of the front-end MMC with multi-winding medium frequency transformer (MFT) under normal and post-fault circumstances are presented. The recon-figuration scheme and fault-tolerant control strategy are also proposed to ride-through sub-module (SM) level fault, phase level fault and bus level fault. Based on the hierarchical fault-tolerant scheme, redundant configuration of the MMC sub-module is further analyzed with the hierarchical reliability modelling. The effectiveness of the proposed control strategy is verified by the experimental results.

  • Industrial Internet of Things: Challenges, Opportunities, and Directions
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-02
    Emiliano Sisinni; Abusayeed Saifullah; Song Han; Ulf Jennehag; Mikael Gidlund

    Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

  • Efficient CU and PU Decision Based on Motion Information for Interprediction of HEVC
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-06
    Mei-Juan Chen; Yu-De Wu; Chia-Hung Yeh; Kao-Min Lin; Shinfeng D. Lin

    High-efficiency video coding encoders provide great improvements in coding efficiency and can also support higher resolution and multiple coding tools. The new coding structures such as coding unit (CU) and prediction unit (PU) have helped a lot, but the computational complexity is much higher than those of previous standards. This paper proposes a fast algorithm combining with CU and PU early termination decisions to reduce computational demand. Based on the analytic results, we can set up an adaptive threshold that can be obtained for early termination. Meanwhile, we also develop an adaptive search range determination according to the motion vector (MV). Compared with HM 12.0, our proposed method achieves an approximate 57% time saving, whereas the average Bjøntegaard-Delta Bit-rate (BDBR) increase is only 0.43%. In addition, our fast algorithm outperforms the previous works in both coding speed and coding performance.

  • Study on Dipping Mathematical Models for the Solder Flip-Chip Bonding in Microelectronics Packaging
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-12
    Junhui Li; Qing Tian; Haoliang Zhang; Xinxin Chen; Xiaohe Liu; Wenhui Zhu

    In order to develop the flux dipping process, a quantitative mathematical model that accurately describes the flux dipping process in the flip-chip bonding is proposed. The whole dynamic dipping process is captured by the high-speed transient imaging followed by image processing. Curve fitting of experimental results yields the relation between flux dipping quantity and process parameters. It suggests a quadratic function of quantity ( $Q$ ) with respect to dipping depth ( $d$ ), and a piece-wise function to dipping speed ( $v$ ) including an exponential section and a quadratic or linear section. While the coupled effect of d and v can be expressed as a merged function of $Q= f_{(d,v)}$ , these mathematical models are proved to be effective when applied to the actual dipping process practiced on a flip-chip bonding machine, thus providing a reliable mathematical basis for optimizing dipping flux in industrial manufacturing.

  • Estimation of Clock Skew for Time Synchronization Based on Two-Way Message Exchange Mechanism in Industrial Wireless Sensor Networks
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-30
    Heng Wang; Lun Shao; Min Li; Baoguo Wang; Ping Wang

    Time synchronization is indispensable for convenient network management, device monitoring, security, and other fundamental operations in industrial wireless sensor networks (IWSNs). Over the past few decades, a wide variety of highly accurate clock synchronization protocols have been investigated by employing powerful statistical signal processing techniques. However, most two-way exchange estimation schemes do not readjust the node's local clock upon every resynchronization before the clock parameters are estimated. And it may not be appropriate in IWSNs where time synchronization is consistently required. Based on the two-way message exchange mechanism, this paper investigates the clock synchronization schemes of active node and overhearing node with immediate clock readjustment. The maximum-likelihood estimators of the clock skew and the corresponding Cramer–Rao lower bounds are derived assuming Gaussian delays. Simulation and experimental results validate the performance of the proposed estimators.

  • Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-09
    Huaizhi Wang; Jiaqi Ruan; Guibin Wang; Bin Zhou; Yitao Liu; Xueqian Fu; Jianchun Peng

    Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks. However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies. Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. Then, in order to effectively detect the established cyber-attacks, an interval state estimation-based defense mechanism is developed innovatively. In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable. At last, a typical deep learning, i.e., stacked auto-encoder, is designed to properly extract the nonlinear and nonstationary features in electric load data. These features are then applied to improve the accuracy for electric load forecasting, resulting in a more narrow width of state variables. The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution. The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks.

  • Experimental Validation of an Explicit Power-Flow Primary Control in Microgrids
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-06
    Lorenzo Reyes-Chamorro; Andrey Bernstein; Niek J. Bouman; Enrica Scolari; Andreas M. Kettner; Benoit Cathiard; Jean-Yves Le Boudec; Mario Paolone

    The existing approaches to control electrical grids combine frequency and voltage controls at different time-scales. When applied in microgrids with stochastic distributed generation, grid quality of service problems may occur, such as under- or overvoltages as well as congestion of lines and transformers. The COMMELEC framework proposes to solve this compelling issue by performing explicit control of power flows with two novel strategies: 1) a common abstract model is used by resources to advertise their state in real time to a grid agent; and 2) subsystems can be aggregated into virtual devices that hide their internal complexity in order to ensure scalability. While the framework has already been published in the literature, in this paper, we present the first experimental validation of a practicable explicit power-flow primary control applied in a real-scale test-bed microgrid. We demonstrate how an explicit power-flow control solves the active and reactive power sharing problem in real time, easily allowing the microgrid to be dispatchable in real time (i.e., it is able to participate in energy markets) and capable of providing frequency support, while always maintaining quality of service.

  • A Technical Approach to the Energy Blockchain in Microgrids
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-15
    Maria Luisa Di Silvestre; Pierluigi Gallo; Mariano Giuseppe Ippolito; Eleonora Riva Sanseverino; Gaetano Zizzo

    The present paper considers some technical issues related to the “energy blockchain” paradigm applied to microgrids. In particular, what appears from the study is that the superposition of energy transactions in a microgrid creates a variation of the power losses in all the branches of the microgrid. Traditional power losses allocation in distribution systems takes into account only generators while, in this paper, a real-time attribution of power losses to each transaction involving one generator and one load node is done by defining some suitable indices. Besides, the presence of P–V nodes increases the level of reactive flows and provides a more complex technical perspective. For this reason, reactive power generation for voltage support at P–V nodes poses a further problem of reactive power flow exchange, which is worth of investigation in future works in order to define a possible way of remuneration. The experimental section of the paper considers a medium voltage microgrid and two different operational scenarios.

  • Energy Efficient Selected Mapping Schemes Based on Antenna Grouping for Industrial Massive MIMO-OFDM Antenna Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-07
    Byung Moo Lee

    Energy efficient massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) antenna systems have received a great deal of attention for use in industrial network applications due to the possibility of reducing operation costs and carbon footprint. One of the difficulties in realizing high energy efficiency (EE) massive MIMO-OFDM antenna systems is the high peak-to-average power ratio (PAPR) of the signal, which seriously limits the efficiency of power amplifiers (PA). Selected mapping (SLM) is a powerful PAPR reduction scheme for OFDM related systems, however, there is implicit consensus that SLM could not be applied to massive MIMO-OFDM antenna systems due to its high computational complexity and side information (SI) burden. In this paper, we propose an SLM-based PAPR reduction scheme that can be applied to massive MIMO-OFDM antenna systems based on antenna grouping. Using the antenna grouping based suboptimal scheme, we show that an SLM-based PAPR reduction scheme can be successfully applied to massive MIMO-OFDM antenna systems with significant increase of EE. The proposed scheme has very high flexibility with various adjustable parameters, so one can easily choose the settings they desire between performance-complexity tradeoff. Numerical analysis shows that the propose scheme can increase EE by $18.69\%$ compared with the conventional system.

  • Efficient Anonymous Password-Authenticated Key Exchange Protocol to Read Isolated Smart Meters by Utilization of Extended Chebyshev Chaotic Maps
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-16
    Dariush Abbasinezhad-Mood; Morteza Nikooghadam

    In smart grid, key exchange protocols play a vital role in providing secure channels to read consumption reports from the smart meters. Thus far, several key exchange schemes have been proposed for the networked smart meters. However, for the first time, quite recently, Sha et al . have presented an interesting two-phase authentication and key agreement scheme that exclusively aims at the isolated smart meters. In their scheme, they have properly addressed the computationally constrained smart meters by offering a lightweight key exchange protocol. Nevertheless, after meticulous observation, we found that their proposed scheme cannot resist the desynchronization attack and cannot provide the perfect forward secrecy. Moreover, there are some other weaknesses in their scheme. As a result, to tackle the existing security challenges, in this paper, by utilization of the extended Chebyshev chaotic maps, we propose an efficient anonymous password-authenticated key exchange protocol that not only is free from the limitations of Sha et al .'s scheme, but also provides the anonymity. The security analysis in the random oracle model and using the widely accepted ProVerif tool besides the computational and communication costs comparison demonstrate that the proposed scheme has reached a proper level of efficiency without sacrificing the desired security properties.

  • Generalized Dynamic Predictive Control for Nonparametric Uncertain Systems With Application to Series Elastic Actuators
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-09
    Yunda Yan; Chuanlin Zhang; Ashwin Narayan; Jun Yang; Shihua Li; Haoyong Yu

    One weakness of the model predictive control method is that the predicted states/outputs are constructed by an exact nominal model. Its accuracy varies if uncertainties exist, which will ultimately deteriorate the closed-loop control performances. To this end, we propose a generalized dynamic predictive control method for a class of lower-triangular systems subjected to nonparametric uncertainties. Instead of relying on the inherent robustness property of the standard predictive controller or on-/off-line parameter identification, a dual-layer adaptive law is designed to estimate the lumped effect of system uncertainties. As another main contribution, under a less ambitious but more practical control objective, namely semi-global stability, various nonlinearity growth constraints utilized in the existing related methods could be essentially relaxed. Numerical simulation and illustrative experimental tests of a series elastic actuator system are provided to demonstrate both simplicity and effectiveness of the proposed method.

  • A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-30
    Kai Zhang; Kaixiang Peng; Jie Dong

    This paper proposes a common and individual (CnI) feature extraction-based process monitoring (PM) method for tracking the operating performance and product quality of processes with multiple operating modes. Different from traditional methods that separately develop PM models concerning only the individual feature of each mode data, the new method seeks to build the PM model simultaneously from all mode data, including to acquire the common subspace that captures the common feature behind different modes, and the individual subspace that reflects the unique feature of each mode. The newly proposed framework is achieved using the conventional principal component analysis (PCA) and partial least squares (PLS) based methods. The resulting CnI-PCA-based operating performance monitoring method and CnI-PLS-based product quality monitoring method are applied to the typical multimode finishing mill process (FMP) where common configuration for all steel products and individual setting for each steel are existing. Finally, the practical application result shows that the proposed method can be preferable to detect and identify different faults in the multimode FMP.

  • Comparative Research of a Wound-Field Doubly Salient Generator With Different Rectifiers
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-12
    Yao Zhao; Huizhen Wang; Dongdong Li; Rongrong Qian

    The wound-field doubly salient generator (WFDSG) is receiving consideration for the field of industrial and aerospace/aviation, since it has the advantages of high robustness with simple structure and adjustable output voltage. In this paper, upon the analysis of generator structures, the features of three rectifiers that are suitable for WFDSG are analyzed in detail. The comparative research of WFDSG with hybrid half-bridge rectifier (HHBR), full-bridge rectifier (FBR), and half-bridge rectifier is presented by the finite-element method (FEM). Then, the load and no-load characteristics of WFDSG under the three rectifiers mentioned above are researched comparatively. The commutation overlap angles and total commutation output voltage losses of three rectifiers are deduced. The experiments of a 12/8-pole single armature-winding WFDSG (SAW-WFDSG) with an FBR and a 12/8-pole dual-armature-winding WFDSG (DAW-WFDSG) with HHBR prototypes have been performed. The FEM and experiment results shows that the SAW-WFDSG with FBR and the DAW-WFDSG with HHBR both have a good application prospect.

  • Design and Realization of Controller for Static Switch in Microgrid Using Wavelet-Based TSK Reasoning
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-12
    Ying-Yi Hong; Jun-Liang Gu; Fu-Yuan Hsu

    A microgrid may comprise several zones with local loads and distributed generation resources. If a fault occurs in a zone, then a static switch between the faulty and fault-free zones must be activated to detect/classify the faults and enable the breaker to open the circuit immediately. This paper presents a new method for designing a detection/classification module in a static switch using the Park Transform (PT), Multiresolution Analysis (MRA) and Takagi–Sugeno–Kang (TSK) fuzzy rules. Both wavelet coefficients/energies of the d -axis component, within 0.96–1.92 kHz, obtained by MRA and PT are employed to effectively detect and classify the fault types using the local fault signals near the static switch without a complicated communication system. The proposed TSK fuzzy rules, defined by the scenario data only, successfully classify the faults. The proposed method is verified by hardware-in-the-loop simulation using a real-time digital simulator and a realistic 5 kVA static switch. The proposed method is implemented in a Xilinx field-programmable gate array. Test results show that the proposed method is efficient in the real-time control environment of a microgrid.

  • SF-OEAP: Starvation-Free Optimal Energy Allocation Policy in a Smart Distributed Multimicrogrid System
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-28
    Nitesh A. Funde; Meera M. Dhabu; Parag S. Deshpande; Nita R. Patne

    The advancements in the smart grid involves high penetration of renewable energy resources into distribution system by building a microgrid. However, the intermittent nature of the nondispatchable generation leads to the issue of demand supply mismatch in the microgrid system. It will be sensible for microgrids to trade energy with one another. This requires a smart distributor, which collects data regarding superfluous energy from providers and takes fair decision of microgrid-generated energy allocation among consumers. This paper presents a starvation-free optimal microgrid-generated energy allocation policy (SF-OEAP) in a smart distributed multimicrogrid system. A novel optimal energy allocation policy is proposed wherein, prediction accuracy of net load forecast and revenue index of consumer microgrid are considered as vital parameters. In a prioritized energy distribution mechanism, it may happen that the consumer microgrid can be starved of microgrid-generated energy. Therefore, in addition to vital parameters, starvation parameter is considered in designing energy allocation policy for sustaining the consumers in the smart distributed multimicrogrid system. Furthermore, the proposed scheme uses weights to control the importance of each of the parameters in the energy allocation policy. The proposed SF-OEAP is simulated and justified so that it is stable enough to apply in a real-time electricity market.

  • Multiperiod Planning of Distribution Networks Under Competitive Electricity Market With Penetration of Several Microgrids, Part I: Modeling and Solution Methodology
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-19
    Mohammad Hadi Shaban Boloukat; Asghar Akbari Foroud

    According to the increasing penetration of microgrids (MGs) in power systems, the design and implementation of these new emerged structures are under the question of planners, operators, and investors of electrical energy sector. This paper aims at finding a response for this question to present a game-based model under a competitive market for long-term multiperiod planning of a distribution network containing several MGs, such that it can model the impact of decision making of the distribution system operator (DSO) on the MG investor and vice versa, also improve MG allocation algorithms. To achieve this, a mixed-integer nonlinear bilevel programming is employed, where in the external level, DSO's objectives and in the internal level, MGs’ objectives are optimized based on the dictation by DSO. Since solving this type of problems by employing the conventional tools is almost impossible, so a mathematical method is utilized to convert this problem to a standard form. In the proposed method, using the reformulation and linearization, the available binary variables are transformed to a continuous form and then are linearized.

  • A Universal Input CrCM Luo Converter With Low-Cost Pilot-Line Dimming Concept for General Purpose LED Lighting Applications
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-20
    Somnath Pal; Bhim Singh; Ashish Shrivastava

    This paper deals with the design and implementation of a Luo converter operating in two different modes with an improved power quality (PQ) and flickerless operation over the universal ac mains for general purpose lighting applications. A cost-effective analog pilot-line dimming concept is incorporated and discussed in detail. The proposed dimming concept offers good PQ, even during dimming, with low cost for the energy efficient lighting solutions. The Bode plot analysis of this proposed Luo converter is presented using state-space analysis to ensure its stability with closed-loop control. The prototype is developed and tested in the laboratory for a 36-W light-emitting diode lamp load with rated current of 700 mA. The measured power factor in case of 30% dimming is 0.97 with total harmonic distortion of 8.7% only. The measured efficiency is 91% at rated ac mains voltage of 230 V.

  • DC-Link Voltage Balance Control Strategy Based on Multidimensional Modulation Technique for Quasi-Z-Source Cascaded Multilevel Inverter Photovoltaic Power System
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-06
    Weihua Liang; Yushan Liu; Baoming Ge; Xilian Wang

    A dc-link voltage balancing control strategy for quasi-Z-source cascaded H-bridge (qZS-CHB) inverter photovoltaic (PV) power system is proposed by using multidimensional pulse-width modulation (MD-PWM) technique. The qZS-CHB PV system usually employs proportional-integral (PI) regulators based closed-loop control methods to balance dc-link voltages, combining with distributed maximum power point tracking and grid-tie power control. These require significant control computations and hardware resources for multiple modules based qZS-CHB system. The proposed MD-PWM for the qZS-CHB inverter performs shoot-through behavior based on geometric representation of the output voltage ranges of each H-bridge in coordinate axes. All possible switching sequences of the qZS-CHB inverter are taken into account to choose the most suitable sequence that can balance dc-link voltages of qZS modules in the qZS-CHB PV power system. When compared with the state-of-the-art of voltage balancing control methods, the proposed control strategy has advantages: 1) The computation is low because there is no PI controller, while the stability of the whole system is improved; and 2) there is a reliable balancing capability with fast regulation of dc-link voltages, owing to no extra controller parameters and handling the voltage balance in each control cycle. Simulation and experimental results of qZS-CHB inverter based grid-tie PV power system verify the proposed dc-link voltage balancing control technique.

  • Integrating Different Levels of Automation: Lessons From Winning the Amazon Robotics Challenge 2016
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-28
    Carlos Hernández Corbato; Mukunda Bharatheesha; Jeff van Egmond; Jihong Ju; Martijn Wisse

    This paper describes Team Delft's robot winning the Amazon Robotics Challenge 2016. The competition involves automating pick and place operations in semistructured environments, specifically the shelves in an Amazon warehouse. Team Delft's entry demonstrated that the current robot technology can already address most of the challenges in product handling: object recognition, grasping, motion, or task planning; under broad yet bounded conditions. The system combines an industrial robot arm, 3-D cameras and a custom gripper. The robot's software is based on the robot operating system to implement solutions based on deep learning and other state-of-the-art artificial intelligence techniques, and to integrate them with off-the-shelf components. From the experience developing the robotic system, it was concluded that: 1) the specific task conditions should guide the selection of the solution for each capability required; 2) understanding the characteristics of the individual solutions and the assumptions they embed is critical to integrate a performing system from them; and 3) this characterization can be based on “levels of robot automation.” This paper proposes automation levels based on the usage of information at design or runtime to drive the robot's behavior, and uses them to discuss Team Delft's design solution and the lessons learned from this robot development experience.

  • Multisource Fusion for Robust Road Detection Using Online Estimated Reliabilities
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-15
    Tran Tuan Nguyen; Jens Spehr; Sebastian Zug; Rudolf Kruse

    For highly available automated driving, a robust road estimation is indispensable. In order to tackle the challenges of this task, many works employ a fusion of multiple sources, e.g., visually detected lane markings, leading vehicle, digital maps, etc. However, each source has certain advantages and drawbacks depending on the operational scenarios. Hence, the assumption made by many existing approaches that the sources always are equally reliable for the fusion process is inappropriate. Therefore, this work proposes a novel concept by incorporating reliabilities into the multisource fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the reliability for each source is estimated online using classifiers trained with the sensor measurements, the past performance, and the context. Using real data recordings, experimental results show that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion.

  • Power Converters Based Advanced Experimental Platform for Integrated Study of Power and Controls
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-26
    Wenxin Liu; Jang-Mok Kim; Cheng Wang; Won-Sang Im; Liming Liu; Hao Xu

    With increasing interest in smart grid and renewable energy, significant investments have been allocated to promote related studies. Since there is a wide spectrum of topics to study, it is necessary to have an advanced experimental platform that can accommodate both system- and component-level studies, both hardware and algorithm designs, and both teaching and research. Unfortunately, such experimental platform is not commercially available. In this paper, an advanced power electronics based experimental platform is introduced. The system is consisted of one OPAL-RT real-time simulator, two one-bus microgrid testbeds, and two modular multilevel converters. The subsystems can form a multiple-bus microgrid testbed if connected through emulated power lines. The system can provide real-time simulation, controller hardware-in-the-loop (HIL) simulation, and power HIL simulation for power systems study. Various converter topologies can be configured with the modular converters for power electronics study. Both real-time simulator and DSP control boards can be used to implement advanced control algorithms. The designs and experiences shared in the paper will benefit many researchers that are in need of such system and promote power and energy related studies.

  • Multiscale Multitask Deep NetVLAD for Crowd Counting
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-02
    Zenglin Shi; Le Zhang; Yibo Sun; Yangdong Ye

    Deep convolutional networks (CNNs) reign undisputed as the new de-facto method for computer vision tasks owning to their success in visual recognition task on still images. However, their adaptations to crowd counting have not clearly established their superiority over shallow models. Existing CNNs turn out to be self-limiting in challenging scenarios such as camera illumination changing, partial occlusions, diverse crowd distributions, and perspective distortions for crowd counting because of their shallow structure. In this paper, we introduce a dynamic augmentation technique to train a much deeper CNN for crowd counting. In order to decrease overfitting caused by limited number of training samples, multitask learning is further employed to learn generalizable representations across similar domains. We also propose to aggregate multiscale convolutional features extracted from the entire image into a compact single vector representation amenable to efficient and accurate counting by way of “Vector of Locally Aggregated Descriptors” (VLAD). The “deeply supervised” strategy is employed to provide additional supervision signal for bottom layers for further performance improvement. Experimental results on three benchmark crowd datasets show that our method achieves better performance than the existing methods. Our implementation will be released at https://github.com/shizenglin/Multitask-Multiscale-Deep-NetVLAD .

  • Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-09
    Xiong Luo; Jiankun Sun; Long Wang; Weiping Wang; Wenbing Zhao; Jinsong Wu; Jenq-Haur Wang; Zijun Zhang

    Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

  • Securing Collaborative Deep Learning in Industrial Applications Within Adversarial Scenarios
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-06
    Christian Esposito; Xin Su; Shadi A. Aljawarneh; Chang Choi

    Several industries in many different domains are looking at deep learning as a way to take advantage of the insights in their data, to improve their competitiveness, to open up novel business possibilities, or to resolve the problem that thought to be impossible to tackle. The large scale of the systems where deep learning is applied and the need of preserving the privacy of the used data have imposed a shift from the traditional centralized deployment to a more collaborative one. However, this has opened up several vulnerabilities caused by compromised nodes and inputs, with traditional crypto primitives and access control models exploited to offer protection means. Providing security can be costly in terms of higher energy consumption, calling for a wise use of these protection means. This paper exploits game theory to model interactions among collaborative deep learning nodes and to decide when using actions to support security enhancements.

  • Shared Control Driver Assistance System Based on Driving Intention and Situation Assessment
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-13
    Mingjun Li; Haotian Cao; Xiaolin Song; Yanjun Huang; Jianqiang Wang; Zhi Huang

    This paper presents a shared control driver assistance system based on the driving intention identification and situation assessment to avoid obstacles. A constrained linear-time-varying model predictive controller is designed to follow the obstacle-avoidance path, which is obtained by the artificial potential method in real time. A human driver's driving intention and the desired maneuver are recognized by the inductive multilabel classification with an unlabeled data approach that is trained based on the lateral offset and lateral velocity to the road center line. In addition, the situation assessment of the collision risk is represented by the time to collision and the performance evaluation is designed according to lateral deviation. All of them are employed for the design of the shared control fuzzy controller. The cooperative coefficient, denoting the control authority between the controller and a human driver, is determined by three fuzzy controllers in different conditions, which are the consistent, the advanced inconsistent, and the lagged inconsistent fuzzy controller, respectively. More importantly, there are two scenarios studies provided to verify the proposed system. The results prove that the shared control driver assistance system can successfully help drivers to avoid obstacles and obtains great vehicle stability performance in different scenarios.

  • SSL: Smart Street Lamp Based on Fog Computing for Smarter Cities
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-20
    Gangyong Jia; Guangjie Han; Aohan Li; Jiaxin Du

    Both safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. Therefore, a street lamp is an indispensable part of the smart cities. However, current street lamps have lack of smart characteristics, which increases both danger and energy consumption. In order to address these problems, a smart street lamp (SSL) based on the fog computing for smarter cities is proposed in this paper. The advantages of the proposed SSL are as follows: 1) fine management, because every street lamp can be operated independently; 2) dynamic brightness adjustment, all street lamps can be adjusted dynamically; and 3) autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, stolen, and so on. The experimental results showed that the proposed SSL can improve the energy efficiency and reduce danger.

  • Automated Test Case Generation Based on Differential Evolution With Relationship Matrix for iFogSim Toolkit
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-18
    Han Huang; Fangqing Liu; Zhongming Yang; Zhifeng Hao

    Fog computing plays an important role in industrial and information process. The programs in fog computing, such as iFogSim toolkit, usually contain some infeasible paths (paths that cannot be covered), which makes it impossible to compare algorithm in models that require covering all paths. In this paper, we proposed a mathematical model to build automated test case generation based on path coverage (ATCG-PC) in fog computing programs as a single-objective problem. Single objective helps to reduce the cost of evaluation functions, which is proportional to the number of test cases. When infeasible paths are contained in tested programs, algorithms can also be compared in this model. In this paper, classical differential evolution (DE) is used to solve the ATCG-PC. However, it is difficult for DE to use generated test cases covering remaining paths in the ATCG-PC of fog computing. Therefore, we proposed a test-case-path relationship matrix to empower DE (RP-DE). Experiment results show that RP-DE uses significantly less test cases and achieves higher path coverage rate than compared state-of-the-art algorithms.

  • Guest Editorial Special Section on Industrial and Commercial Demand Response
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-05
    J. P. S. Catalão; P. Siano; F. Li; M. A. S. Masoum; J. Aghaei

    The eleven papers in this special section focus on the industrial and commercial potential of demand response (DR). Customers from this non-residential market base have great potential in providing flexibility for power systems through diverse demand response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if onsite control, information, and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation, and air conditioning systems. In the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral.

  • Internet-of-Things Hardware-in-the-Loop Simulation Architecture for Providing Frequency Regulation With Demand Response
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-13
    Matsu Thornton; Mahdi Motalleb; Holm Smidt; John Branigan; Pierluigi Siano; Reza Ghorbani

    Following recent advances in network infrastructure, cloud computing, and embedded systems, fascinating work is underway exploring the utility of demand response in increasing grid stability while permitting high penetration of intermittent renewable distributed generation resources. Although works have demonstrated diverse theoretical advantages of demand response programs, little real-world data are available and utilities generally remain reticent in moving forward with large-scale implementation due to risks inherent to any modification of the power system. Deciding that the next pertinent step in bringing demand response from theory to technology is developing an Internet-of-things hardware-in-the-loop simulation power system integrated device capable of empirically testing the theoretical mechanisms, this work presents an architecture testbed for providing demand response (telemetric monitoring and actuation of loads), which is real node in a power system simulation where virtual node's parameters derive real node data. We test a demand response algorithm, which provides frequency regulation services.

  • Planning Energy Storage and Photovoltaic Panels for Demand Response With Heating Ventilation and Air Conditioning Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-07
    Mohemmed Alhaider; Lingling Fan

    The objective of this engineering problem is to determine the size of a battery energy storage system and number of photovoltaic (PV) panels to be installed in a building with Heating Ventilation and Air Conditioning systems (HVACs) as the main load. The building is connected to the power grid where electricity price is varying at different hours. This engineering problem is formulated as an optimization problem with a goal to achieve minimum installation cost and operation cost while satisfying room temperature requirements. Stochastic PV outputs are taken into consideration as well. The mathematical problem formulated is a large-scale mixed integer linear programming (MILP) problem. To improve the solving speed, two Benders decomposition strategies are applied to solve this stochastic MILP problem. The optimization problem will lead to the battery energy capacity, power limit, number of PV to be installed, as well as theon/offstatus of HVACs over 8 h. The contribution of this paper is the implementation of Benders decomposition methods to reduce the computation complexity. Parallel computing structure and maximum feasible subsystem cut generation strategy have been exploited and implemented in this research.

  • Demand-Side Regulation Provision From Industrial Loads Integrated With Solar PV Panels and Energy Storage System for Ancillary Services
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-11
    Tat Kei Chau; Samson Shenglong Yu; Tyrone Fernando; Herbert Ho-Ching Iu

    Nowadays, enabled by current smart grid technology, electricity consumers can play an active role in providing ancillary service (AS) as a type of demand response. Participating AS can assist stabilizing the power grid by following the frequency regulation signal, or dynamic regulation signal (RegD) in this study while receiving economic benefits. Industrial loads are an indispensable component as a demand-side regulating resource of ancillary service due to their intensive electricity consumption. In this paper, we use grid-connected solar photovoltaics panels combined with the energy storage system (ESS) to produce continuous electricity consumption signals in order to follow the RegD signal. The participation of solar energy in real-time regulation provision process is emphasized, which is modeled based on a variety of operation modes in accordance to Australian Standard. Through a particular case study, with the integration of solar energy, the proposed method poses cost-effectiveness in industrial plant scheduling and a favorable load following capability, helping ensure the frequency stability of the electric grid. The proposed methodology is more economically advantageous compared to identical industrial loads only equipped with on-site ESS, and requires less switchings on machines compared to industrial plants with passive use of solar energy.

  • Power Market Load Forecasting on Neural Network With Beneficial Correlated Regularization
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-03
    Fang Yuan Xu; Xin Cun; Mengxuan Yan; Haoliang Yuan; Yifei Wang; Loi Lei Lai

    In day-ahead market (DAM), load serving entities (LSEs) are required to submit their future load schedule to market operator. Due to the cost computation, we have found the inconformity between load accuracy and cost of power purchase. It means that more accurate load forecasting model may not lead to a lower cost for LSEs. Accuracy pursuing load forecast model may not target a solution with optimal benefit. Facing this issue, this paper initiates a beneficial correlated regularization (BCR) for neural network (NN) load prediction. The training target of NN contains both accuracy section and power cost section. Also, this paper establishes a virtual neuron and a modified Levenberg–Marquardt algorithm for network training. A numerical study with practical data is presented and the result shows that NN with BCR can reduce power cost with acceptable accuracy level.

  • Optimal Price Based Demand Response of HVAC Systems in Multizone Office Buildings Considering Thermal Preferences of Individual Occupants Buildings
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-08
    Young-Jin Kim

    Thermal energy capacity of buildings can be coupled to power networks via heating, ventilating, and air-conditioning (HVAC) units. Optimizing the operation of HVAC systems in multizone buildings is a challenging task, as occupants have different thermal preferences dependent on time-varying indoor and outdoor environments. To estimate the social cost of demand response (DR), building aggregators need to assess occupants’ thermal discomfort (TD), which is related to their productivity outcomes. This paper proposes a price-based DR strategy for multizone office buildings to co-optimize the energy cost of HVAC units and the TD levels of occupants. To overcome simplified TD representations, a mobile interface is developed that grants occupants the ability to indicate their personal thermal preferences. These preferences are modeled using artificial neural networks, which are explicitly and directly integrated into the optimal DR scheduling. In addition, we evaluate the thermal response of a multizone office building to the operation of a variable speed heat pump (VSHP). Using the models of occupants’ TD and building thermal response, the optimization problem for the proposed DR strategy is formulated and solved with mixed-integer linear programming. The case study results verify that the proposed strategy successfully optimizes VSHP operations and occupants’ TD levels, mitigating the risk of occupant interruption to the optimal DR schedule.

  • A Heuristic-Based Smart HVAC Energy Management Scheme for University Buildings
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-05
    Anish Jindal; Neeraj Kumar; Joel J. P. C. Rodrigues

    Energy management in commercial buildings is a challenging task due to their specific set of requirements. One such building that has not been fully investigated in the literature to provide energy efficiency is a university building. There are many challenges associated while managing the energy of a university building, such as-scheduling of classes, availability of faculty, and capacity of classrooms. To address these challenges for providing better energy efficiency, an efficient heating, ventilation, and air-conditioning (HVAC) management scheme for a university building is presented in this paper. The HVAC loads are chosen as these are more flexible in the classrooms than other loads, such as-lighting and projectors. In this paper, the HVAC energy management problem is formulated as a mixed-integer linear programming (MILP) problem. To solve this problem, a heuristic-based algorithm is proposed, which optimally minimizes the use of HVAC without affecting user comfort. Moreover, it also minimizes the cost of rescheduling the classes on a given day. The results obtained on the dataset traces taken from a university building clearly indicate that the proposed scheme reduces the energy demand of HVAC systems by 19.75% for an entire week without affecting the user comfort. Moreover, this scheme shows superior performance when compared with existing commercial demand response management schemes with respect to load reduction and cost savings.

  • Data Center Control Strategy for Participation in Demand Response Programs
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-16
    Lisette Cupelli; Thomas Schütz; Pooyan Jahangiri; Marcus Fuchs; Antonello Monti; Dirk Müller

    This paper presents a framework for the optimal operation of data centers, leveraging their heating, ventilation, and air conditioning unit, delay-tolerant information technology workload and battery storage system for participating in demand response programs. In this context, an model predictive control based control framework has been developed that guarantees the reliable operation of the data centers core activities. We derive a modeling approach to represent the dynamics of the data centers subsystems and validate it for a data center test bed via practical experiments. Hereby, the thermal subsystem leads to deviations of less than 0.60 K in the modeled outlet temperature. The validated model is used for incremental prototyping of the proposed control via simulations under uncertainties. The results demonstrate a mean absolute error of the relative deviations between the data center consumption and the target load profile of 2.71% for an incentive-based scenario and a cost reduction of 3.86% for a price-based scenario.

  • Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-17
    Niloofar Pourghaderi; Mahmud Fotuhi-Firuzabad; Moein Moeini-Aghtaie; Milad Kabirifar

    Regarding the potentials of activating commercial consumers in demand response programs, this paper proposes a new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network. The TVPP enables presence of commercial buildings and other distributed energy resources in day-ahead (DA) electricity market, as a price maker agent. In this regard, a bilevel optimization framework is designed to optimize the bidding strategy of TVPP in the DA market with the main goal of maximizing TVPP profit. The upper-level problem maximizes the TVPP profit, while in the lower level, market is cleared from independent system operator viewpoint. Using Karush—Kuhn–Tucker optimality conditions and strong duality theory, the proposed bilevel problem is transformed into mixed integer linear programming problem. Implementing the model on the Roy Billinton Test System (RBTS) test system demonstrates the applicability of the proposed model.

  • Optimal Demand Response Scheduling for Water Distribution Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-02
    Konstantinos Oikonomou; Masood Parvania; Roohallah Khatami

    As energy intensive infrastructures, water distribution systems (WDSs) are promising candidates for providing demand response (DR) and frequency regulation services in power systems operation. However, models that tap the full flexibility of WDSs to provide the services while respecting the operational constraints of water networks are remained scarce. This paper proposes a comprehensive framework for optimizing the participation of water distribution system operators (W-DSOs) in DR and frequency regulation markets, which captures the joint flexibility of variable frequency pumps and water tanks and takes into account the underlying hydraulic operating constraints of WDSs. The proposed framework consists of two optimization models, where the first-step model optimizes the operation of water pumps and tanks for minimizing the W-DSO's water procurement cost, and the second-step model optimizes the DR and frequency regulation up and down offers by modifying the operation of water pumps and tanks, such that the W-DSO's profit of providing the services is maximized. The proposed model ensures the availability of services by taking into account the interdependence and compatibility of the DR load reduction and load recovery and the frequency regulation up and down services. In addition, the proposed models incorporate a detailed formulation of water distribution networks and the associated hydraulic constraints, ensuring deliverability of the services to power systems. The nonlinear terms appearing in the WDS constraints are linearized to convert the proposed models to instances of mixed-integer linear programming problems. The proposed model is implemented on a 15-node WDS, using the energy and ancillary service prices of the California ISO. The results reflect significant profit opportunities for the W-DSO by providing DR and frequency regulation services in the markets.

  • Quantifying the Potential Economic Benefits of Flexible Industrial Demand in the European Power System
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-08
    Dimitrios Papadaskalopoulos; Roberto Moreira; Goran Strbac; Danny Pudjianto; Predrag Djapic; Fei Teng; Michael Papapetrou

    The envisaged decarbonization of the European power system introduces complex techno-economic challenges to its operation and development. Demand flexibility can significantly contribute in addressing these challenges and enable a cost-effective transition to the low-carbon future. Although extensive previous work has analyzed the impacts of residential and commercial demand flexibility, the respective potential of the industrial sector has not yet been thoroughly investigated despite its large size. This paper presents a novel, whole-system modeling framework to comprehensively quantify the potential economic benefits of flexible industrial demand (FID) for the European power system. This framework considers generation, transmission, and distribution sectors of the system, and determines the least-cost long-term investment and short-term operation decisions. FID is represented through a generic, process-agnostic model, which, however, accounts for fixed energy requirements and load recovery effects associated with industrial processes. The numerical studies demonstrate multiple significant value streams of FID in Europe, including capital cost savings by avoiding investments in additional generation and transmission capacity and distribution reinforcements, as well as operating cost savings by enabling higher utilization of renewable generation sources and providing balancing services.

  • Investigation of Carrier Demand Response Uncertainty on Energy Flow of Renewable-Based Integrated Electricity–Gas–Heat Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-26
    Hamid Reza Massrur; Taher Niknam; Mahmud Fotuhi-Firuzabad

    Since there are heavy interdependencies among the electrical, heat, and gas systems to supply various load types worldwide, operation of multi-energy carrier (MEC) systems faces critical challenges. Moreover, any uncertainty rising in one carrier would directly influence the energy flow and the secure operation of the whole MEC system. This issue intensifies when an MEC system is integrated with industrial energy carrier demand response (ECDR) consumers and renewable energy sources (RESs). Large industrial ECDR consumers can increase the system uncertainty by randomly participating in the demand response programs and utilizing various carriers. Accordingly, this paper presents a powerful probabilistic tool named 2 m + 1 point estimate strategy for energy flow analysis of an integrated MEC system considering ECDR, RES, and various load types uncertainties to control the risks associated with the uncertainties. In addition, a new decomposition technique is presented to accelerate the energy flow solving of the integrated MEC systems. This technique has been promoted by adding a novel noniterative method named holomorphic embedding and the less-computational graph methods for solving the energy flow of the MEC. The proposed probabilistic energy flow is tested on an integrated MEC system employing various incentives for industrial ECDR consumers.

  • Guest Editorial Advanced Mechatronics in Research and Industrial Applications
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-05
    Yousef Ibrahim; Toshiyuki Murakami; Hideki Hashimoto; Péter Korondi

    The six papers in this special section focus on the use of advanced mechatronics in research and industrial applications. Mechatronics has evolved during the last few decades to become well recognized as a philosophy of design and an engineering discipline. It has emerged through the philosophy of concurrent design of mechanics, electronics, and computer of a system for an integrated and efficient approach to system design. Most universities nowadays offer a degree in mechatronics. This stems from the fact that as technology progresses, the traditional barriers between engineering disciplines are dimensioning. Mechatronics evolution went through many stages to reach its current advanced stage.

  • A Friction Model-Based Frequency Response Analysis for Frictional Servo Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-21
    Yoshihiro Maeda; Kazuya Harata; Makoto Iwasaki

    This paper presents a friction model-based frequency response analysis (FRA) method, which gives a precise linear mechanical dynamics model to design effective controllers and analyze accurate control characteristics for frictional servo systems. As well known, frequency-domain identification approaches using a sine sweep are widely used to obtain linear dynamics. However, nonlinear friction in the mechanism varies the apparent frequency-domain characteristic of the linear dynamics due to the nonlinearity. The proposed FRA estimates effective excitation thrust for actual linear dynamics in the sine sweep movement, by means of a friction model as well as a phase delay model. Theoretical analyses show that the proposed FRA can identify the correct linear dynamics, preventing influence of nonlinear friction as well as phase delay properties included in a plant system. The effectiveness of the proposed FRA is verified through theoretical analyses and experiments both in frequency and time domains, in comparison to two conventional FRA methods.

  • Interpolation of a Clothoid Curve Based on Iterative True-Value Prediction Considering the Discretization Error
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-25
    Issei Takeuchi; Seiichiro Katsura

    Computerized numerical control (CNC) systems are widely used in the processing field. The key technology of these systems is the interpolation that connects the desired points. The interpolation using a clothoid curve is one of the effective methods for connecting the points because of its outstanding smoothness properties. A clothoid curve is a spatial function. Hence, synthesization with a time function is necessary to apply the curve for the actual CNC equipment. The clothoid curve can be easily synthesized with a time function by designing the tangential velocity as the time function. However, a numerical calculation is necessary because the clothoid curve has a limitation in that it does not have an analytical expression. Therefore, the discretization error of the numerical calculation must be considered because the calculating resolution of the clothoid curve is determined by the tangential velocity and the sampling time. This study proposes a clothoid interpolation based on an iterative true-value prediction. This method can immediately estimate the true value of a clothoid curve by pinching integration and Aitken acceleration even in the case where the calculation resolution is limited. Hence, the method can suppress the discretization error of the clothoid curve. A smooth interpolation for the CNC machines can be achieved using the proposed method.

  • Design and Evaluation of a Remote Actuated Finger Exoskeleton Using Motion-Copying System for Tendon Rehabilitation
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-23
    Simon Lemerle; Takahiro Nozaki; Kouhei Ohnishi

    Hand recovery process is a major issue in the rehabilitation field as hands are vital to perform most daily activities. This paper presents the design and evaluation of a one actuated degree-of-freedom exoskeleton finger using flexible actuator to assist patients in rehabilitation process and more specifically in tendon recovery protocols. The control strategy based on the motion-copying system is used to be able to take advantages of the sensation of the patient. The wearability and adaptability of the device are improved, compared to other specific devices, mainly by the use of remote actuation and light-weight materials. Evaluation of the device in terms of wearability, adaptability, repeatability and accuracy of the position estimation are conducted. All these criteria are confirmed. The next step is to test the device in actual recovery protocols to evaluate its efficiency.

  • Human Motion Analysis and Its Application to Walking Stabilization With COG and ZMP
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-26
    Seonghye Kim; Kiichi Hirota; Takahiro Nozaki; Toshiyuki Murakami

    A stability index has been generally used in a gait system of formulaic structure. In this standardized structure, a human model has a center of gravity (COG), while a robot device has a zero moment point (ZMP) with COG as the stability standard in the walking motion. In the gait system of wearable type, the stability of human model is closely related with the robot device, in other words, the COG and ZMP are located in the limited domain of human motion and robot task. In this paper, the human and device are designed individually and the knee joint of human is unconstrained from the device work to allow the human reliance. The stability index that includes the COG and ZMP is defined. The validation of this index is guaranteed through a few tests to describe a correlation between the stability index and the human model condition. Based on the validity of the stability index, the experiment for the walking stabilization is implemented with the ZMP estimation by reaction torque observer.

  • Data-Based Predictive Hybrid Driven Control for a Class of Imperfect Networked Systems
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-26
    Truong Q. Dinh; James Marco; David Greenwood; Kyoung K. Ahn; Jong I. Yoon

    A data-based predictive hybrid driven control (DPHDC) approach is presented for a class of networked systems compromising both computation and communication delays, packet dropouts, and disturbances. First, network problems are classified in a generic way, which is used to design a network problem detector capable of detecting online current delays and packet dropouts. Second, a single-variable first-order proportional-integral based adaptive grey model [PIAGM(1,1)] is designed to predict future network problems, and to predict system disturbances. Third, a hybrid driven scheme integrated optimal small buffer (OSB) is constructed to allow the system to operate without any interrupts due to large delays or packet dropouts. Furthermore, the OSB size is online optimized using an adaptive grey fuzzy cognitive map technique. Forth, a prediction-based model-free adaptive controller is developed to compensate for network problems. The DPHDC stability is theoretically proved, while its effectiveness is demonstrated through a case study.

  • An Efficient Iterative Learning Approach to Time-Optimal Path Tracking for Industrial Robots
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-07-02
    Armin Steinhauser; Jan Swevers

    In pursuit of the time-optimal motion of an industrial robot along a desired path, a previously identified model is typically used to calculate the required inputs for perfect tracking. An inevitable model-plant mismatch, however, causes the obtained inputs to be suboptimal—resulting in poor tracking performance—or even be infeasible by exceeding given limits. This paper, at hand, presents a two-step iterative learning algorithm that compensates for such model-plant mismatch and finds the time-optimal motion, improving tracking performance, and ensuring feasibility. Due to an efficient solution of the path tracking problem using a sequential convex log barrier method, the delay between consecutive task executions is eliminated. To show the effectiveness of the proposed algorithm, an experimental validation on a standard industrial manipulator is performed, illustrating that the developed approach is capable of reducing the execution time while at the same time improving the tracking performance.

  • Distributed Approach for Temporal-Spatial Charging Coordination of Plug-in Electric Taxi Fleet
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 
    Zaiyue Yang; Tianci Guo; Pengcheng You; Yunhe Hou; S. Joe Qin

    This paper considers a city with a large fleet of plugin electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real time information. In the first stage, a thresholding method is proposed to assist PET driver choosing a proper time slot for charging, with comprehensive consideration of state of charge (SOC) of PET, time varying income and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the travelling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate that the three-fold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance the utilization ratio for CSs, and also flatten the unevenness of charging request for power grid.

  • Fv-SVM based Wall Thickness Error Decomposition for Adaptive Machining of Large Skin Parts
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 
    Qingzhen Bi; Xinzhi Wang; Qi Wu; LiMin Zhu; Han Ding

    Large skin parts play an important role in the aerospace industry. The wall thickness of the machined pocket in the skin part needs to be strictly controlled to ensure the transport capacity and structural strength. The wall thickness accuracy is generally decreased by various factors, such as the shaping error of the work-piece blank, fixing error, machine tool error and deformation caused by cutting force or internal stress. These factors are usually inevitable and stochastic due to the extremely weak rigidity and easy-to-deflect characteristics of the large skin parts. To ensure the wall thickness accuracy, a fuzzy v-support vector machine (Fv-SVM) based wall thickness error decomposition method is proposed. The wall thickness errors, which are monitored in the cutting process, are decomposed into spatial related errors and time related errors. The Fv-SVM based decomposition method with the principle of spatial statistical analysis is a data-driven approach for intelligent manufacturing. The data-driven method can consider all factors that affect the wall thickness accuracy, while the model-driven method usually only considers one factor, such as the work-piece deformation or fixing error. After decomposition, the spatial related wall-thickness error is off-line compensated, and the time related wall-thickness error is compensated by using a real-time strategy. The novel method can be applied to complex tool-paths. The cutting experiment of rectangular pockets in a large skin panel was conducted to verify the effectiveness of the proposed method. The wall-thickness accuracy can be improved to 0.05mm for the workpiece with only 2mm thickness.

  • Distributed Algorithm for Making Scale-free Network by Preferential Rewiring without Growth
    IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 
    Jae-Hyun Park

    The digital payment system that uses cryptocurrency, such as Bitcoin, is a distributed ledger working on a peer-to-peer network. We present a method to make a scale-free network for such applications. Using some biased physical quantities that are observable in sites, we can make the scale-free network through processes of cooperating distributed sites. Each node only proposes connecting to the more attractive node among randomly known nodes. The candidate node that is found by each node agrees to set two-way links if the requesting node is more attractive than the old node that already connected. Once they establish the new bidirectional relationship, they respectively remove the outgoing link to the less attractive node. We analytically calculate the connectivity distribution and show the scaling exponent is 2.5. By Monte Carlo simulations, we confirm that a power law distribution of the scaling exponent 2.5 describes the degree distribution of the topology.

Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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