High Voltage Gain Quasi-Switched Boost Inverters with Low Input Current Ripple IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-23 Minh-Khai Nguyen; Truong-Duy Duong; Young-Cheol Lim; Joon-Ho Choi
Two high voltage gain quasi-switched boost inverters (HG-qSBIs) are introduced in this paper. The proposed HG-qSBIs has the following characteristics: 1) continuous input current with low ripple; 2) reduced voltage stress on the capacitor, switch and diodes; 3) shoot-through immunity; 4) achieved high voltage gain with single-stage conversion; and 5) improve the output voltage capability with using high modulation index. A novel PWM control technique is proposed for the introduced HG-qSBI. Operating principle, circuit analysis, and passive component design guideline of the HG-qSBI are addressed. Comparison analysis between the introduced HG-qSBI and other Z-source-based high voltage gain inverters is presented. A prototype is made to test the introduced HG-qSBI. Simulation and experimental verifications are shown to prove the accuracy of the theoretical analysis.
Intelligent Impulsive Synchronization of Nonlinear Interconnected Neural Networks for Image Protection IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-23 Bin Hu; Zhi-Hong Guan; Naixue Xiong; Han-Chieh Chao
Inspired by security applications in the Industrial Internet of Things (IIoT), this paper focuses on the usage of impulsive neural network synchronization technique for intelligent image protection against illegal swiping and abuse. A class of nonlinear interconnected neural networks with transmission delay and random impulse effect is first formulated and analyzed in the paper. In order to make network protocols more flexible, a randomized broadcast impulsive coupling scheme is integrated into the protocol design. Impulsive synchronization criteria are derived for the chaotic neural networks in presence of nonlinear protocol and random broadcast impulse, with the impulse effect discussed. Illustrative examples are provided to verify the developed impulsive synchronization results and to show its potential application in image encryption and decryption.
Robust adaptive sliding mode control for switched networked control systems with disturbance and faults IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-23 Meng Li; Yong Chen
In this paper, the problem of switched networked control systems (SNCSs) with external disturbance and actuator /sensor faults is investigated. Meanwhile, the communication constraints such as network-induced delay, packet dropouts, and packet disorder are considered in communication network. A robust adaptive sliding mode control method is proposed for disturbance damping and faults tolerant, which is designed on an observer and second-order discrete-time adaptive sliding mode function. Furthermore, the reachability of sliding motion is proved. Then, the networked predictive control (NPC) method is employed to compensate the communication constraints. Finally, the stability of the closed-loop system is proved, and a numerical example and a mass-spring-damping system are used to verify the effectiveness of the proposed method.
An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-23 Qingchen Zhang; Laurence Tianruo Yang; Zheng Yan; Zhikui Chen; Peng Li
Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a non-trivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics. In the proposed model, the parameters are compressed significantly by converting the weight matrices to the canonical polyadic format. Furthermore, an efficient learning algorithm is designed to train the parameters. Finally, the proposed efficient deep learning model is applied to the workload prediction of virtual machines on cloud. Experiments are conducted on the datasets collected from PlanetLab to validate the performance of the proposed model by comparing with other machine learning-based approaches for workload prediction of virtual machines. Results indicate that the proposed model achieves a higher training efficiency and workload prediction accuracy than state-of-the-art machine learning-based approaches, proving the potential of the proposed model to provide predictive services for industry informatics.
ADAPTIVE TORQUE and FLUX CONTROL of SENSORLESS IPMSM DRIVE in the STATOR FLUX FIELD ORIENTED REFERENCE FRAME IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-21 Mohammadreza Moradian; Jafar Soltani; Abbas Najjar-Khodabakhsh; Gholamreza Arab Markadeh
In this paper, direct torque and flux control of interior permanent magnet synchronous motor (IPMSM) has been described in the stator flux field-oriented reference frame based on adaptive input-output feedback linearization (AIOFL) control method. The proposed control method does not need to know the motor two axis inductances, the rotor permanent magnetic flux linkages, and the rotor position. However, the knowledge of the stator resistance is mandatory and that is estimated by AIOFL method. In practice the rotor speed is approximately estimated by using the derivation of the stator flux vector angle in the stator stationary reference frame. The experimental and simulation results presented in this paper show the effectiveness and capability of the proposed control approach.
A Friction Model-Based Frequency Response Analysis for Frictional Servo Systems IEEE Trans. Ind. Inform. (IF 6.764) 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 sine sweep are widely used to achieve the 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 an effective excitation thrust for the 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 achieve the correct linear dynamics, preventing the influence of the nonlinear friction as well as phase delay properties. The effectiveness of the proposed FRA is verified by experiments both in frequency and time domains, in comparison to two conventional FRA methods.
A Multifunctional Three-Phase Four-Leg PV-SVSI with Dynamic Capacity Distribution Method IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-14 Jahangir Hossain; Fida Rafi; Graham Town; Junwei Lu
The unequal single-phase load distribution in three-phase (3P) four-wire (4W) low-voltage (LV) networks can cause significant neutral current and neutral to ground voltage rise problems at both customer and distribution transformer (DT) terminals. High neutral current can overload the neutral conductors and can cause electrical safety concerns to the users. To mitigate the high neutral current problem in an unbalanced residential LV network, a multifunctional 3P four-leg (4L) rooftop photovoltaic (PV) smart voltage source inverter (SVSI) is designed with improved active neutral current compensation along with active power export and point of common coupling (PCC) voltage regulation. A novel dynamic capacity distribution (DCD) method is proposed using the available SVSI capacity after active and reactive power operations to achieve higher capacity neutral compensation at PCC. The performance of the designed 3P-4L PV-SVSI with DCD method is compared with a traditional four-leg SVSI with fixed unbalanced compensation capacity and a passive unbalance compensator, such as a zig-zag transformer, in PSCAD/EMTDC software. Several case studies, such as balanced and unbalanced load changing effects, are presented with actual residential loads connected to an Australian 3P-4W LV network. A Semikron Semiteach modified inverter and a real-time TMSF28235 DSP microcontroller are used to provide experimental verification on the improvement of the proposed neutral current compensation with the DCD method. Detailed simulations and experimental studies are presented to verify the robustness and efficacy of the proposed control strategy with the designed 3P-4L PV-SVSI.
Design of Distributed Cyber-Physical Systems for Connected and Automated Vehicles with Implementing Methodologies IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-14 Yixiong Feng; Bingtao Hu; He Hao; Yicong Gao; Zhiwu Li; Jianrong Tan
With the development of communication and control technology, intelligent transportation systems have received increasing attention from both industry and academia. Intelligent transportation systems are supported by the Internet of Things, Cyber-Physical System, Artificial Intelligence, Cloud Computing and many other technologies, which supply fundamental information for connected and automated vehicles. Although plenty of studies have provided different formulations for intelligent transportation systems, many of them depend on Master Control Center. However, a centralized control mode requires a huge amount of data transmission and high level of hardware configuration and may cause communication delay and privacy leak. Some distributed architectures have been proposed to overcome the above problems but systematized technologies to collect and exchange information, process large amounts of data, model the dynamics of vehicles, and safely control the connected and automated vehicles are not explored in detail. In this paper, we proposed a novel distributed cyber-physical system for connected and automated vehicles in which every vehicle is modeled as a double-integrator using edge computing to analyze information collected from its nearest neighbors. The vehicles are supposed to travel along a desired trajectory and to maintain a rigid formation geometry. Related methodologies for the proposed system are illustrated and experiments are conducted showing that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.
Peak-to-Peak Filtering for Networked Nonlinear DC Motor Systems with Quantization IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-13 Xiao-Heng Chang; Yi-Ming Wang
This paper investigates the peak-to-peak filtering problem for a class of networked nonlinear DC motor systems with quantization. The nonlinear DC motor system is modeled by a Takagi-Sugeno (T-S) fuzzy model. Consider that the measurement output signal and the performance output signal of the system are quantized by two static quantizers before being transmitted by the digital communication channel, respectively. Attention is focused on the design of a peak-to-peak filter such that the filtering error system is asymptotically stable and satisfies the prescribed peak-to-peak filtering performance index. Sufficient conditions for such a peak-to-peak filter are expressed in the form of linear matrix inequalities. Finally, an illustrative example is given to show the effectiveness of the proposed approach.
A Virtual Phase-Lead Impedance Stability Control Strategy for the Maritime VSC-HVDC System IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-12 Wenhua Wu; Yandong Chen; Leming Zhou; Xiaoping Zhou; Ling Yang; Yanting Dong; Zhiwei Xie; An Luo
In the maritime VSC-HVDC system supplying power to passive network on the island, the dc-side of the system is prone to oscillation and even instability, which is resulted from the negative incremental input resistance characteristic of the inverter station. Firstly, a virtual phase-lead impedance stability control strategy aiming at rectifier station is proposed. This control strategy can ef-fectively mitigate the dc-side oscillation of the VSC-HVDC system without affecting the load performance of the in-verter station. Then, the dc impedance model of the VSC-HVDC system is built, including the output impedance of the rectifier station, dc cable impedance and the input im-pedance of the inverter station. In addition, the oscillation mechanism of the VSC-HVDC system is analyzed by im-pedance-based Nyquist stability criterion. The reason why rectifier station fails to mitigate the dc-side oscillation us-ing traditional virtual resistance stability control is that the output impedance of the rectifier station exhibits negative damping characteristic outside the control bandwidth of voltage outer-loop. For this issue, the proposed control strategy can correct the output impedance of the rectifier station to exhibit positive damping characteristic outside the control bandwidth of voltage outer-loop, thus mitigating the dc-side oscillation of the VSC-HVDC system. Finally, simulation and experiment results validate the proposed control strategy and analysis.
Comparative Research of Wound Field Doubly Salient Generator with Different Rectifiers IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-12 Yao Zhao; Huizhen Wang; Dongdong Li; Rongrong Qian
The wound field doubly salient generator (WFDSG) are 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 which 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 (HBR) 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 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 6.764) Pub Date : 2018-02-12 Ying Yi Hong; Jun-Liang Gu; Fu-Yuan Hsu
A microgrid may comprise several zones with critical loads or regular loads. If a fault occurs in a zone, then a static switch between the faulty and fault-free zones must be activated to enable the breaker to open the circuit immediately. This paper presents a new method for designing a detection/classification module in a static switch. The wavelet coefficient of the d-axis component of the fault voltage and the inference results of Takagi-Sugeno-Kang fuzzy reasoning using the wavelet energy of the fault current and ground current determine the activation of a static switch. The proposed method is applied to the first outdoor microgrid test bed in Taiwan, with a generation capacity of 360.5 kW. The proposed method is verified by hardware-in-the-loop simulation using a real-time digital simulator and a 5kVA static switch. Test results show that the proposed method is efficient in the real-time control environment of a microgrid.
Experimentable Digital Twins - Streamlining Simulation-based Systems Engineering for Industry 4.0 IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-12 Michael Schluse; Marc Priggemeyer; Linus Atorf; Jurgen Romann
Digital Twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the Internet of Things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings Digital Twins to life and makes them experimentable. Initially, these Experimentable Digital Twins (EDTs) communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in Virtual Testbeds, providing new foundations for comprehensive Simulation-based Systems Engineering. The EDTs become more detailed with every single application. Thus, a complete digital representation of the respective real assets and their behaviors is created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which simulation technology is used on the real hardware, thus realizing complex control algorithms, innovative user interfaces or Mental Models for intelligent systems.
MAC Protocols for Wake-up Radio: Principles, Modeling and Performance Analysis IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-12 Debasish Ghose; Frank Li; Vicent Pla
In wake-up radio (WuR) enabled wireless sensor networks (WSNs), a node triggers a data communication at any time instant by sending a wake-up call (WuC) in an on-demand manner. Such wake-up operations eliminate idle listening and overhearing burden for energy consumption in duty-cycled WSNs. Although WuR exhibits its superiority for light traffic, it is inefficient to handle high traffic load in a network. This paper makes an effort towards improving the performance of WuR under diverse load conditions with a twofold contribution. We first propose three protocols that support variable traffic loads, referred to respectively as CCA, backoff plus CCA, and adaptive WuC transmission. These protocols provide various options for achieving reliable data transmission, low latency, and energy efficiency for ultra-low power consumption applications. Then, we develop an analytical framework based on M/G/1/2 to evaluate the performance of these WuR protocols. Discrete-event simulations validate the accuracy of the analytical models.
Ontology-Assisted Engineering of Cyber-Physical Production Systems in the Field of Process Technology IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-12 Grischan Engel; Thomas Greiner; Sascha Seifert
Future cyber-physical production systems (CPPS) constitute a complex and dynamic network of services and plant components such as actuators and sensors. Consequently, the manual technical specification and design of these systems is a complex and time consuming task involving extensive expert knowledge. In scope of CPPS, current approaches to reduce the engineering effort focus on manufacturing technology. There are initial approaches in the domain of process engineering available. However, these neither consider the knowledge-supported definition of recipe-based operations nor the assignment of dynamic service networks to process modules. The objective of this contribution is the design of a concept and a systematic approach to automate the engineering of batch process plants respecting dynamic service networks and process modules using a knowledge-based assistance system. For this purpose, a declarative recipe description is combined with an ontological model. This enables an automatic inference of technical requirements. Based on this information, a multi-stage orchestration algorithm selects and combines process modules and networked services to find appropriate engineering solutions. Finally, a comprehensive case-study demonstrates that the proposed approach is able to automate target-oriented the selection and combination of process modules and service networks.
Study on dipping mathematical models for solder Flip-chip bonding in microelectronics packaging IEEE Trans. Ind. Inform. (IF 6.764) 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 flip-chip bonding is proposed. The whole dynamic dipping process is captured by high-speed transient imaging followed by image processing. Curve-fitting of experimental results yield 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 actual dipping process practiced on a flip-chip bonding machine, thus providing a reliable mathematical basis for optimizing dipping flux in industrial manufacturing.
Generalized Dynamic Predictive Control for Non-Parametric Uncertain Systems with Application to Series Elastic Actuators IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-09 Yan Yunda; Chuanlin Zhang; Ashwin Narayan; Jun Yang; Shihua Li; Haoyong Yu
One weakness of model predictive control (MPC) 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 (GDPC) method for a class of lower-triangular systems subjected to non-parametric 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, semiglobal 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 (SEA) system are provided to demonstrate both simplicity and effectiveness of the proposed method.
Deep Learning Based Interval State Estimation of AC Smart Grids against Sparse Cyber Attacks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-09 Huaizhi Wang; Jiaqi Ruan; Guibin Wang; Bin Zhou; Yitao Liu; Xueqian Fu; Jian-Chun Peng
Due to the aging of electric infrastructures, conventional power grid is being modernized towards 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 (ISE) 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 (SAE), is designed to properly extract the nonlinear and non-stationary features in electric load data. These features are then applied to improve the accuracy for electric load fore-casting, 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.
Adaptive Periodic-Disturbance Observer for Periodic-Disturbance Suppression IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-09 Hisayoshi Muramatsu; Seiichiro Katsura
Precise repetitive works have been widely conducted by automatic machines in industry. Then, periodic disturbances induced by the repetitive works must be compensated to achieve precise motions. In this paper, a periodic disturbance observer (PDOB) based on a disturbance observer (DOB) structure is proposed. The PDOB compensates a periodic disturbance including a fundamental wave and harmonics rather than a sinusoidal wave by using a time-delay element. Furthermore, an adaptive PDOB is also proposed to enable to compensate also frequency-varying periodic disturbances. An adaptive notch filter (ANF) is used in the adaptive PDOB, which estimates a fundamental frequency of the periodic disturbance. In experiments using a multi-axis manipulator, practical performances were verified. The proposals provide a new framework based on the DOB structure to design controllers using a time-delay element.
Self-Triggered Reduced-Attention Output Feedback Control for Linear Networked Control Systems IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-09 Erick Rodriguez-Seda
A major challenge in Networked Control Systems is to reduce the use of shared communication and control resources without compromising the performance of the closed-loop system. Motivated by this challenge, this paper presents an observer-based controller with irregular sampling that reduces the control attention and an observer-based, reduced-attention controller with irregular sampling that guarantees closed-loop stability and global ultimate boundedness of the plant's state vector regardless of bounded input and output disturbances. The feedback control design is based on self-triggered control, while the observer is based on the block-form state space representation of the plant. It is shown via analysis and simulation that the overall control law decreases the number of samples without loosing observability and closed-loop stability, reducing the utilization of communication resources.
3D Multi-Objective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-08 Bin Cao; Jianwei Zhao; Po Yang; Zhihan Ge Lv; Xin Liu; Geyong Min
Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time.we propose a novel distributed parallel cooperative coevolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications.
PPFA: Privacy Preserving Fog-enabled Aggregation in Smart Grid IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-08 Lingjuan Lyu; Karthik Nandakumar; Benjamin Rubinstein; Jiong Jin; Justin Bedo; Marimuthu Palaniswami
For constrained end devices in Internet of Things (IoT), such as smart meters, data transmission is an energy-consuming operation. To address this problem, we propose an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby smart meters and accurately derive aggregate statistics as the fine-grained Fog level aggregation. The Cloud/utility supplier computes overall aggregate statistics by aggregating Fog level aggregation. To minimize the privacy leakage and mitigate the utility loss, we use more efficient and concentrated Gaussian mechanism to distribute noise generation among parties, thus offering provable differential privacy guarantees of the aggregate statistic on both Fog level and Cloud level. In addition, to ensure aggregator obliviousness and system robustness, we put forward a two-layer encryption scheme: the first layer applies OTP to encrypt individual noisy measurement to achieve aggregator obliviousness, while the second layer uses public-key cryptography for authentication purpose. Our scheme is simple, efficient and practical, it requires only one round of data exchange among a smart meter, its connected Fog node and the Cloud if there are no node failures, otherwise, one extra round is needed between a meter, its connected Fog node and the trusted third party.
ANFIS Based Add-on Controller for Unbalance Voltage Compensation in Low Voltage Microgrid IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-08 Jaipal Saroha; Mukhtiar Singh; Dinesh Kumar Jain
This paper presents an adaptive add-on controller for the unbalance voltage compensation in low voltage microgrid (LVMG) constituting multiple voltage source converters (VSC) based distributed generation. Since, the VSC based LVMG is almost inertia-less system and any kind of load variations have very significant impact on voltage profile, which is highly undesirable. Presence of unbalance load at point of common coupling (PCC) further exaggerates the problem. In order to mitigate the negative effect of unbalance load, an ANFIS based add on control loop has been added in to the conventional VSC control. Here, the add-on controller sets the reference current gains equivalent to voltage unbalance factor. These reference current gains obtained from add-on controller are added to the output of voltage control loop to set the modified reference current for inner current control loop. The extensive simulation results with experimental validation have been provided to validate the proposed control algorithm.
Automatic Power Quality Events Recognition based on Hilbert Huang Transform and Extreme Learning Machine IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-08 Mrutyunjaya Sahani; P. K. Dash
In this paper, Hilbert Huang transform (HHT) and weighted bidirectional extreme learning machine (WBELM) are integrated to detect and classify power quality events (PQEs) in real-time. Empirical mode decomposition (EMD) is used to decompose the non-stationary PQEs into the mono-component mode of oscillation, known as intrinsic mode functions (IMFs). The efficacious features are extracted by applying the Hilbert transform (HT) on the IMFs. An efficient WBELM computational intelligence technique is proposed to recognize the single, as well as multiple PQEs and its performances are compared with the recently developed classifiers such as support vector machine (SVM), least-square support vector machine (LSSVM), extreme learning machine (ELM) and bidirectional extreme learning machine (BELM). The recognition architecture of HHT integrated with WBELM (HHT-WBELM) method is tested and compared with the empirical wavelet transform (EWT) associated with HT and WBELM (EWTHT-WBELM) method, and tunable- Q wavelet transform (TQWT) along with HT and WBELM (TQWTHT-WBELM) method. The faster learning speed, lesser computational complexity, superior classification accuracy, and short event detection time prove that the proposed HHT-WBELM method can be implemented in the online power quality monitoring system. Finally, a hardware prototype is developed based on the digital signal processor (DSP) to verify the cogency of the proposed method in real time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments
Energy Efficient Selected Mapping Schemes based on Antenna Grouping for Industrial Massive MIMO-OFDM Antenna Systems IEEE Trans. Ind. Inform. (IF 6.764) 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 trade-off. Numerical analysis shows that the propose scheme can increase EE by 18.69% compared to the conventional system.
Reliability-Based Scheduling for Delay Guarantees in Hybrid Wired/Wireless Industrial Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-07 Samuele Zoppi; Amaury Van Bemten; Murat Gursu; Mikhail Vilgelm; Jochen W. Guck; Wolfgang Kellerer
Industrial control systems are foreseen to operate over hybrid wired/wireless networks. While the controller will be deployed in the wired network, sensors and actuators will be deployed in a Wireless Sensor Network (WSN). To support QoS for control systems, an end-to-end delay bound and a target reliability must be provided in both wireless and wired domains. However, for industrial WSNs, guaranteeing reliability is a challenging task because of low-power communications and the harsh wireless environment. In this work, we present the first QoS framework for arbitrary hybrid wired/wireless networks, which guarantees that the delay bound and the target reliability of each application are provided. As part of this framework, we propose the first reliability-based scheduler for WSN able to achieve a target reliability in the presence of dynamic interference. Simulations of the proposed scheduler prove its suitability in different interference scenarios and motivate further work.
Restoring Aspect Ratio Distortion of Natural Images with Convolutional Neural Network IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-06 Ryuhei Sakurai; Sasuke Yamane; Joo-Ho Lee
We propose a method to restore aspect ratio distortion of images using convolutional neural network (CNN). Images can be distorted by vertical or horizontal stretching, which does not maintain aspect ratios. In the proposed method, we construct an aspect ratio estimator whose input are images and output are scalar aspect ratios. Since estimation of aspect ratio from image is regression problem, we modeled the estimator by CNN. Once we have a reliable estimate of aspect ratio of an image, the restoration can be done straightforwardly by inverse stretching. In the experiments, we evaluated performance of the model trained on Pascal VOC natural image dataset. Our method can precisely restore the distortion within 1.4% of stretch from original images on average, which outperforms average human performance (about 13%). In terms of accuracy, 99.86% of distorted images are successfully restored.
Efficient CU and PU Decision Based on Motion Information for Inter-Prediction of HEVC IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-06 Mei-Juan Chen; Wu Yu-De; Chia-Hung Yeh; Kao-Min Lin; Shinfeng D. Lin
High efficiency video coding (HEVC) encoders provide great improvements in coding efficiency and also can 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 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 can be obtained through for early termination. Meanwhile, we also develop an adaptive search range determination according to the MV. Compared with HM 12.0, our proposed method achieves an approximate 57% time saving, while the average BD-rate increase is only 0.43%. In addition, our fast algorithm outperforms the previous works in both coding speed and coding performance.
Experimental Validation of an Explicit Power-Flow Primary Control in Microgrids IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-06 Lorenzo Reyes-Chamorro; Andrey Bernstein; Niek J. Bouman; Enrica Scolari; Andreas 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; (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-flows primary control applied in a real-scale test-bed microgrid. We demonstrate how an explicit power-flows 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.
Performance evaluation and modeling of an industrial application-layer firewall IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-06 Manuel Cheminod; Luca Durante; Lucia Seno; Adriano Valenzano
The availability of performance studies and simple models for firewalls able to deal with industrial application-layer communication protocols, like Modbus/TCP, is crucial when the impact of these devices has to be estimated, even roughly, before their actual deployment in industrial networks. Unfortunately, most manufacturers do not provide this kind of information for COTS available products. Thus, a viable solution is the development and experimental validation of simple models that can be used by designers to predict those firewall characteristics not explicitly related to their security capabilities. As an example, latency introduced on message forwarding is an aspect of significant interest in many industrial control systems, where delays and jitters in data delivery can severely impact on the effectiveness of the control actions. This paper reports on our experience in developing a performance model for a commercial device able to perform advanced application-layer filtering, in particular of Modbus/TCP traffic. A set of ad-hoc designed experiments, performed by means of a purposely-developed laboratory testbed, enabled both model development and validation, confirming a good correspondence of the estimated performance with the device actual behavior.
Reservoir Computing Meets Smart Grids: Attack Detection Using Delayed Feedback Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-11-02 Kian Hamedani; Lingjia Liu; Rachad Atat; Jinsong Wu; Yang Yi
A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.
Thermal Pattern Contrast Diagnostic of Micro Cracks with Induction Thermography for Aircraft Braking Components IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-05 Yizhe Wang; B. Gao; Wai-Lok Woo; Gui Yun Tian; Xavier Maldague; Li Zheng; Zheyou Guo; Yuyu Zhu
Reciprocating impact load leads to plastic deformation on the surface of the kinematic chains in aircraft brake system. As a result, this causes fatigue and various complex natural damages. Due to the complex surface conditions and the coexistence damages, it is extremely difficult to diagnose micro cracks by using conventional thermography inspection methods. In this paper, the Thermal Pattern Contrast (TPC) method is proposed for weak thermal signal detection using eddy current pulsed thermography (ECPT). In this process, the extraction and subsequent separation differentiate a maximum of the thermal spatial-transient pattern between defect and non-defect areas. Specifically, a successive optical flow (OF) is established to conduct a projection of the thermal diffusion. This directly gains the benefits of capturing the thermal propagation characteristics. It enables us to build the motion context connected between the local and the global thermal spatial pattern. Principal Component Analysis (PCA) is constructed to further mine the spatial-transient patterns to enhance the detectability and sensitivity in micro crack detection. Finally, experimental studies have been conducted on an artificial crack in a steel sample and on natural fatigue cracks in aircraft brake components in order to validate the proposed method.
A Heuristic-based Smart HVAC Energy Management Scheme for University Buildings IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-05 Anish Jindal; Neeraj Kumar; Joel Jose Rodrigues
Energy management in commercial buildings is a challenging task due to their specific set of requirements. One such building is a university building which faces many challenges while managing its energy such as-scheduling of classes, availability of faculty, and capacity of classrooms. To address these challenges, an efficient heating, ventilation, and air-conditioning (HVAC) management scheme is presented 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 minimizes the re-scheduling cost of classes on a given day. The results obtained clearly indicate that proposed scheme reduces the energy demand of HVAC by 19.75% for an entire week without compromising user comfort. Moreover, it shows superior performance when compared with existing commercial DR management schemes with respect to load reduction and cost savings.
Performance Analysis of CSMA/CA and PCA for Time Critical Industrial IoT Applications IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-05 Pavana Ravi Sai Kiran Malyala; Rajalakshmi Pachamuthu
Recently proposed IEEE 802.15.4-2015 MAC introduced a new Prioritized Contention Access (PCA) for transfer of time-critical packets with lower channel access latency compared to CSMA/CA. In this paper, we first propose a novel Markov chain based analytical model for unslotted CSMA/CA and PCA for industrial applications. The unslotted model is further extended to derive the analytical model for slotted CSMA/CA and PCA. Primary emphasis is laid on understanding the performance of PCA compared to CSMA/CA for different traffic classes in industrial applications. The performance analysis shows the slotted PCA achieves a reduction of 63.3% and 97% in delay and power consumption respectively compared to slotted CSMA/CA, whereas unslotted PCA achieves a delay reduction of 53.3% and reduction of power consumption by 96% compared to unslotted CSMA/CA without any significant loss of reliability. The proposed analytical models for both slotted and unslotted IEEE 802.15.4-2015 MAC offer satisfactory performance with less than 5% error when validated using Monte Carlo simulations. Also, the performance is verified using real-time testbed.
Two-Step Interpolation Algorithm for Measurement of Longitudinal Cracks on Pipe Strings Using Circumferential Current Field Testing System IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-19 Xin'an Yuan; Wei Li; Guoming Chen; Xiaokang Yin; Weichao Yang; Jiuhao Ge
Pipe strings are critical facilities for well drilling, production, and transportation in oil and gas industry. Due to the stress corrosion cracking, longitudinal cracks are the most common defects on pipe strings. This paper presents a simple two-step interpolation algorithm based on a circumferential current field testing method for the measurement of longitudinal cracks on pipe strings. The theory and finite element method model of the circumferential current field testing method are analyzed. The two-step interpolation algorithm fitted by characteristic signals obtained from simulations is proposed to size longitudinal cracks. The first-step is to measure and calibrate the crack length by a quadratic polynomial interpolation formula and the second-step is to measure the crack depth by a cubic polynomial interpolation formula. Experiments are conducted to verify the efficacy of the proposed two-step interpolation algorithm based on the circumferential current field testing system. The results suggest that the two-step interpolation algorithm can obtain the length and depth information of longitudinal cracks effectively on pipe strings using the circumferential current field testing system.
Battery-Less Short-Term Smoothing of Photovoltaic Generation Using Sky Camera IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-27 Mojtaba Saleh; Lindsay Meek; Mohammad A. S. Masoum; Masoud Abshar
There is a growing concern over addressing the adverse effects of variations in the output power of distributed generators such as photovoltaic generation (PVG) systems that continue to be widely introduced into power networks. Nowadays, most network operators are requiring these intermittent energy resources to seek compliance with new regulations pertaining to the restriction of their export power fluctuations. This paper aims to investigate the smoothing of the export power fluctuations primarily attributed to clouds passing over the PVG plant, which are traditionally compensated by integrating a battery storage (BS) system. The idea of incorporating short-term solar prediction information into the conventional smoothing approach is examined to indicate how it affects the engagement of BS in the smoothing process. Afterward, an enhanced solar forecasting scheme based on whole-sky imaging is proposed and its performance is demonstrated through several real-time experiments complemented with simulation studies. The results reveal that the proposed PVG smoothing strategy is capable of successfully filtering rapid export power fluctuations to an acceptable extent and the conventional generation reserves will experience a negligible amount of remaining undesired power variation. This clearly bears out the hypothesis of battery-less PVG regulation.
Analysis, Design, and Implementation of Passivity-Based Control for Multilevel Railway Power Conditioner IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-31 Jun Min; Fujun Ma; Qianming Xu; Zhixing He; An Luo; Alfio Spina
In recent years, railway power conditioner (RPC) has been used to improve the power quality of a traction power system. In engineering application of RPC, mismatches between parameters used in controller and actual values are inevitable, which will increase the difficulty of current controller design and deteriorate compensation performance. In this paper, a passivity-based control (PBC) system is studied for multilevel RPC to enhance its tolerance for mismatches. According to the topology of multilevel RPC, equivalent electrical and mathematical models are developed. To employ PBC, Euler–Lagrange system model of RPC is established by Park ( αβ/dq ) transformation, and the passivity of RPC and stability of PBC are proved. On this basis, the robustness of PBC is analyzed and criteria for damping selection are derived. Finally, simulation and experiments have been carried out to verify the structure and control method in the paper.
Image Autoregressive Interpolation Model Using GPU-Parallel Optimization IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-07 Jiaji Wu; Long Deng; Gwanggil Jeon
With the growth in the consumer electronics industry, it is vital to develop an algorithm for ultrahigh definition products that is more effective and has lower time complexity. Image interpolation, which is based on an autoregressive model, has achieved significant improvements compared with the traditional algorithm with respect to image reconstruction, including a better peak signal-to-noise ratio (PSNR) and improved subjective visual quality of the reconstructed image. However, the time-consuming computation involved has become a bottleneck in those autoregressive algorithms. Because of the high time cost, image autoregressive-based interpolation algorithms are rarely used in industry for actual production. In this study, in order to meet the requirements of real-time reconstruction, we use diverse compute unified device architecture (CUDA) optimization strategies to make full use of the graphics processing unit (GPU) (NVIDIA Tesla K80), including a shared memory and register and multi-GPU optimization. To be more suitable for the GPU-parallel optimization, we modify the training window to obtain a more concise matrix operation. Experimental results show that, while maintaining a high PSNR and subjective visual quality and taking into account the I/O transfer time, our algorithm achieves a high speedup of 147.3 times for a Lena image and 174.8 times for a 720p video, compared to the original single-threaded C CPU code with -O2 compiling optimization.
A Real-Time Heterogeneous Emulator of a High-Fidelity Utility-Scale Variable-Speed Variable-Pitch Wind Turbine IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-06 Mohammed Moness; Muhammad Osama Mahmoud; Ahmed Mahmoud Moustafa
Wind energy has the highest development rates of renewables. The increasing complexity of wind turbine (WT) systems requires careful analysis and design with thorough testing and certification procedures. Hardware emulators contribute to safe and cost-effective assessment and testing of WT in research and industry. Most of the available emulators concentrate on emulating electrical subsystems with simplified mechanical models. In this paper, a real-time (RT) heterogeneous emulator that combines RT discrete-time step simulation and a high-fidelity linear parameter-varying model of a utility-scale WT system is proposed and implemented on a heterogeneous CPU/GPU platform. The RT emulator is built on an embedded NVIDIA Jetson TK1 board for a National Renewable Energy Laboratory 5-MW WT as a case study. The proposed emulator is capable of further integration of electrical models and control systems of WT.
Extracting and Defining Flexibility of Residential Electrical Vehicle Charging Loads IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-11 Amr A. Munshi; Yasser Abdel-Rady I. Mohamed
The popularization of electric vehicles raises concerns about their negative impact on the electrical grid. Extracting electric vehicle charging load patterns is a key factor that allows smart grid operators to make intelligent and informed decisions about conserving energy and promoting the stability of the electrical grid. This paper presents an unsupervised algorithm to extract electric vehicle charging load patterns nonintrusively from the smart meter data. Furthermore, a method to define flexibility for the collective electric vehicle charging demand by analyzing the time-variable patterns of the aggregated electric vehicle charging behaviors is presented. Validation results on real residential loads have shown that the proposed approach is a promising solution to extract electric vehicle charging loads and that the approach can effectively mitigate the interference of other appliances that have similar load behaviors as electric vehicles. Furthermore, a case study on real residential data to analyze electric vehicle charging trends and quantify the flexibility achievable from the aggregated electric vehicle load in different time periods is presented.
PPMA: Privacy-Preserving Multisubset Data Aggregation in Smart Grid IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-06-29 Shaohua Li; Kaiping Xue; Qingyou Yang; Peilin Hong
Privacy-preserving data aggregation has been extensively studied in smart grid. However, almost all existing schemes aggregate the total electricity consumption data of the whole user set, which sometimes cannot meet the fine-grained demands from control center in smart grid. In this paper, we propose a privacy-preserving multisubset data aggregation scheme, named PPMA, in smart grid. PPMA can aggregate users’ electricity consumption data of different ranges, while guaranteeing the privacy of individual users. Detailed security analysis shows that PPMA can protect individual user's electricity consumption privacy against a strong adversary. In addition, extensive experiments results demonstrate that PPMA has less computation overhead and no more extra communication and storage costs.
Critical Links Identification for Selective Outages in Interdependent Power-Communication Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-21 Bassam Moussa; Parisa Akaber; Mourad Debbabi; Chadi Assi
Critical infrastructure, such as the smart grid, is vulnerable to failures and attacks. The complex nature of these systems embeds hidden vulnerabilities that threaten their functionality when exploited. In this paper, we perform a vulnerability analysis of the smart grid based on the power flow dynamics and in the presence of the essential communication network. Our analysis identifies a small number of power lines and communication links that can trigger a cascading failure and result in a blackout when removed. We quantify the failure effect in the form of fractional loss in the served load. Moreover, we formulate a mathematical model to present both components of the smart grid and their interdependency. A scalable algorithm is introduced to analyze the output of the model. We evaluate the proposed model and algorithm on the IEEE 14, 30, 57, and 300 Bus systems and associated communication networks, and report on the collected results.
Classification and Discrimination Among Winding Mechanical Defects, Internal and External Electrical Faults, and Inrush Current of Transformer IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-06-28 Sajad Bagheri; Zahra Moravej; Gevork B. Gharehpetian
In this paper, the mechanical faults of transformers including the winding radial deformation and axial displacement on 1.6 MVA transformer winding are investigated. Then, by estimating the parameters of the detailed model of this transformer winding in MATLAB software and changing these parameters in a manner that is proportional to the mechanical defects in electro-magnetic transients program software, the sampled differential current of the transformer is extracted for each disturbance. Next, the internal and external electrical faults and inrush current of the transformer are simulated. Afterwards, these signals are analyzed using maximal overlap discrete wavelet transform with Daubechies4 wavelet function, and their features are extracted. These extracted features are considered for training the classifiers of Decision Tree and artificial neural network. According to the simulation results, the proposed procedure is capable of classifying and discriminating among winding mechanical defects, internal and external electrical faults, and inrush current with a good accuracy that is the main novelty of this paper in comparison to other published works, which are limited to classifying only some of the mentioned faults.
Multimodal Forecasting Methodology Applied to Industrial Process Monitoring IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-21 Daniel Zurita; Miguel Delgado; Jesus A. Carino; Juan A. Ortega
Industrial process modeling represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, accurate models of critical signals need to be designed in order to forecast process deviations. In this work, a novel multimodal forecasting methodology based on adaptive dynamics packaging and codification of the process operation is proposed. First, a target signal is decomposed by means of the empirical mode decomposition in order to identify the characteristic intrinsic mode functions. Second, such dynamics are packaged depending on their significance and modeling complexity. Third, the operating condition of the considered process, reflected by available auxiliary signals, is codified by means of a self-organizing map and presented to the modeling structure. The forecasting structure is supported by a set of ensemble adaptive-neurofuzzy-inference-system-based models, each one being focused on a different set of signal dynamics. The performance and effectiveness of the proposed method are validated experimentally with industrial data from a copper rod manufacturing plant and performance comparison with classical approaches. The proposed method shows improved performance and generalization over classical single-model approaches.
Multivariate Alarm Systems for Time-Varying Processes Using Bayesian Filters With Applications to Electrical Pumps IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-05 Wanqi Xiong; Jiandong Wang; Kuang Chen
Alarm systems are critically important for safety and efficiency of industrial plants. However, many alarm variables in contemporary alarm systems are generated in a way being isolated from related process variables, resulting in false and missing alarms. This paper is motivated by abnormality detection for condensate-water electrical pumps in thermal power plants and proposes a method to design multivariate alarm systems for time-varying processes. A novel feature to distinguish normal and abnormal conditions is observed on the variation rates of multiple linear regression model parameters. A model estimator based on Bayesian filters is formulated to track the variations of model parameters in normal conditions, and not to do so in abnormal conditions so that absolute cumulative modeling errors are large enough to raise alarms. The effectiveness of the proposed method is validated by industrial case studies.
Optical and Mechanical Excitation Thermography for Impact Response in Basalt-Carbon Hybrid Fiber-Reinforced Composite Laminates IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-24 Hai Zhang; Stefano Sfarra; Fabrizio Sarasini; Clemente Ibarra-Castanedo; Stefano Perilli; Henrique Fernandes; Yuxia Duan; Jeroen Peeters; Nicolas P. Avdelidis; Xavier Maldague
In this paper, optical and mechanical excitation thermography was used to investigate basalt-fiber-reinforced polymer, carbon-fiber-reinforced polymer, and basalt-carbon fiber hybrid specimens subjected to impact loading. Interestingly, two different hybrid structures including sandwich-like and intercalated stacking sequence were used. Pulsed phase thermography, principal component thermography, and partial least-squares thermography (PLST) were used to process the thermographic data. X-ray computed tomography was used for validation. In addition, signal-to-noise ratio analysis was used as a means of quantitatively comparing the thermographic results. Of particular interest, the depth information linked to Loadings in PLST was estimated for the first time. Finally, a reference was provided for taking advantage of different hybrids in view of special industrial applications.
Hierarchical Decentralized Optimization Architecture for Economic Dispatch: A New Approach for Large-Scale Power System IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-05 Fanghong Guo; Changyun Wen; Jianfeng Mao; Jiawei Chen; Yong-Duan Song
In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems. Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents. Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent . In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. Several case studies implemented on the IEEE 30-bus and the IEEE 118-bus are discussed and tested to validate the proposed method.
Consensus of Networked Euler–Lagrange Systems Under Time-Varying Sampled-Data Control IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-06-15 Wenbing Zhang; Yang Tang; Tingwen Huang; Athanasios V. Vasilakos
This paper is concerned with the consensus of multiple Euler–Lagrange systems with time-varying sampled-data control. Different from traditional sampled-data strategies, a time-varying sampled-data strategy is developed to realize the consensus of multiple Euler–lagrange systems, in which a function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler–Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.
A Dendritic Cell Immune System Inspired Scheme for Sensor Fault Detection and Isolation of Wind Turbines IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-29 Esmaeil Alizadeh; Nader Meskin; Khashayar Khorasani
In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified.
Identification of Flux Linkage Map of Permanent Magnet Synchronous Machines Under Uncertain Circuit Resistance and Inverter Nonlinearity IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-03 Kan Liu; Jianghua Feng; Shuying Guo; Lei Xiao; Zi-Qiang Zhu
This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq -axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.
ISOMAP-Based Spatiotemporal Modeling for Lithium-Ion Battery Thermal Process IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-23 Kang-Kang Xu; Han-Xiong Li; Zhen Liu
The real-time monitoring of temperature distribution in lithium-ion batteries (LIBs) is crucial for their safety and optimal operation in electrical vehicles. An accurate and effective thermal model is needed for online temperature monitoring since limited sensors are available in vehicle application. In this paper, a data-based spatiotemporal modeling method is researched for online estimation of temperature distribution of LIBs. First, Isometric Mapping (ISOMAP) method is used for time/space separation and model reduction. Then, the low-dimensional representation can be obtained in terms of ISOMAP based mapping functions. The unknown temporal dynamics in the low-dimensional space can be approximated using neural network model with parameters trained using extreme learning machine (ELM) algorithm. Finally, the spatiotemporal model of the thermal process can be reconstructed by integrating the neural network model and the mapping functions. The generalization bound of the proposed spatiotemporal model can be analyzed using Rademacher complexity. Simulation results showed that the proposed modeling method can model the LIB thermal process very well.
Optimal Offering of Demand Response Aggregation Company in Price-Based Energy and Reserve Market Participation IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-12 Srikanth Reddy Konda; Lokesh Kumar Panwar; Bijaya Ketan Panigrahi; Rajesh Kumar
This paper investigates the combined price-based scheduling/participation of generation company (GENCO) and demand response aggregation company (DRACO) in energy and reserve markets. The temporally coupled customer behavior can be better represented using the load profile attributes, when compared to the traditional approach with random willingness assignment. The proposed cost models for energy and reserve offerings consider the effect of load type, load pattern consumption, and availability/flexibility patterns of each type of load with time of use constraints. The load curtailment (LC) cost model accounts for criticality and willingness of the responsive loads via utilization factor and availability factors, respectively. The proposed cost models present a realistic picture of LC cost by eliminating the random willingness factor of the existing LC cost models. Thereafter, various cases of market participation with different reserve payment policies are formulated for combined participation of GENCO and DRACO. In addition, the sensitivity of participation decision of various entities to the seasonal load variation is examined for summer and winter loading profiles. The proposed cost models and scheduling framework is simulated using GENCO with ten thermal units and DRACO with various load types, profiles distributed across different load sectors comprising of commercial, residential, industrial, municipal, and agricultural loads. The combined participation resulted in improved market surplus with reduced GENCO surplus. Also, the energy and reserve market surplus dependence on seasonal load patterns is observed across different test cases and payment policies.
Image-Based Characterization of Alternative Fuel Combustion With Multifuel Burners IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-21 Markus Vogelbacher; Patrick Waibel; Jörg Matthes; Hubert B. Keller
Many industrial high-temperature processes such as cement production employ multifuel burners in order to achieve the required energy input with low-cost alternative fuel. So far, a constant operation of multifuel burners with high fractions of alternative fuel (>70%) is not possible due to inherent fluctuating fuel properties. Energy input and product quality are directly affected by varying points of combustion time, different scattering of fuel, and insertion of unburned fuel or chemical substances into the product. We propose an image-processing system based on infrared images that detects the alternative fuel streakline and derives parameters for the characterization of the flight and burning behavior. Using these parameters, an adjustment of the burner settings depending on the fluctuating fuel properties can be carried out. This automatic monitoring and control of the combustion process allows an increased use of alternative fuels in constant operation. Experimental data from a rotary kiln for cement clinker production are used to validate the image-processing system.
Unified System- and Circuit-Level Optimization of RES-Based Power-Supply Systems for the Nodes of Wireless Sensor Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-13 Ioannis Mandourarakis; Eftichios Koutroulis
An extensive utilization of wireless sensor networks has evolved during the last years for monitoring various environmental and artificial processes. When operating in remote locations, the nodes of wireless sensor networks are typically power supplied by an energy production and management system, comprising low-power renewable energy sources, a power electronic converter, and a battery-based energy storage unit. In this paper, a methodology is proposed for optimally designing the energy production and processing system of a wireless sensor network node simultaneously at both the renewable power-supply system level and the power converter circuit level, through a unified design process. The impact of the objective function type on the power-supply design is also investigated in this paper. Design optimization and experimental results are presented, which demonstrate that the optimized power-supply structures derived by applying the proposed optimization technique exhibit lower cost of generated energy compared to partially optimized or totally nonoptimized structures and by that reduce the cost of the overall wireless sensor network node.
Asset-Based Dynamic Impact Assessment of Cyberattacks for Risk Analysis in Industrial Control Systems IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-17 Xuan Li; Chunjie Zhou; Yu-Chu Tian; Naixue Xiong; Yuanqing Qin
With the evolution of information, communications, and technologies, modern industrial control systems (ICSs) face more and more cybersecurity issues. This leads to increasingly severe risks in critical infrastructure and assets. Therefore, risk analysis becomes a significant yet not well investigated topic for prevention of cyberattack risks in ICSs. To tackle this problem, a dynamic impact assessment approach is presented in this paper for risk analysis in ICSs. The approach predicts the trend of impact of cybersecurity dynamically from full recognition of asset knowledge. More specifically, an asset is abstracted with properties of construction, function, performance, location, and business. From the function and performance properties of the asset, object-oriented asset models incorporating with the mechanism of common cyberattacks are established at both component and system levels. Characterizing the evolution of behaviors for single asset and system, the models are used to analyze the impact propagation of cyberattacks. Then, from various possible impact consequences, the overall impact is quantified based on the location and business properties of the asset. A special application of the approach is to rank critical system parameters and prioritize key assets according to impact assessment. The effectiveness of the presented approach is demonstrated through simulation studies for a chemical control system.
A New Fault Classifier in Transmission Lines Using Intrinsic Time Decomposition IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-08-18 Mohammad Pazoki
As nonstationarity exists in fault signals of transmission lines, their classification and quantification remain a challenging issue. This paper presents a new scheme for feature extraction in an attempt to achieve high fault classification accuracy. The proposed scheme consists of three steps: first, the proper rotation components (PRCs) matrix of current signals captured from one end of the protected line is constructed using the intrinsic time decomposition, a fast time-domain signal processing tool with no need for sensitive tuning parameters. Second, the singular value decomposition and nonnegative matrix factorization are employed to decompose the PRCs into its significant components. Finally, eight new normalized features extracted from the output of the data processing techniques are fed into the probabilistic neural network classifier. The data processing techniques employed for classification substantially improve the overall quality of the input patterns classified and increase the generalization capability of the trained classifiers. The proposed scheme is evaluated through two simulated sample systems in the PSCAD/EMTDC software and field fault data. Moreover, the effects of the current transformer saturation, decaying dc component, and noisy conditions are evaluated. The comparison results and discussion regarding the different aspects of the problem confirm the efficacy of the proposed scheme.
A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-19 Miadreza Shafie-Khah; Pierluigi Siano
Home energy management (HEM) systems enable residential consumers to participate in demand response programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertainty related to renewable energies as well as the uncertainty of consumers’ behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances. In addition, technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the “response fatigue” phenomenon in the long-term period. In this paper, a stochastic model of an HEM system is proposed by considering uncertainties of electric vehicles availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the inhabitants’ satisfaction by introducing a response fatigue index. Different case studies indicate that the implementation of the proposed stochastic HEM system can considerably decrease both the customers’ cost and response fatigue.
Adaptive Signal Selection of Wide-Area Damping Controllers Under Various Operating Conditions IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-15 Tossaporn Surinkaew; Issarachai Ngamroo
Since operating conditions of power systems always change, the input and output signals of wide-area damping controller (WADC), which are selected at an operating point, may not be able to guarantee the damping effect at other operating points. This paper focuses on a new adaptive signal selection for WADC against several operating conditions, such as various load demands, control signal failure, line and generator outages, and effect of communication latency. The joint controllability and observability is used to determine the best input and output pairs of WADC at any operating points. Small-signal and transient stabilities study in the IEEE 50-machine system including renewable sources, i.e., wind and solar photovoltaic generators are conducted to evaluate the effect of the proposed method. Study result demonstrates that the WADC with the adaptive signal selection yields superior damping effect to the WADC with the fixed signal selection over wide range operations.
A Prediction Backed Model for Quality Assessment of Screen Content and 3-D Synthesized Images IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-26 Vinit Jakhetiya; Ke Gu; Weisi Lin; Qiaohong Li; Sunil Prasad Jaiswal
In this paper, we address problems associated with free-energy-principle-based image quality assessment (IQA) algorithms for objectively assessing the quality of Screen Content (SC) and three-dimensional (3-D) synthesized images and also propose a very fast and efficient IQA algorithm to address these issues. These algorithms separate an image into predicted and disorder residual parts and assume disorder residual part does not contribute much to the overall perceptual quality. These algorithms fail for quality estimation of SC images as information of textual regions in SC images are largely separated into the disorder residual part and less information in the predicted part and subsequently, given a negligible emphasis. However, this is in contrast with the characteristics of human vision. Since our eyes are well trained to detect text in daily life. So, our human vision has prior information about text regions and can sense small distortions in these regions. In this paper, we proposed a new reduced-reference IQA algorithm for SC images based upon a more perceptually relevant prediction model and distortion categorization, which overcomes problems with existing free-energy-principle-based predictors. From experiments, it is validated that the proposed model has a better capability of efficiently estimating the quality of SC images as compared to the recently developed reduced-reference IQA algorithms. We also applied the proposed algorithm to judge the quality of 3-D synthesized images and observed that it even achieves better performance than the full-reference IQA metrics specifically designed for the 3-D synthesized views.
Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-11-20 Hassan Harb; Abdallah Makhoul
The use of wireless sensor network for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since the sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, whereas the second one is based on the set-similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches.
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