WSN Design and Verification using On-board Executable Specifications IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-25 Salvatore Gaglio; Giuseppe Lo Re; Gloria Martorella; Daniele Peri
The gap between informal functional specifications and the resulting implementation in the chosen programming language is notably a source of errors in embedded systems design. In this paper, we discuss a methodology and a software platform aimed at coping with this issue in programming resource-constrained Wireless Sensor Network nodes. Whereas the typical development model for WSNs is based on cross compilation, the proposed approach supports high-level symbolic coding of abstract models and distributed applications, as well as their test and their execution, directly on the target hardware. As a working example, we discuss the application of our methodology to specify the functional behavior of a radio transceiver chip. The resulting executable specifications are augmented with automatically generated runtime verification code. Our approach is also compared to code development for two prominent WSN general-purpose operating systems.
Complex Network Based Cascading Faults Graph for the Analysis of Transmission Network Vulnerability IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-24 Xiaoguang Wei; Shibin Gao; Tao Huang; Ettore Bompard; Renjian Pi; Tao Wang
Transmission network vulnerability (TNV) assessment is a key issue in power systems to identify the vulnerable components against accidents or malicious threats. Recently, constructing the topological vulnerability indices, particularly extended topological indices, is a popular method to evaluate the network vulnerability. However, the topological vulnerability indices cannot reveal the mechanism of fault propagation. To overcome the shortcomings, this paper proposes a new method to assess the TNV through the cascading faults graph (CFG) based on fault chains, which is a statistical graph that comprehensively considers the physical, operational and structural features of electrical networks. Based on the complex network theory (CNT), the scale-free properties of the CFG are revealed through simulations on various transmission networks by corresponding the degree distribution of the CFG; then, the model constancy of the CFG is analyzed. Resorting to the CFG, a set of indices from the CNT is used to identify the vulnerable branches of transmission networks. Illustrative applications are applied to the IEEE 39-bus and 118-bus test systems to demonstrate the effectiveness of the proposed method.
Network Slicing in Industry 4.0 Applications: Abstraction Methods and End-to-End Analysis IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-23 Anders Ellersgaard Kalor; Rene Guillaume; Jimmy Jessen Nielsen; Andreas Mueller; Petar Popovski
Industry 4.0 introduces modern communication and computation technologies such as cloud computing and Internet of Things to industrial manufacturing systems. As a result, many devices, machines and applications will rely on connectivity, while having different requirements to the network, ranging from high reliability and low latency to high data rates. Furthermore, these industrial networks will be highly heterogeneous as they will feature a number of diverse communication technologies. Current technologies are not well suited for this scenario, which requires that the network is managed at an abstraction level which is decoupled from the underlying technologies. In this paper, we consider network slicing as a mechanism to handle these challenges. We present methods for slicing deterministic and packet-switched industrial communication protocols which simplifies the manageability of heterogeneous networks with various application requirements. Furthermore, we show how to use network calculus to assess the end-to-end properties of the network slices.
Leader-Follower Formation Control of USVs With Prescribed Performance and Collision Avoidance IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-23 Shude He; Min Wang; Shi-Lu Dai; Fei Luo
This paper addresses distributed leader-follower formation control problem for a group of unmanned surface vehicles (USVs) with prescribed performance and collision avoidance. The vehicle dynamics include both parametric uncertainties and uncertain nonlinear functions and are subjected to time-varying external disturbances. The control objective is to make each vehicle follow its reference trajectory and avoid collisions between neighboring vehicles. To provide transient performance specifications on the formation tracking errors, we enforce prescribed performance constraints on the transient response of tracking errors in the control design. With the help of tracking error transformation functions, we employ a novel logarithm Lyapunov function to guarantee non-violation of the prescribed performance constraints. Consequently, we design a distributed adaptive formation controller that ensures uniformly ultimate boundedness (UUB) of the closed-loop system with guaranteeing prescribed performance of formation errors and avoids collisions between neighboring vehicles. Simulation results demonstrate the performance of the adaptive formation controller.
FallDroid: An Automated Smart Phone based Fall Detection System using Multiple Kernel Learning IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-23 Ahsan Shahzad; Kiseon Kim
Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper presents a pervasive fall detection system developed on smartphones (SPs) namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the accelerometer signals. Comprising of the Threshold Based Method and Multiple Kernel Learning Support Vector Machine, the proposed algorithm uses novel techniques to effectively identify fall-like events and reduce false alarms. In addition to user convenience and low power consumption, experimental results reveal that the system detects falls with high accuracy(97.8%), sensitivity(99.5%), and specificity(95.2%), while achieving lowest false alarm rate of 1 alarm per 59 hours of usage.
Detection of micro solder balls using active thermography technology and K-means algorithm IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-22 Xiangning Lu; Zhenzhi He; Lei Su; Mengying Fan; Fan Liu; Guanglan Liao; Tielin Shi
Solder bump/ball technology has been extensively applied in microelectronic packaging industry. However, the size of solder balls/bumps as well as the pitch are getting smaller and smaller, conventional inspection techniques are insufficient for diagnosis of the defect. It is indispensable to explore new methods for solder joint inspection. In this paper, a nondestructive diagnosis system based on active thermography was proposed. The test vehicles, named as SFA1 and SFA2, were excited by the laser pulse, and the consequent thermal response of the packages were captured by a thermal imager. In order to improve the signal to noise ratio, the polynomial fit and differential absolute contrast (DAC) techniques were utilized to reconstruct the thermal images. Then the statistical features corresponding to each solder ball were extracted from the reconstructed thermal images, and used for clustering analysis with K-Means algorithm. The results show that all the solder balls were recognized accurately, which demonstrates that the intelligent system using active thermography and K-Means algorithm is effective for defects inspection in microelectronic packaging industry.
Energy and Labor Aware Production Scheduling for Industrial Demand Response Using Adaptive Multi-objective Memetic Algorithm IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-22 Xu Gong; Ying Liu; Niels Lohse; Toon De Pessemier; Luc Martens; Wout Joseph
Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices to reduce cost. This paper introduces a multi-objective optimization model which schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to the trade-off between energy and labor costs. An adaptive multi-objective memetic algorithm is proposed to leverage feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. Thus, adaptive balance remains between exploration of the NSGA-II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer demonstrate the effectiveness and efficiency of the algorithm. The proposed scheduling method enables intelligent production systems, where production loads and human workers are mutually matched and jointly adapted to real-time electricity pricing for cost-efficient production.
Smart Wristwatches Employing Finger-Conducted Voice Transmission System IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-22 Kwangsub Song; Joon-Hyuk Chang
We present a novel finger-conducted speech transmission system between an actuator in wristwatch and the ear bone of a user. If an individual wears a smart watch equipped with an actuator that can play speech sent via communication lines, speech vibrations propagate from the actuator to fingertips through human tissue and bone. When an individual places his or her finger into their ear, fingerconducted speech can be registered and heard. While listening to fingerconducted speech, sounds are muffled, significantly degrading the intelligibility of speech. To mitigate this problem, a formant enhancement filter is applied to the speech prior to being fed into the actuator. The impulse response of human tissue and bones between the fingertips and wrist on which the watch was worn was first estimated to account for distortion. Based on impulse response, a gain filter was designed to boost the sound spectrum to compensate for frequency distortion.
A Novel UKF-RBF Method Based on Adaptive Noise Factor for Fault Diagnosis in Pumping Unit IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-21 Zhou Wei; Xiaoliang Li; Yi Jun; Haibo He
Fault detection and diagnosis in the pumping unit is a challenging industrial problem for the system that exhibits nonlinearity, coupled parameters and time-varying noise. This paper proposes a novel unscented Kalman filter - radical basis function (UKF-RBF) method based on an adaptive noise factor (NAUKF-RBF) for fault diagnosis in the pumping unit. First, to avoid the curse of dimensionality, the Fourier descriptor method based on an approximate polygon is introduced to extract the features of the indicator diagram curve. Then, an RBF neural network is applied to establish the fault diagnosis model, and UKF is used to optimize the weighting, center, and width of the RBF neural network due to its strong nonlinear tracking performance. In particular, the adaptive noise factor method is presented to address the unknown and time-varying noise statistics in the actual production process. The proposed method is applied to the pumping unit system, and experimental results show the effectiveness and favorable recognition rate in classifying multiple faults.
Energy Management of PV-Storage Systems: Policy Approximations using Machine Learning IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-21 Chanaka Keerthisinghe; Archie Chapman; Gregor Verbic
In this paper, we propose a policy function approximation (PFA) algorithm using machine learning to effectively control PV-storage systems. The algorithm uses an offline policy planning stage and an online policy execution stage. In the planning stage, a suitable machine learning technique is used to generate models that map states (inputs) and decisions (outputs) using training data. The training data set is generated by solving a deterministic smart home energy management problem using a suitable optimization technique (e.g. mathematical programming or dynamic programming). In the execution stage, the model generated by the machine learning algorithm is then used to generate fast real-time decisions. Since the decisions can be made in real-time, the policy can rely on up-to-date information on PV and electrical demand. Moreover, we can use PFA models over a long period of time (i.e. months) without having to update them but still obtain similar quality solutions. Our results show that the solutions from the PFAs are close to the best solutions obtained using dynamic programming and approximate dynamic programming, which have the drawback of requiring an optimization problem to be solved before the beginning of each day or as new information on demand or PV become available.
Optimal power equipment sizing and management for cooperative buildings in microgrids IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-21 Ioannis Zenginis; John Vardakas; Jordi Abadal; Cynthia Echave; Moises Morato; Christos Verikoukis
We present a novel model for the optimal design and power management of a group of buildings with diverse load patterns that are able to exchange energy through a common DC bus. The determination of the optimal sizes of the photovoltaic arrays, energy storage systems and inverters, and the optimal scheduling of power exchanges are achieved through the formulation of a mixed integer linear programming problem. Furthermore, the Nash bargaining method is used in order to fairly distribute the cooperation profits among the participants. The proposed approach achieves the reduction of the microgrid's cost and carbon emissions compared to non-cooperative approaches, and promotes the enhancement of the microgrid's energy sufficiency by allowing energy exchanges among buildings with energy surplus and buildings with energy deficit. Our model also takes into account the case where additional buildings join the microgrid after the initial coalition establishment.
Power System Real-Time Emulation: A Practical Virtual Instrumentation to Complete Electric Power System Modelling IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-16 Ali Parizad; Sobhan Mohamadian; Mohammad Esmaeil Iranian; Josep M. Guerrero
Hardware-in-the-Loop (HIL) simulation is a technique that is being used increasingly in the development and test of complex systems. Real-world testing of an intricate system in a field like power plant can be challenging, time-consuming, expensive, and hazardous. HIL emulators allow engineers to test devices, thoroughly and efficiently, in a virtual environment with high reliability and minimum risk of defect. In this paper, the complete electric power system (including generator, turbine-governor, excitation system, transmission lines, transformer, external grid and related loads) is implemented in MATLAB/Simulink environment. Different virtual instrument (VI) pages are modeled in the graphical programming language of LabVIEW which enable fast and reliable measurement functions, such as data acquisition, archiving, real-time graphical display and processing. Interaction between MATLAB and LabVIEW is accomplished by generating a Phar Lap ETS Targets *.dll file which enables two software to exchange real-time data. Also, a real 1518 kW excitation system is considered as a test case for introduced HIL system. This equipment is connected to LabVIEW software through a National Instrument PXI technology. Different scenarios (electrical frequency/active power change, voltage step response and etc.) are simulated in the designed Power System Emulator (PSE). Validity of the implemented model for excitation system is verified by finding good matching between MATLAB and HIL simulation results.
Analytic Hierarchy Process Based Fuzzy Decision Fusion System for Model Prioritization and Process Monitoring Application IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-15 Zhiqiang Ge; Yue Liu
Many fault detection and identification methods have been de-veloped in recent years, whereas, each method works under its own assumption, which means a method works well under one condition may not provide a satisfactory performance under another condition. In this paper, several data-based process monitoring methods are used in order to provide an effective monitoring scheme for process under various conditions, and then the analytic hierarchy process approach is introduced for model prioritization, Comparing to the conventional ensemble systems, the proposed method is able to provide different priorities for different models in monitoring different process faults. Fur-thermore, a new fuzzy decision fusion system is designed for the purpose of online process monitoring. Effectiveness of developed method is verified through Tennessee Eastman benchmark pro-cess.
High-Dimensional Robust Multi-Objective Optimization for Order Scheduling: A Decision Variable Classification Approach IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-15 Wei Du; Weimin Zhong; Yang Tang; Wenli Du; Yaochu Jin
This paper tackles the high-dimensional robust order scheduling problem. A multi-objective evolutionary algorithm called constrained nondominated sorting differential evolution based on decision variable classification is developed to search for robust order schedules. The decision variables are classified into highly and weakly robustness-related variables according to their contributions to the robustness of candidate solutions. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust optimization problem. It is also unveiled that the order scheduling is greatly affected by the uncertain daily production quantities. The robust order schedules are able to provide more information on earliness/tardiness of the orders, which enhances the flexibility of the production.
Automated dynamic inspection using active infrared thermography IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-15 Ruben Usamentiaga; Yacine Mokhtari; Clemente Ibarra-Castanedo; Matthieu Klein; Marc Genest; Xavier Maldague
Active thermography is a proven technology used in a wide variety of applications. In the most common approach using a static configuration, the elements involved in the inspection do not move. This presents serious drawbacks when it is applied to the inspection of large products and machines. An alternative approach is the dynamic inspection, which enables the inspection of large and complex products with better resolution, but it is also extremely challenging as data reconstruction is necessary. This work analyzes two methods for dynamic inspection using active infrared thermography: the thermal photocopier and the line scan. Automatic robust methods are proposed to calculate the temperature-time history, producing a pseudo-static sequence that can be further processed using advanced data processing algorithms to improve defect detection. Results demonstrate the robustness of the proposed methods and the ability to inspect large products with excellent results.
A Multi-Domain Layered Approach in Development of Industrial Ontology to Support Domain Identification for Unstructured Text IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-11 Rajbabu Kumaravel; Sudha Selvaraj; Mala C
Due to the emergence of digital revolution and competitiveness in recent decades, almost all organizations and industries intend to develop solutions to extract information from unstructured documents. These documents comprise of information related to multiple divergent domains and therefore there is a need of a multi-domain knowledge base. Since recent research works suggest ontology as the predominant model, it is proposed to evolve a unified ontology modeling approach with multiple layers and divergent domains to support information processing from unstructured documents. The model is evolved by integrating relevant domains to facilitate cross domain query. Further as the features of unstructured documents span across multiple domains, domain identification is to be performed prior to any information processing. Hence, an attempt is made to identify the domain using the proposed ontology model. The proposed ontology is developed for the Thermal Power Plant Industry and domain identification is demonstrated with an example. A statistical similarity index is proposed to associate divergent volatile features of unstructured text with ontology knowledge for domain identification. The outcome of the proposal is evaluated using the proposed similarity index. A subsequent study to extract information using classified content with the support of Directed Acyclic Graph relationship is under progress. The merit of the proposal is its ability to extend its usage across multiple stages of information processing with distinctive purpose.
Induction Infrared Thermography and Thermal-Wave-Radar Analysis for Imaging Inspection and Diagnosis of Blade Composites IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-08 Ruizhen Yang; Yunze He; A. Mandelis; Nichen Wang; Xuan Wu; Shoudao Huang
Condition monitoring, nondestructive testing and fault diagnosis are currently considered crucial processes for on-condition maintenance (OCM) to increase the reliability and availability of wind turbines and reduce the wind energy generation cost. Carbon fiber reinforced plastics (CFRP) have been increasingly used to fabricate wind turbine blades. Delamination type damage is inevitable during manufacture or in-service of a CFRP blade. This inner (subsurface) flaw, usually difficult to be detected by artificial visual inspection or machine vision based on CCD or CMOS, severely degrades the load bearing capacity of a blade. Induction infrared thermography (IIT) is an emerging infrared machine vision inspection technology, which has the capability of insight to CFRP based on electromagnetic induction and heat conduction. This paper introduces photothermal thermal-wave radar (TWR) non-destructive imaging (NDI) to IIT, based on cross correlation (CC) pulse compression and matched-filtering and applies TWR principles to CFRP imaging inspection and diagnosis. The experimental studies carried out under the transmission mode have shown that TWR B-scan and phasegram can be used to inspect and diagnose subsurface delaminations in CFRP with improved SNR and shape identification. As a new machine vision inspection method, TWRI will play an important role in the OCM of wind turbine blade.
Measuring Two-Factor Authentication Schemes for Real-Time Data Access in Industrial Wireless Sensor Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-08 Ding Wang; Wenting Li; Ping Wang
Dozens of two-factor authentication schemes have been proposed to secure real-time data access in industrial wireless sensor networks (WSNs). However, more often than not, the protocol designers advocate the merits of their scheme, but do not reveal (or unconsciously overlooking) the aspects on which their scheme performs poorly. Such lack of an objective, comprehensive measurement leads to the unsatisfactory "break-fix-break-fix" cycle in this research area. In this paper, we make an attempt towards breaking this undesirable cycle by (1) proposing a systematical evaluation framework for schemes to be assessed objectively; (2) revisiting two foremost schemes proposed by Wu et al. (2017) and Srinivas et al. (2017) to reveal the challenges and difficulties in designing a sound scheme; and (3) conducting a large-scale evaluation of 44 representative schemes under our evaluation framework, thereby providing the missing measurements for two-factor schemes in industrial WSNs.
Adaptive Protection Coordination Scheme using Numerical Directional Overcurrent Relays IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-08 Mahamad Nabab Alam
Dynamic behavior of distribution system operating conditions compromise coordination settings of the existing protection schemes. This paper proposes an on-line adaptive protection coordination scheme utilizing numerical directional overcurrent relays (DOCRs) which is solved using commercial AMPL (A Mathematical Programming Language) based IPOPT (Interior Point OPTimization) solver. It also utilizes the available Intelligent Electronic Devices (IEDs) and communication channel to obtain real-time system information and to update relays settings. The proposed approach is able to handle different operating conditions of the system, including loss of loads, generations and lines. Further, the miscoordination arising due to injections of distributed generations (DGs) have also been considered in the proposed scheme of protection coordination. The proposed coordination approach has been tested on the IEEE 14-bus system (with and without DG) under various operating conditions of the system. Overall system reliability enhancement using the proposed approach has been analyzed in terms of the amount of Energy Not Supplied (ENS). Further, the optimum settings of DOCRs obtained using AMPL based IPOPT solver has been compared with that obtained by GAMS (General Algebraic Modelling System) based SNOPT (Sparse Nonlinear OPTimizer) solver, OPTI Toolbox based IPOPT solver, and MATLAB based interior point method (IPM) solver and metaheuristic genetic algorithm (GA) and differential evolution (DE) algorithms. It has been found that the proposed coordination approach gives good results in reasonably small time and is thus suitable for on-line operation.
Planning Energy Storage and Photovoltaic Panels for Demand Response with Heating Ventilation and Air Conditioning Systems IEEE Trans. Ind. Inform. (IF 6.764) 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 (BESS) 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 also 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 the on/off status of HVACs over eight hours. 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.
Identification of Black Plastics Based on Fuzzy RBF Neural Networks: Focused on Data Preprocessing Techniques Through Fourier Transform Infrared Radiation IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-11-09 Seok-Beom Roh; Sung-Kwun Oh; Witold Pedrycz
The performance enhancement of system identification of various plastic materials to effectively recycle the waste plastics arises as a key issue studied here. For black plastics, which contain carbon black, one is unable to discriminate it from other materials. To facilitate the identification process, Fourier transform-infrared with attenuated total reflectance is used to carry out qualitative as well as quantitative analysis of black plastics. Since a spectrum obtained in this manner constitutes highly dimensional data, feature reduction becomes necessary to extract sound features and reduce the dimensionality of the original spectrum. In this study, three types of feature extraction techniques are considered: peak detection technique, feature extraction based on the chemical characteristics, and fuzzy transform-based feature extraction to determine sound discriminative features. In order to enhance classification process, fuzzy radial basis function neural networks classifier is constructed; these architectures of the classifiers take advantage of the hybrid technologies. Based upon experimental studies, it is shown that the proposed classification system with the feature extraction techniques exhibits superior performance over the performance reported for the already studied classifiers.
Complex-Coefficient Complex-Variable Filter for Grid Synchronization Based on Linear Quadratic Regulation IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-10 Xiangjun Quan; Xiaobo Dou; Zaijun Wu; Minqiang Hu; Alex Q. Huang
Advanced digital filter is important for grid voltage synchronization, harmonic information extraction, and converter control. The complex variable filter (CVF) such as the reduced order generalized integrator is one of the most prevalent filters. However, it is proven in this paper that the real coefficient CVF achieves performance identical to the real variable filter such as the second-order generalized integrator. It implies that the advantages of CVF are not fully utilized. Therefore, in this paper, the real coefficient is extended to a complex one leading to a complex coefficient CVF (CC-CVF), and the filter performance of CVF for extracting the harmonics is significantly improved. Furthermore, linear quadratic regulation is employed to design the complex coefficients to achieve a better dynamic performance. Finally, the CC-CVF is enhanced with frequency estimation capability. The proposed CC-CVF is verified by extensive simulations and experiments.
Hybrid Control of High-Efficient Resonant Converter for Renewable Energy System IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-26 Hiralal Murlidhar Suryawanshi; Snehal Pachpor; T. Ajmal; Girish Gowd Talapur; Shelas Sathyan; Makarand S. Ballal; Vijay B. Borghate; Manoj R. Ramteke
This paper presents the hybrid control of a dc–dc resonant converter for a dc micro-grid. The hybrid control is the simultaneous variation of the frequency and the duty ratio, which can provide excellent voltage regulation and maintain zero-voltage switching (ZVS) over a wide load range. Hence, excellent conversion efficiency is also maintained over the wide load range using hybrid control. However, the conventional control methods for a dc–dc resonant converter using either variable switching frequency or duty ratio have their own limitations. The frequency control requires wide variation in switching frequency for output voltage regulation, which leads to higher switching losses at turn-off of switches and lower efficiency particularly at light loads. The duty ratio control has a limitation of loosing of ZVS at light loads. The simulation and experimental results of hybrid control of resonant converter operating above 100 kHz with maximum duty ratio of 0.48 for 3 kW are presented from full load to no load. The maximum efficiency of the resonant converter is found to be 98%, which was achieved at 75% of the full load.
Received Signal Strength Based Indoor Positioning Using a Random Vector Functional Link Network IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-09 Wei Cui; Le Zhang; Bing Li; Jing Guo; Wei Meng; Haixia Wang; Lihua Xie
Fingerprinting based indoor positioning system is gaining more research interest under the umbrella of location-based services. However, existing works have certain limitations in addressing issues such as noisy measurements, high computational complexity, and poor generalization ability. In this work, a random vector functional link network based approach is introduced to address these issues. In the proposed system, a subset of informative features from many randomized noisy features is selected to both reduce the computational complexity and boost the generalization ability. Moreover, the feature selector and predictor are jointly learned iteratively in a single framework based on an augmented Lagrangian method. The proposed system is appealing as it can be naturally fit into parallel or distributed computing environment. Extensive real-world indoor localization experiments are conducted on users with smartphone devices and results demonstrate the superiority of the proposed method over the existing approaches.
A Fused Load Curve Clustering Algorithm Based on Wavelet Transform IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-11-02 Zigui Jiang; Rongheng Lin; Fangchun Yang; Budan Wu
The electricity load data recorded by smart meters contain plenty of knowledge that contributes to obtaining load patterns and consumer categories. Generally, the daily load curves are clustered first in order to obtain load patterns of each consumer. However, due to the volume and high dimensions of load curves, existing clustering algorithms are not appropriate in this situation. Thus, a fused load curve clustering algorithm based on wavelet transform (FCCWT) is proposed to solve this problem. The algorithm includes two main phases. First, FCCWT applies multilevel discrete wavelet transform (DWT) to convert the daily load curves for dimensionality reduction. Second, it detects clusters at two outputs of the first phase, and then fuses two groups of clusters with a subalgorithm named cluster fusion to achieve the optimized clusters. FCCWT is implemented on datasets of both China and United States. Their clustering performances are evaluated by diverse validity indices comparing with four typical clustering methods. The experimental results show that FCCWT outperforms other comparison methods. Additionally, case analysis of two datasets are also provided to discuss the significance of load patterns.
A Framework for Robust Hybrid State Estimation With Unknown Measurement Noise Statistics IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-19 Junbo Zhao; Lamine Mili
In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.
Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-28 Peyman Neamatollahi; Saeid Abrishami; Mahmoud Naghibzadeh; Mohammad Hossein Yaghmaee Moghaddam; Ossama Younis
Organizing sensor nodes into a clustered architecture is an effective method for load balancing and prolonging the network lifetime. However, a serious drawback of the clustering approach is the imposed energy overhead caused by the “global” clustering operations in every round of the global round-based policy (GRBP). To mitigate this problem, this paper proposes a hierarchical clustering-task scheduling policy (HCSP), which triggers node-driven clustering as opposed to GRBP's time-driven clustering. Based on HCSP, each cluster is reconfigured only once at each local super round. Therefore, the cluster reconfiguration frequency varies on-demand and may differ from one cluster to another throughout the network lifetime. However, in order to refresh the entire network structure, global clustering is performed at the end of every global hyper round. Accordingly, HCSP aims to achieve a more flexible, energy-efficient, and scalable clustering-task scheduling than that of GRBP. This policy mitigates the clustering overhead, which is the worst disadvantage of clustering approaches. Energy consumption calculations and extensive simulations show the effectiveness of HCSP in saving energy and in prolonging the network lifetime.
Multiparty Energy Management for Grid-Connected Microgrids With Heat- and Electricity-Coupled Demand Response IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-27 Nian Liu; Li He; Xinghuo Yu; Li Ma
Combined heat and power (CHP) is an important distributed generation type for the microgrids (MGs) with both thermal and electricity demand. In this paper, a multiparty energy management framework with electricity and heat demand response is proposed for the CHP-MG. First, in order to decide the electricity and thermal prices, an optimization profit model of a microgrid operator (MGO) is formulated including the cost of gas, the income of energy sold to the consumers, and the income of surplus electricity feed to the utility grid. The CHP system is operated in a hybrid mode by dynamically selecting the following-thermal-load mode and the following-electric-load mode. Moreover, for the building energy consumers, an optimization model is formulated containing the utility of electricity consumption, the expenditure of purchasing electricity/heat, and the comfortable degree of indoor temperature. The trading process between the MGO and consumers is designed as a one-leader $N$ -follower Stackelberg game, and the existence and uniqueness of the Stackelberg equilibrium is proved. Finally, the case study of a CHP-MG system containing six building users is provided to show the effectiveness of the proposed method.
Novel Detection Scheme Design Considering Cyber Attacks on Load Frequency Control IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-23 Chunyu Chen; Kaifeng Zhang; Kun Yuan; Lingzhi Zhu; Minhui Qian
With the increase of cyberhacking activities, cyber attacks become urgent problems to system security. In this paper, cyber attacks on load frequency control (LFC) is studied. By unifying attack and detection, detection scheme considering specific attack strategies is presented. As for attack scheme design, four attack strategies are systematically analyzed with respect to their mechanism and influence on LFC performance, so that the most effective one is selected as the adopted attack scheme from the viewpoint of hackers. As for attack detection, a novel attack detection approach is proposed by analyzing differences between dynamic features of variables. Multilayer percepton classifier-based approach is used to extract the differences of area control error under attack and in normal situation, thus distinguishing compromised signals from normal ones. Simulation results show the effectiveness of the proposed detection approach.
Interests and Limits of Machine Learning-Based Neural Networks for Rotor Position Estimation in EV Traction Drives IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-24 Wided Zine; Zaatar Makni; Eric Monmasson; Lahoucine Idkhajine; Bruno Condamin
In this paper, a novel rotor position estimator for an interior permanent magnet synchronous motor is presented and evaluated. The proposed estimator lies on one of the most popular methods in the field of artificial intelligence: Machine learning-based neural networks algorithm. The main interest is to introduce a cost-efficient position estimator that is comparable to classic methods in terms of functional performances. The estimator model is built by learning from a dataset that associates phase currents and voltages to the rotor position. Learning signals are generated using a simulation model. This is primarily intended to save the resources invested in testbench trials. In this work, offline training steps and results are described and commented. The efficiency of the proposed position estimator is first verified by functional simulations. Second, real-time experiments are conducted on an actual scale testbench. The NN-based estimator covers a wide speed range and is implemented in the context of IPMSM-based EV traction drives. More broadly, these findings can also be applicable to the ac-based electric drives for the position estimation purpose.
Mixed-Integer Nonlinear Programming Formulation for Distribution Networks Reliability Optimization IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-11-14 Alireza Heidari; Zhao Yang Dong; Daming Zhang; Pierluigi Siano; Jamshid Aghaei
An optimal placement of protective devices could increase the reliability and quality level of a distribution network. An innovative mixed-integer nonlinear programming model is proposed in this paper to find the type, optimal siting, and number of protective devices to be accurately installed in distribution networks. The customer outage and protective devices costs are considered to derive a value-based reliability equation. To ensure the effectiveness of the proposed formulation economic and technical constraints is considered. Further, this paper aims at aiding decision-makers in providing appropriate protective device allocation by minimizing the expected interruption cost index. Case studies are employed to demonstrate the reliability optimization of a test network and a typical real-size network in which the several cost constraints and protection schemes are assumed to extract the results. Accuracy and effectiveness of the proposed method are assessed and sensitivities analysis is carried out.
A Novel Decentralized Coordinated Voltage Control Scheme for Distribution System With DC Microgrid IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-23 M. V. Gururaj; Narayana Prasad Padhy
This paper proposes a novel decentralized coordinated voltage control (CVC) scheme for a distribution system consisting of dc microgrid (DCMG), doubly fed induction generator-based wind system, on-load tap changer (OLTC), and DSTATCOM. The proposed scheme considers the response time of various voltage regulating devices and assigns a master/slave role based on the operating conditions of the grid and availability of the device. This paper offers a distinctive solution to optimally utilize the voltage regulating devices which do not take part in the contingency situation such as OLTC and DCMG converter in order to achieve the following objectives: 1) A better voltage regulation and increased reactive power reserve during normal operating conditions of the grid. 2) To improve the transient performance of the system in terms of reduction in postfault voltage recovery time. Two modified IEEE 33 bus systems are implemented in a real-time digital simulator platform to test the effectiveness of the proposed CVC scheme. Furthermore, power hardware in loop (PHIL) experimentation is conducted with a reduced scale DCMG hardware setup to test the stability, feasibility, and practicability of the proposed scheme. The simulation and PHIL results demonstrate that the proposed CVC scheme provides a better solution compared to existing work by fulfilling the set objectives.
An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-27 Xin Luo; MengChu Zhou; Shuai Li; MingSheng Shang
High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related and industrial applications like recommender systems. When acquiring useful patterns from them, nonnegative matrix factorization (NMF) models have proven to be highly effective owing to their fine representativeness of the nonnegative data. However, current NMF techniques suffer from: 1) inefficiency in addressing HiDS matrices; and 2) constraints in their training schemes. To address these issues, this paper proposes to extract nonnegative latent factors (NLFs) from HiDS matrices via a novel inherently NLF (INLF) model. It bridges the output factors and decision variables via a single-element-dependent mapping function, thereby making the parameter training unconstrained and compatible with general training schemes on the premise of maintaining the nonnegativity constraints. Experimental results on six HiDS matrices arising from industrial applications indicate that INLF is able to acquire NLFs from them more efficiently than any existing method does.
Computation of Energy Loss in an Induction Motor During Unsymmetrical Voltage Sags—A Graphical Method IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-16 Vijaya Huchche; Nita Patne; Anjali Junghare
Symmetrical and unsymmetrical voltage sags are a major cause of energy loss in induction motors affecting the efficiency and longevity of the motor. A comprehensive and novel graphical method to determine energy loss during unsymmetrical sags is proposed. The energy is computed using torque–speed characteristics of the induction motor. Due to unbalance in the voltages during unsymmetrical sags, oscillations are observed in torque–speed characteristics. Throughout these oscillations, motor torque and speed attain extreme values. Area under the curve of torque and speed during oscillation is computed using MATLAB/Simulink simulation, which is a measure of the output power during unsymmetrical sags. The sag duration is measured to compute the energy loss. The analytical expression to assess output power of an induction motor during unsymmetrical sag is derived. The graphical results are validated using analytical calculations. Graphical representation is easy and faster to construct than analytical methods; in addition, the graphical method proposed makes it easier to assess the comparative magnitudes. Assessment of energy loss provides a useful tool for optimal injection of energy for sag mitigation techniques.
A Data-Emergency-Aware Scheduling Scheme for Internet of Things in Smart Cities IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-16 Tie Qiu; Kaiyu Zheng; Min Han; C. L. Philip Chen; Meiling Xu
With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.
Distributed Control of Inverter-Interfaced Microgrids With Bounded Transient Line Currents IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-01-18 Jiajun Duan; Cheng Wang; Hao Xu; Wenxin Liu; Jian-Chun Peng; Hui Jiang
Distributed generators (DGs) in a microgrid are tightly coupled through power lines, whose dynamics should not be ignored. If not properly handled, large transient line currents may trigger false protection even under normal operating conditions. Droop-based control adjustments also unnecessarily increase frequency and voltage oscillations. Targeting at these problems, this paper presents a distributed control solution for inverter-interfaced microgrids. The objective of primary control is to realize the desired regulations of bus voltages and frequency as well as suppression of transient line currents. The objective of secondary control is to maintain fair load sharing. At secondary control level, a consensus algorithm is introduced to calculate the references for phase angles of bus voltages based on fair load sharing and dc power flow. At primary control level, a feedback linearization based control algorithm with dynamic control bounds is designed for voltage regulation and transient line current suppression. In addition to a common reference frame, the subsystem controllers only require measurements of local and neighboring subsystems. The effectiveness of the proposed control solution is demonstrated through simulations based on both simplified and detailed models.
Incremental Flow Scheduling and Routing in Time-Sensitive Software-Defined Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-12-11 Naresh Ganesh Nayak; Frank Dürr; Kurt Rothermel
Several networking architectures have been developed atop IEEE 802.3 networks to provide real-time communication guarantees for time-sensitive applications in industrial automation systems. The basic principle underlying these technologies is the precise transmission scheduling of time-triggered traffic through the network for providing deterministic and bounded latency and jitter. These transmission schedules are typically synthesized offline (computational time in the order of hours) and remain fixed thereafter, making it difficult to dynamically add or remove network applications. This paper presents algorithms for incrementally adding time-triggered flows in a time-sensitive software-defined network (TSSDN). The TSSDN is a network architecture based on software-defined networking, which provides real-time guarantees for time-triggered flows by scheduling their transmissions on the hosts (network edge) only. These algorithms exploit the global view of the control plane on the data plane to schedule and route time-triggered flows needed for the dynamic applications in the Industrial Internet of Things (Industry 4.0). The evaluations show that these algorithms can compute incremental schedules for time-triggered flows in subseconds with an average relative optimality of 68%.
Co-Optimal Placement of PMUs and Their Communication Infrastructure for Minimization of Propagation Delay in the WAMS IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-01-30 Bhargav Appasani; Dusmanta Kumar Mohanta
The phasor measurement unit (PMU) has evolved as an indispensable tool for real-time monitoring and control of the power system, thereby leading to its large-scale deployment in a wide-area measurement system (WAMS). Communication system plays a significant role for transferring measured real-time PMU data to the phasor data concentrator (PDC) for monitoring and control purposes. The propagation delay depends on the spatial position of the PMUs with respect to the PDC and the communication network. This paper deals with the problem of co-optimal placement of PMUs and their communication infrastructure for minimization of propagation delay in a WAMS. The novelty of the work is to explore the feasibility of microwave communication technology as an alternative to the existing fiber-based communication infrastructure in the WAMS. The proposed approach takes the link reliability and the geographical topological variations into consideration for the placement of the microwave links so as to minimize the propagation delay. The merit of the microwave communication technology in the framework of the WAMS is evaluated in terms of its cost and reliability. Simulation results and the propagation delay evaluations for the Eastern power grid of India validate the efficacy of the proposed approach in practical field applications.
Probabilistic Per-Packet Real-Time Guarantees for Wireless Networked Sensing and Control IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-01-23 Yu Chen; Hongwei Zhang; Nathan Fisher; Le Yi Wang; George Yin
The mission-critical nature of wireless networked sensing and control (WSC) systems, such as the control of industrial plants, requires stringent real-time delivery of packets. Due to inherent dynamics and uncertainties in wireless communication, real-time communication guarantees are probabilistic in nature. In this paper, a probabilistic framework is therefore proposed for per-packet real-time delivery guarantee. The notion of real-time in this paper differs from the existing work in the sense that it ensures, in an execution history of arbitrary length, every packet is successfully delivered before its deadline with a probability no less than a user-specified threshold (e.g., 99%). The framework has several novel building blocks: First, “R3 (requirement-reliability-resource) mapping” translates the upper layer probabilistic real-time communication requirement , and the lower layer links reliability into the resource (i.e., optimal number of transmission opportunities) reserved for each packet. Second, “EDF (earliest deadline first) based real-time scheduling” as well as the “admission test” and “traffic load optimization” maximize system utility while satisfying per-packet real-time communication requirements. The proposed admission test is proved to be both sufficient and necessary, and the simulation results show that the proposed framework ensures probabilistic per-packet real-time communication.
Novel Power Management Scheme and Effects of Constrained On-Node Storage on Performance of MAC Layer for Industrial IoT Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-26 M. P. R. S. Kiran; V. Subrahmanyam; P. Rajalakshmi
In this paper, we propose a novel IEEE 802.15.4 media access control (MAC) power management scheme that achieves the user specified reliability with minimal power consumption at the node. Also, we develop an accurate mathematical model to analyze the effects of constrained on-node memory for sensed data storage on the MAC layer performance. We use three-dimensional Markov chain and M/G/1/K queue to model the IEEE 802.15.4 MAC and on-node packet queue, respectively. By formulating the precise packet service time, the reliability, packet queue overflow losses, delay, and power consumption of the node are analyzed. When compared with simulations and the real-time test bed, the proposed model achieves an accuracy of 97% and 94%, respectively. Also, the performance analysis shows that the proposed power management scheme provides energy savings of up to 74.82%.
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, such as 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 commercial off-the-shelf 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.
An Efficient and Secure Automotive Wireless Software Update Framework IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-12-04 Marco Steger; Carlo Alberto Boano; Thomas Niedermayr; Michael Karner; Joachim Hillebrand; Kay Roemer; Werner Rom
Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.
Compressed Acquisition and Denoising Recovery of EMGdi Signal in WSNs and IoT IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-02 Fei-Yun Wu; Kunde Yang; Zhi Yang
Telemonitoring of diaphragmatic electromyogram (EMGdi) signal in wireless sensor networks (WSNs) and Internet of Things (IoT) holds the promise to be an evolving direction in personalized medicine. The WSNs and IoT enable EMGdi information telemonitoring and communications technologies play important roles in the process of personal medical care, especially for the respiratory diseases. However, while designing such a system, one should consider the required functionality, miniaturization, energy efficiency, etc., to make fewer resources required in WSNs and IoT. Conventional methods of data acquisition cannot energy-effectively compress data with reduced device costs. Different from the traditional compression methods, compressed sensing (CS) takes promising steps toward these challenges. Unfortunately, EMGdi is not sparse in time domain. Hence, current CS algorithms are extremely difficult to use directly for recovering EMGdi. In order to satisfy the requirements of applications of personal medical care in WSNs and IoT, this study proposes an approximated $l_0$ norm based method to search the solution via the gradient descent method, then projects the searched solution to the reconstruction feasible set. Meanwhile, this study adopts a new wavelet threshold based method to denoise the electrocardiographic interference. Experimental results are provided to testify the performance of the proposed methods.
Confident Information Coverage Hole Healing in Hybrid Industrial Wireless Sensor Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-17 Xianjun Deng; Zujun Tang; Laurence Tianruo Yang; Man Lin; Bang Wang
The emergence of coverage holes will dramatically degrade the quality of service of the industrial wireless sensor networks (IWSNs). Based on the novel confident information coverage (CIC) model, this work focuses on how to heal the CIC holes in hybrid IWSNs containing both static nodes and mobile nodes. We pinpoint the CIC hole healing (CICHH) problem with the goal of selecting and dispatching some randomly scattered mobile nodes to the CIC holes detected by the stationary nodes such that the CIC holes can be repaired and the CIC performance can be satisfied, and prove its NP-completeness. For handling the CICHH problem, we devise two energy-efficient heuristic solutions including a centralized CICHH algorithm and a distributed one. Both the proposed schemes aim at efficiently healing the CIC holes while minimizing the total moving energy consumption of the dispatched mobile nodes, or maximizing the mobile nodes’ average remaining energy after movement, or minimizing the maximum mobile energy consumption of each dispatched mobile node. Experimental simulation results show the proposed schemes can energy-efficiently heal the CIC holes and outperform three peer algorithms in terms of energy efficiency and coverage ratio.
Topology Control Strategy for Movable Sensor Networks in Ultradeep Shafts IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-12-06 Gongbo Zhou; Penghui Wang; Zhencai Zhu; Houlian Wang; Wei Li
Ultradeep shafts are the only means available to extract solid mineral resources from deep within the earth, and the conditions of these shafts directly affect the working safety of the lifting system. Chain-type wireless sensor networks (CWSNs), which are mobile and self-supporting, provide a new option for mobile collaborative monitoring of ultradeep shafts by sampling panoramic images. In this paper, a topological control method for CWSNs in an ultradeep shaft based on mobile sensor nodes is proposed. First, a video sensor node that can vertically climb a rope and automatically generate power is designed. Then, a uniform chain-type deployment strategy is proposed to accommodate the actual conditions of an ultradeep shaft. Ultimately, the network repair strategies based on mobile nodes are designed to address node movement and communication failures. The research results indicate that a uniform hierarchical deployment algorithm with a scheduling strategy can achieve overall monitoring of the ultradeep shaft in a monitoring cycle and that the collaborative repair strategy can effectively maintain the linear-domain coverage of the network.
Cloud-Orchestrated Physical Topology Discovery of Large-Scale IoT Systems Using UAVs IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-01-23 Tianqi Yu; Xianbin Wang; Jiong Jin; Kenneth McIsaac
Wireless sensor networks (WSNs) have been rapidly integrated into Internet of Things (IoT) systems, empowering rich and diverse applications such as large-scale environment monitoring. However, due to the random deployment of sensor nodes (SNs), physical topology of the WSNs cannot be controlled and typically remains unknown to the IoT cloud server. Therefore, in order to derive the physical topology at the cloud for effective real-time event detection, a cloud-orchestrated physical topology discovery scheme for large-scale IoT systems using unmanned aerial vehicles (UAVs) is proposed in this paper. More specifically, the large-scale monitoring area is first split into a number of subregions for UAV-enabled data collection. Within the subregions, parallel Metropolis–Hastings random walk (MHRW) is developed to gather the information of WSN nodes, including their IDs and neighbor tables. The collected information is then forwarded to the cloud through UAVs for the initial generation of logical topology. Thereafter, a network-wide 3-D localization algorithm is further developed based on the discovered logical topology and multidimensional scaling method (Topo-MDS), where the UAVs equipped with global positioning system are served as mobile anchors to locate the SNs. Simulation results indicate that the parallel MHRW improves both the efficiency and accuracy of logical topology discovery. In addition, the Topo-MDS algorithm dramatically improves the 3-D location accuracy, as compared to the existing algorithms in the literature.
IIHub: An Industrial Internet-of-Things Hub Toward Smart Manufacturing Based on Cyber-Physical System IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-02 Fei Tao; Jiangfeng Cheng; Qinglin Qi
Smart manufacturing is increasingly becoming the common goal of various national strategies. Smart interconnection is one of the most important issues for implementing smart manufacturing. However, current solutions are not tended to realize smart interconnection in dealing with heterogeneous equipment, quick configuration and implementation, and online service generation. To solve the issues, industrial Internet-of-Things hub (IIHub) is proposed, which consists of customized access module (CA-Module), access hub (A-Hub), and local service pool (LSP). A set of flexible CA-Modules can be configured or programed to connect heterogeneous physical manufacturing resources. Besides, the IIHub supports manufacturing services online generation based on the service encapsulation templates and also supports quick configuration and implementation for smart interconnection. Furthermore, related smart analysis and precise management have the potential to be achieved. Finally, a prototype is given to illustrate the functions of the proposed IIHub, and to show how IIHub realizes smart interconnection.
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 M. P. R. S. Kiran; P. Rajalakshmi
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 carrier sense multiple access—collision avoidance (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 that 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.
Achieving Hybrid Wired/Wireless Industrial Networks With WDetServ: Reliability-Based Scheduling for Delay Guarantees IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-02-07 Samuele Zoppi; Amaury Van Bemten; H. Murat Gürsu; Mikhail Vilgelm; Jochen 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.
Analysis and Optimized Design of Compensation Capacitors for A Megahertz WPT System Using Full-Bridge Rectifier IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2018-05-04 Minfan Fu; Zefan Tang; Chengbin Ma
The spatial freedom of wireless power transfer (WPT) systems can be improved using a high operating frequency such as several megahertz (MHz). In the conventional compensations the load of the coupling coils is usually assumed to be pure resistive. However, in MHz WPT systems this assumption is not accurate anymore due to the non-neglectable rectifier input reactance. This paper discusses the impedance characteristics of the full-bridge rectifier at MHz and their influence under the series-series, parallel-series, series-parallel, and parallel-parallel compensation topologies. An undesirable non-zero phase (i.e., none unity power factor) is shown to exist at the primary input port, which leads to decreased power transfer capability. In order to minimize this negative effect, the compensation capacitors are optimally designed, and the series-series topology is found to have the smallest phase under load and coupling variations. Finally, an experimental 6.78 MHz system is built up to verify the optimized design of the compensation capacitors. The results show that the average non-zero phase is effectively reduced together with the improved power factor from 0.916 to 0.982.
Distributed Model-Based Control and Scheduling for Load Frequency Regulation of Smart Grids over Limited Bandwidth Networks IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-26 Shichao Liu; Peter Xiaoping Liu
An integrated model-based control and scheduling scheme is proposed for the load frequency control (LFC) of large-scale power systems under the distributed structure and uncertainties. Specifically, the limited bandwidth constraint is considered when state observation is exchanged over shared communication networks. Each area controller uses the explicit models of its own and neighboring areas to predict state observations when the actual one is not available. At each transmission instant, the state observation of the scheduled area is broadcasted to the relevant areas and the model-based controllers are partially updated. By properly scheduling the transmission sequence and intervals, the stability of the power system can be guaranteed with a substantial reduction of the bandwidth usage and this is proved by performing a thorough theoretical analysis. Simulation results of a four-area power system verify that the proposed distributed model-based control scheme integrated with a proper scheduling strategy can greatly enhance the performance and the resiliency to parameter uncertainty in large-scale power systems.
Improving Synchronous Generator Parameters Estimation Using d-q Axes Tests and Considering Saturation Effect IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-05 Behrooz Zaker; Gevork B. Gharehpetian; Mehdi Karrari
Fundamentals of parameter estimation of SG have already been presented in different standards such as IEEE Std. 115. The first proposed methods require short circuit tests and/or some tests for which the synchronous generator should be out of service. In recent reports, however, to avoid the shortcomings of former methods, partial load rejection tests on $d-q$ axes have been recommended to estimate the electrical parameters of SG such as different reactances and time constants. In this paper, it is first shown that the standard well-known methods are valid when there is no saturation effect. Therefore, a new method is proposed to improve SG parameters estimation taking into account the saturation effect. The proposed method uses saturation curve parameters, rotor angle and analytical equations of the SG alongside the load rejection tests results. To show the accuracy and precision of the proposed method, it is applied to experimental data of an 80 MVA gas turbine unit, and the results are discussed.
Long-Term Maintenance Scheduling and Budgeting in Electricity Distribution Systems Equipped with Automatic Switches IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-11-13 Hamed Mirsaeedi; Alireza Fereidunian; Seyed Mohsen Mohammadi-Hosseininejad; Payman Dehghanian; Hamid Lesani
Maintenance management, as a key part of the asset management practices, plays a vital role in enhancing the reliability of the electricity distribution systems (EDS) where realizing a highly reliable EDS is being attributed higher and higher criticality in modern society. In this paper, a new approach is proposed to improve the reliability of EDS through optimal scheduling of preventive maintenance (PM) tasks and allocation of automatic switches. The suggested objective is to minimize a combined customer-based (SAIDI and SAIFI) and cost-based reliability indices. The total reliability cost (TRC) includes those associated with the corrective maintenance (CM) actions, PM tasks, and automatic switch investments. The proposed approach is implemented in three different scenarios: (1) switch placement, (2) PM tasks scheduling and budget management, as well as (3) a joint switch and PM tasks decision-making. The aforementioned scenarios are applied on a standard reliability test system (RBTS4) followed by multiple sensitivity analysis to further demonstrate the efficacy and performance of the proposed framework.
Optimal Design of Community Battery Energy Storage Systems with Prosumers Owning Electric Vehicles IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-14 Shady El-Batawy; Walid G. Morsi
This paper presents a novel approach that aims to assist the distribution system operator to intelligently design the community battery energy storage systems considering high penetration of prosumers equipped with rooftop solar photovoltaics and electric vehicles. The design problem is mathematically formulated after incorporating the battery storage system cost model. The results have shown the capability of the proposed approach to optimally design the community battery energy storage systems. This resulted in improving the voltage profile, reducing the power loss, and mitigating the distribution transformer aging while attaining a profit from the energy arbitrage. Also, the obtained results have shown a positive net present value of using the community battery energy storage systems, which demonstrates the cost-effectiveness of the proposed optimal design approach. The proposed approach may represent an effective tool into future disturbed energy resources management systems.
Data-Driven Flotation Industrial Process Operational Optimal Control Based on Reinforcement Learning IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-10 Yi Jiang; Jialu Fan; Tianyou Chai; Jinna Li; Frank L. Lewis
This paper studies the operational optimal control problem for the industrial flotation process, a key component in the mineral processing concentrator line. A new model-free data-driven method is developed here for real-time solution of this problem. A novel formulation is given for optimal selection of the process control inputs that guarantees optimal tracking of the operational indices while maintaining the inputs within specified bounds. Proper tracking of prescribed operational indices, namely concentrate grade and tail grade, is essential in the proper economic operation of the flotation process. The difficulty in establishing an accurate mathematic model is overcome, and optimal controls are learned online in real time, using a novel form of reinforcement learning we call Interleaved Learning for online computation of the operational optimal control solution. Simulation experiments are provided to verify the effectiveness of the proposed Interleaved Learning method and to show that it performs significantly better than standard Policy Iteration.
Second-Order Sliding Mode Controller Design and Its Implementation for Buck Converters IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-09-29 Shihong Ding; Wei Xing Zheng; Jinlin Sun; Jiadian Wang
A second-order sliding mode (SOSM) control method is developed for the regulation problem of a DC-DC buck converter. By taking into account the model uncertainties and external disturbances in the mathematical model, a sliding variable with relative of degree two is first constructed. Then, a new SOSM controller is developed such that the output voltage will well track the desired reference voltage. Theoretical analysis shows that the resulting closed-loop system is globally finite-time stable, while similar SOSM control results only give the proof for finite-time convergence. The way on how to implement the proposed SOSM algorithm is also presented. The theoretical findings are verified by extensive simulations and experiments.
An Adaptive Approach for PEVs Charging Management and Reconfiguration of Electrical Distribution System Penetrated by Renewables IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-11 Mehdi Rahmani-Andebili; Mahmud Fotuhi-Firuzabad
A robust approach for distribution system reconfiguration (DSR) and charging management (CHM) of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control (MPC) is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources (RESs). The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs drivers (based on their income level) are modeled with respect to the value of incentive and their hourly distance from each charging station (CHS). It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.
Self-Adaptive Incremental Conductance Algorithm for Swift and Ripple Free Maximum Power Harvesting from PV Array IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-10-23 Nishant Kumar; Ikhlaq Hussain; Bhim Singh; Bijaya Ketan Panigrahi
This paper deals with a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the solar photovoltaic array, which has inherent decision taking and self-adaptive ability. The working principle of self-adaptive incremental conductance (SAInC) algorithm is based on three consecutive operating points on the power-voltage characteristic. Out of three points, the first point is the driving point, which smartly detects the dynamic condition, as well as in normal condition, searches the maximum power peak (MPP) zone. Moreover, by using a triangular analogy, the rests two points decide the optimum operating position for next iteration, which is responsible for quick MPP tracking as well as good dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on a experimental system. Moreover, performance of SAInC algorithm is compared with the popular and recent state of art methods. The satisfactory dynamic and steady-state performance with low complexity as well as low computational burden of SAInC algorithm shows the superiority over state of art methods.
Accurate Timing Networks for Dependable Smart Grid Applications IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-12-25 Francisco Ramos; José Luis Gutiérrez-Rivas; José López-Jiménez; Benito Caracuel; Javier Diaz
The Smart Grid is a complex and interconnected system where actions and events in one part of the system affect operations elsewhere. In order to perform a rigorous analysis a common and deterministic timing reference is required. This paper addresses this need, using as use case the Substation Automation System (SAS), a key element of the Smart Grid, where utilities, classically, have opted for a satellite clock providing a timing signal and different ways to distribute it through the Substation. This paper proposes an appropriate solution for the Substation needs presenting the implementation details, the analysis of the system reliability, safety and security together with promising results and novel mechanisms for providing timing on the grid.
Characterization of Substation Process Bus Network Delays IEEE Trans. Ind. Inform. (IF 6.764) Pub Date : 2017-07-24 André dos Santos; Bruno Soares; Chen Fan; Martijn Kuipers; Sérgio Sabino; António Grilo; Paulo Pereira; Mário Nunes; Augusto Casaca
The paper presents the characterization of network delays in an IEC61850 process bus substation area network, both through theoretical analysis and simulations. Several design targets were defined considering the recommendations of standards and good design practices: number of network hops; total network delay; probability of the delay being exceeded; link load; network topology and availability. An analytical delay estimation methodology is proposed, considering both the steady-state traffic and traffic resulting from a breaker failure event. A complete substation is taken as example for characterizing the network delays, considering a star network topology. Simulations allow obtaining the cumulative distribution functions and percentile values of network delays. Results show a good agreement between the simulation and the analytical analysis. While the delay is best characterized statistically through simulation, finding the maximum network delay through simulations can be very time consuming, making the analytical analysis more suitable.
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