Showing papers in "IEEE Transactions on Industrial Informatics in 2016"

Survey on Recent Advances in Networked Control Systems

Abstract: Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical development of NCSs. In-depth analysis and discussion is made on sampled-data control, networked control, and event-triggered control. More specifically, existing research methods on NCSs are summarized. Furthermore, as an active research topic, network-based filtering is reviewed briefly. Finally, some challenging problems are presented to direct the future research.

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Abstract: Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Abstract: Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand–supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

Distributed Event-Triggered Scheme for Economic Dispatch in Smart Grids

Abstract: To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the $\theta$ -logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.

Resilient Control of Networked Control System Under DoS Attacks: A Unified Game Approach

Abstract: We consider the problem of resilient control of networked control system (NCS) under denial-of-service (DoS) attack via a unified game approach. The DoS attacks lead to extra constraints in the NCS, where the packets may be jammed by a malicious adversary. Considering the attack-induced packet dropout, optimal control strategies with multitasking and central-tasking structures are developed using game theory in the delta domain, respectively. Based on the optimal control structures, we propose optimality criteria and algorithms for both cyber defenders and DoS attackers. Both simulation and experimental results are provided to illustrate the effectiveness of the proposed design procedure.

A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem

Abstract: Prognostics of the remaining useful life (RUL) has emerged as a critical technique for ensuring the safety, availability, and efficiency of a complex system. To gain a better prognostic result, degradation information is quite useful because it can reflect the health status of a system. However, due to the lack of accurate information about the plants’ degradation, the prognostic model is usually not well established. To solve this problem, this paper proposes a two-stage strategy that is in the context of data-driven modeling to predict the future health status of a bearing, where the degradation information was estimated by calculating the deviation of multiple statistics of vibration signals of a bearing from a known healthy state. Then, a prediction stage based on an enhanced Kalman filter and an expectation–maximization algorithm were used to estimate the RUL of the bearing adaptively. To verify the effectiveness of the proposed approach, a real-bearing degradation problem was implemented.

Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System

Abstract: The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed maximum power point (MPP) tracking (MPPT) schemes for photovoltaic (PV) power-generation systems. However, under partially shaded conditions (PSCs), P&O method miserably fails to recognize global MPP (GMPP) and gets trapped in one of the local MPPs (LMPPs). This paper proposes ant-colony-based search in the initial stages of tracking followed by P&O method. In such a hybrid approach, the global search ability of ant-colony optimization (ACO) and local search capability of P&O method are integrated to yield faster and efficient convergence. A theoretical analysis of the static and dynamic convergence behavior of the proposed algorithm is presented together with computed and measured results.

Sensor-Less Five-Level Packed U-Cell (PUC5) Inverter Operating in Stand-Alone and Grid-Connected Modes

Abstract: In this paper, a new mode of operation has been introduced for packed U-cell (PUC) inverter. A sensor-less voltage control based on redundant switching states is designed for the five-level packed U-cell (PUC5) inverter, which is integrated into switching process. The sensor-less voltage control is in charge of fixing the dc capacitor voltage at half of the dc source value results in generating symmetric five-level voltage waveform at the output with low harmonic distortion. The sensor-less voltage regulator reduces the complexity of the control system, which makes the proposed converter appealing for industrial applications. An external current controller has been applied for grid-connected application of the introduced sensor-less PUC5 to inject active and reactive power from inverter to the grid with arbitrary power factor, while the PUC auxiliary dc bus is regulated only by sensor-less controller combined with new switching pattern. Experimental results obtained in stand-alone and grid-connected operating modes of proposed PUC5 inverter prove the fast response and good dynamic performance of the designed sensor-less voltage control in balancing the dc capacitor voltage at desired level.

Energy Management for Joint Operation of CHP and PV Prosumers Inside a Grid-Connected Microgrid: A Game Theoretic Approach

Abstract: This paper mainly focuses on the energy management of microgrids (MGs) consisting of combined heat and power (CHP) and photovoltaic (PV) prosumers. A multiparty energy management framework is proposed for joint operation of CHP and PV prosumers with the internal price-based demand response. In particular, an optimization model based on Stackelberg game is designed, where the microgrid operator (MGO) acts as the leader and PV prosumers are the followers. The properties of the game are studied and it is proved that the game possesses a unique Stackelberg equilibrium. The heuristic algorithm based on differential evolution is proposed that can be adopted by the MGO, and nonlinear constrained programing can be adopted by each prosumer to reach the Stackelberg equilibrium. Finally, via a practical example, the effectiveness of the model is verified in terms of determining MGO's prices and optimizing net load characteristic, etc.

Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks

Abstract: Network lifetime is a crucial performance metric to evaluate data-gathering wireless sensor networks (WSNs) where battery-powered sensor nodes periodically sense the environment and forward collected samples to a sink node. In this paper, we propose an analytic model to estimate the entire network lifetime from network initialization until it is completely disabled, and determine the boundary of energy hole in a data-gathering WSN. Specifically, we theoretically estimate the traffic load, energy consumption, and lifetime of sensor nodes during the entire network lifetime. Furthermore, we investigate the temporal and spatial evolution of energy hole and apply our analytical results to WSN routing in order to balance the energy consumption and improve the network lifetime. Extensive simulation results are provided to demonstrate the validity of the proposed analytic model in estimating the network lifetime and energy hole evolution process.

Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections

Abstract: The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.

Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

Abstract: This paper presents a methodology aimed at extracting features to obtain information that will highlight disturbances related to the field of power quality. Due to the concept of smart grids, it is clear that the classification of the disturbances should be undertaken using smart meters, so that a large amount of data corresponding to the voltage and current waveforms are not exchanged using the communication channels, i.e., between smart meter and Utility’s database server. Thus, it is necessary to ensure a balance between computational effort (arising from the implementation of algorithms on hardware) and the satisfactory performance of the algorithm for the classification of disturbances. Based on the assumption that the classification task is onerous, this paper proposes a step of feature extraction that may be calculated and analyzed offline using synthetic waveforms/signals, which are subsequently validated using field measurements. It should be noted that this offline analysis is required to determine the most relevant features for the process of classifying each disturbance. However, in order to establish the effectiveness of the feature extraction step, the response of decision trees of the C4.5 type and of artificial neural networks of the multilayer perceptron type were verified during the phase of disturbance classification. In short, good results were obtained that corroborate the hypothesis that the feature extraction step is necessary to classify disturbances effectively and with low computational effort.

Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life

Abstract: Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor device, namely, Smart Insole, to tackle the challenge of efficient gait monitoring in real life. An array of electronic textile (eTextile)-based pressure sensors are integrated in the insole to fully measure the plantar pressure. Smart Insole is also equipped with a low-cost inertial measurement unit including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer to capture the gait characteristics in motion. Smart Insole can offer precise acquisition of gait information. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform the gait monitoring. Furthermore, a smartphone graphic user interface is developed to display the sensor data in real-time via Bluetooth low energy. We perform a set of experiments in four real-life scenes including hallway walking, ascending/descending stairs, and slope walking, where gait parameters and features are extracted. Finally, the limitation and improvement, wearability and usability, further work, and healthcare-related potential applications are discussed.

Balancing Power Demand Through EV Mobility in Vehicle-to-Grid Mobile Energy Networks

Abstract: Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and power grid, which provides flexible demand response management (DRM) for the reliability of smart grid. EV mobility is a unique and inherent feature of the V2G system. However, the inter-relationship between EV mobility and DRM is not obvious. In this paper, we focus on the exploration of EV mobility to impact DRM in V2G systems in smart grid. We first present a dynamic complex network model of V2G mobile energy networks, considering the fact that EVs travel across multiple districts, and hence EVs can be acting as energy transporters among different districts. We formulate the districts’ DRM dynamics, which is coupled with each other through EV fleets. In addition, a complex network synchronization method is proposed to analyze the dynamic behavior in V2G mobile energy networks. Numerical results show that EVs mobility of symmetrical EV fleet is able to achieve synchronous stability of network and balance the power demand among different districts. This observation is also validated by simulation with real world data.

Compensation of Cross Coupling in Multiple-Receiver Wireless Power Transfer Systems

Abstract: Simultaneous wireless charging of multiple devices is a unique advantage of wireless power transfer (WPT). Meanwhile, the multiple-receiver configuration makes it more challenging to analyze and optimize the operation of the system. This paper aims at providing a general analysis on the multiple-receiver WPT systems and compensation for the influence of the cross coupling. A two-receiver WPT system is first investigated as an example. It shows that theoretically by having derived optimal load reactances, the important system characteristics can be preserved, such as the original system efficiency, input impedance, and power distribution when there is no cross coupling between receivers. The discussion is then extended to general multiple-receiver WPT systems with more than two receivers. Similar results are obtained that show the possibility of compensating the cross coupling by having the derived optimal load reactances. Finally, the theoretical analysis is validated by model-based calculation and final experiments using real two- and three-receiver systems.

Secure Resource Allocation for OFDMA Two-Way Relay Wireless Sensor Networks Without and With Cooperative Jamming

Abstract: We consider secure resource allocations for orthogonal frequency division multiple access (OFDMA) two-way relay wireless sensor networks (WSNs). The joint problem of subcarrier (SC) assignment, SC pairing and power allocations, is formulated under scenarios of using and not using cooperative jamming (CJ) to maximize the secrecy sum rate subject to limited power budget at the relay station (RS) and orthogonal SC allocation policies. The optimization problems are shown to be mixed integer programming and nonconvex. For the scenario without CJ, we propose an asymptotically optimal algorithm based on the dual decomposition method and a suboptimal algorithm with lower complexity. For the scenario with CJ, the resulting optimization problem is nonconvex, and we propose a heuristic algorithm based on alternating optimization. Finally, the proposed schemes are evaluated by simulations and compared with the existing schemes.

Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver

Abstract: Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with $-32\,{\text {dBm}}$ sensitivity), sensitivity up to $-55\,{\text {dBm}}$ (with maximum power of $1,2 \upmu{\text {W}}$ ), latency from $8\;\upmu\text{s}$ , tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.

Network-Based Fault Detection Filter and Controller Coordinated Design for Unmanned Surface Vehicles in Network Environments

Abstract: This paper is concerned with the network-based modeling, and observer-based fault detection filter (FDF) and controller coordinated design for an unmanned surface vehicle (USV) in network environments. Network-based models for the USV subject to actuator faults and wave-induced disturbances are established for the first time by introducing an observer-based FDF, and considering network-induced characteristics such as delays and packet dropouts in the sampler-to-control station communication network channel and the control station-to-actuator communication network channel. Based on these models, network-based FDF and controller coordinated design criteria are derived to asymptotically stabilize the residual system. The designed network-based FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the USV to external disturbances. Fault detection performance analysis verifies the effectiveness of the proposed network-based FDF and controller coordinated design scheme for the USV in network environments.

Design and Real-Time Controller Implementation for a Battery-Ultracapacitor Hybrid Energy Storage System

Abstract: In this study, two real-time energy management strategies have been investigated for optimal current split between batteries and ultracapacitors (UCs) in electric vehicle applications. In the first strategy, an optimization problem is formulated and solved using Karush–Kuhn–Tucker conditions to obtain the real-time operation points of current split for the hybrid energy storage system (HESS). In the second strategy, a neural network-based strategy is implemented as an intelligent controller for the proposed system. To evaluate the performance of these two real-time strategies, a performance metric based on the battery state-of-health (SoH) is developed to reveal the relative impact of instantaneous battery currents on the battery degradation. A 38 V–385 Wh battery and a 32 V–4.12 Wh UC HESS hardware prototype has been developed and a real-time experimental platform has been built for energy management controller validation, using xPC Target and National Instrument data acquisition system. Both the simulation and real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the two real-time controller designs. It is shown that under a high speed, high acceleration, aggressive drive cycle US06, the two real-time energy management strategies can greatly reduce the battery peak current and consequently decreases the battery SoH reduction by 31% and 38% in comparison to a battery-only energy storage system.

Optimal Bidding Strategy and Intramarket Mechanism of Microgrid Aggregator in Real-Time Balancing Market

Abstract: Microgrid (MG) system with multienergy resources has a wide and dispatchable generation range and shows instant response, therefore, constituting a potentially suitable real-time power balancing resource. In this paper, we introduce the concept of MG aggregator to involve small-scale MGs in real-time balancing market bidding via a hierarchical market framework. At the upper real-time market level, the bidding strategy of aggregator is optimized by a risk-constrained mean-variance model to depress the effects of renewable energy sources (RES) uncertainty; at the lower intramarket level, an event-driven mechanism is presented to reach the cleared quantity of the upper market while realizing maximum economy. Furthermore, the above-centralized self-scheduling problem of MG aggregator is decomposed into individual MG optimization problems to achieve an optimal solution with fast convergence. Simulation results verify that the proposed market framework and the bidding strategy for MG aggregator are of high applicability in real market environment.

IR-UWB-Based Non-Line-of-Sight Identification in Harsh Environments: Principles and Challenges

Abstract: Impulse radio ultrawideband ranging has recently received significant attention due to the high accuracy it can achieve. Although most research efforts have focused on ranging in indoor and outdoor environments, other environments such as harsh industrial environments introduce unique challenges. This paper discusses the impact of propagation characteristics of harsh industrial environments on ranging accuracy, and also discusses principles and challenges of non-line-of-sight identification in industrial scenarios. To illustrate these challenges, a measurement campaign using 802.15.4a radios was conducted in a Heavy Machines Laboratory. The results show that the non-line-of-sight condition can be accurately identified if adequate models for such an environment are used.

Dynamic Multiple-Period Reconfiguration of Real-Time Scheduling Based on Timed DES Supervisory Control

Abstract: Based on the supervisory control theory (SCT) of timed discrete-event systems (TDES), this study presents a dynamic reconfiguration technique for real-time scheduling of real-time systems running on uni-processors. A new formalism is developed to assign periodic tasks with multiple-periods. By implementing SCT, a real-time system (RTS) is dynamically reconfigured when its initial safe execution sequence set is empty. During the reconfiguration process, based on the multiple-periods, the supervisor proposes different safe execution sequences. Two real-world examples illustrate that the presented approach provides an increased number of safe execution sequences as compared with the earliest-deadline-first (EDF) scheduling algorithm.

Virtual Power Plant for Grid Services Using IEC 61850

Abstract: This paper assesses the communication, information, and functional requirements of virtual power plants (VPPs). A conceptual formulation of the interoperability requirements, as well as a comparative study of their fulfillment by state-of-the-art communication techniques, is presented. VPP requirements are then mapped against services and information models of IEC 61850 and common information model (CIM) power utility automation standards. Proposals are given for extensions of the IEC 61850 standard to enhance the interaction between the VPP controller and the distributed energy resources. Finally, the methodology and concepts are applied to a specific VPP consisting of hydro and wind plants, solar photovoltaic (PV) power, and storage facilities. Several applications to provide grid services from the proposed VPP in an existing 50-kV grid are covered. The implementation of the VPP communication and control architecture in the supervisory control and data acquisition (SCADA) of demonstration plant is also presented.

Optimal Battery Sizing in Microgrids Using Probabilistic Unit Commitment

Abstract: The Stochastic nature of wind power can cause insufficiency of supply in electrical systems. Applying an energy storage system can alleviate the impact of wind power forecast error on power systems performance and increase system tolerance against deficiency of supply. This paper attempts to investigate a new unit commitment (UC) problem based on the cost–benefit analysis and here-and-now (HN) approach for optimal sizing of battery banks (BBs) imicrogrids (MGs) with wind power systems. To solve this problem, particle swarm optimization is used to minimize the total cost and maximize the total benefit. In this paper, 12 scenarios have been considered in the presence of BBs and without them in 2 operating modes: 1) stand-alone mode and 2) grid-connected mode. Using the HN approach, the uncertainty of wind power is applied as a constraint in these operating modes. The mathematical formulations related to the HN approach in MGs and its combination in a UC problem are presented in detail for optimal sizing of BBs. Simulation results show that the best sizes of BBs and the scheduling of distributed generations would be entirely different when the accessibility of wind power is taken into consideration by applying HN approach to the proposed probabilistic UC problem.

A Cybersecurity Detection Framework for Supervisory Control and Data Acquisition Systems

Abstract: This paper presents a distributed intrusion detection system (DIDS) for supervisory control and data acquisition (SCADA) industrial control systems, which was developed for the CockpitCI project. Its architecture was designed to address the specific characteristics and requirements for SCADA cybersecurity that cannot be adequately fulfilled by techniques from the information technology world, thus requiring a domain-specific approach. DIDS components are described in terms of their functionality, operation, integration, and management. Moreover, system evaluation and validation are undertaken within an especially designed hybrid testbed emulating the SCADA system for an electrical distribution grid.

Wireless Gas Leak Detection and Localization

Abstract: Thousands of industrial gas leaks occur every year, with many leading to injuries, deaths, equipment damage, and a disastrous environmental effect. There have been many attempts at solving this problem, but with limited success. This paper proposes a wireless gas leak detection and localization solution. With a monitoring network of 20 wireless devices covering $200\;\text{m}^2$ , 60 propane releases are performed. The detection and localization algorithms proposed here are applied to the collected concentration data, and the methodology is evaluated. A detection rate of 91% is achieved, with seven false alarms recorded over 3 days, and an average detection delay of 108 s. The localization results show an accuracy of 5 m. Recommendations for future explosive gas sensor design are then presented.

Bearing Defect Classification Based on Individual Wavelet Local Fisher Discriminant Analysis with Particle Swarm Optimization

Abstract: In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.

A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn

Abstract: As market competition intensifies, customer churn management is increasingly becoming an important means of competitive advantage for companies. However, when dealing with big data in the industry, existing churn prediction models cannot work very well. In addition, decision makers are always faced with imprecise operations management. In response to these difficulties, a new clustering algorithm called semantic-driven subtractive clustering method (SDSCM) is proposed. Experimental results indicate that SDSCM has stronger clustering semantic strength than subtractive clustering method (SCM) and fuzzy c-means (FCM). Then, a parallel SDSCM algorithm is implemented through a Hadoop MapReduce framework. In the case study, the proposed parallel SDSCM algorithm enjoys a fast running speed when compared with the other methods. Furthermore, we provide some marketing strategies in accordance with the clustering results and a simplified marketing activity is simulated to ensure profit maximization.

A Probabilistic Approach to Determine Optimal Capacity and Location of Electric Vehicles Parking Lots in Distribution Networks

Abstract: In this paper, a probabilistic approach based on the point estimate method is presented to determine the optimal capacity and location of electric vehicles (EVs) parking lots in distribution networks. For this purpose, uncertain parameters in driving patterns of vehicle owners are considered, and the effects of these uncertainties on determination of optimal capacity and profit from the construction of parking lots have been investigated. The proposed approach has considered both technical and economical aspects simultaneously. Additionally, because of the necessity of optimal schedule and the determination of EV charging and discharging time intervals to calculate the benefits of the amount of parking lot power exchange with a distribution network and EVs, a new approach is proposed to charge schedule of EVs. This method runtime, in addition to simple mechanism and global optimal responses, is very short. Finally, the presented probabilistic method is performed on two test distribution networks and obtained results are discussed.

Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework

Abstract: With the growing complexity of the modern industrial process, monitoring large-scale plant-wide processes has become quite popular. Unlike traditional processes, the measured data in the plant-wide process pose great challenges to information capture, data management, and storage. More importantly, it is difficult to efficiently interpret the information hidden within those data. In this paper, the road map of a distributed modeling framework for plant-wide process monitoring is introduced. Based on this framework, the whole plant-wide process is decomposed into different blocks, and statistical data models are constructed in those blocks. For online monitoring, the results obtained from different blocks are integrated through the decision fusion algorithm. A detailed case study is carried out for performance evaluation of the plant-wide monitoring method. Research challenges and perspectives are discussed and highlighted for future work.