Search

The work presented in this paper discusses about the usage of DC electric springs in the microgrid system consisting of more than one renewable energy source and one main grid connection point. The objective of this work is to reduce the dependability of the microgrids on the main grid by coordinating the power supplied by renewable energy sources during generation intermittence of the renewable generators. In order to achieve the said objective, a control algorithm is proposed in this work which makes the DC noncritical loads consume lesser power during generation deficits in the microgrid. The proposed control algorithm is simple to implement and can be used for complex microgrid structures also. A microgrid system consisting of two renewable generators, one main grid connection point and DC loads fed through a rectifier are considered for the study, and the models of the same are developed in MATLAB/Simulink.

Electric vehicles are an integral part of futuristic smart grids. Electric vehicles give rise to a new player in the retail market called as aggregator. This paper proposes an intelligent charging scheduling problem for an Electric Vehicle (EV) aggregator considering vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capabilities with an objective to minimize the total charging cost. Since electricity price at the charging node may be subject to uncertainties, Information Gap Decision Theory (IGDT) is proposed in this paper to handle uncertainties in the price. The original intelligent charging scheduling problem is non-linear. The paper proposes a modified Mixed Integer Linear Programming (MILP) based reformulation and solves with CPLEX using GAMS as an aggregator.

The work proposed in this article proposes a different energy management scheme for residential consumers having higher penetration of solar power and heating loads. The presented methodology assumes the heating loads present in the residence as non-critical loads and tries to operate them in power saving mode, whenever necessary. In the considered scheme non-critical loads are connected in series with AC electric springs which in turn schedule them as per the requirement. The major scope of this work is to make the non-critical loads consume lesser power by pressing springs into action in the presence of solar power. It is expected that the springs compliment the efforts of solar panels in reduction of power consumed from utility grid, daily peaks and energy purchase from the utility grid. In order to implement the proposed methodology a completely customer driven home energy management algorithm is proposed and the robustness of the same is tested on a residential consumer, considering a 24 load curve. The simulation studies for the projected methodology are performed in MATLAB.

The work presented in this paper proposes a new Demand side management (DSM) algorithm for residential users having higher penetration of heating/resistive loads. The proposed algorithm uses electric springs to operate the non-critical loads in power saving mode, so as to reduce the daily peak, ramping and energy bill paid by the consumers. In order to implement the suggested methodology it is assumed that all the non-critical loads present in the residential complex are fitted with electric springs. The quantum of the power to be saved is completely left to the consumers and the customers are given full independence to choose the required percentage saving in power, which in turn reduces the electricity bill paid by them. Further it should be noted that the proposed energy management scheme re-shapes the entire load curve and achieves energy saving without shifting the non-critical appliances to off-peak timings, which is something new to the literature. The robustness of the suggested algorithm is tested by considering a 24 hour load curve of a residential user and the simulation studies of the same are performed in MATLAB.

In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving the unit commitment (UC) problem. Dynamic programming (DP) is a conventional algorithm used to solve the deterministic problem. In this paper DP is used to solve the stochastic model of UC problem. The stochastic modeling for load and generation side has been formulated using an approximate state decision approach. The programs were developed in a MATLAB environment and were extensively tested for a four-unit eight-hour system. The results obtained from these techniques were validated with the available literature and outcome was good. The commitment is in such a way that the total cost is minimal. The novelty of this paper lies in the fact that DP is used for solving the stochastic UC problem.

 Abstract—Security assessment is a major concern in real time operation of electric power systems. Traditional method of security assessment performed by continuous load flow analysis involves long computer time and generates voluminous results. This paper presents a practical and feasible Support Vector Machine Based Pattern Classification (SVMBPC) approach for static security assessment in power systems. The proposed approach classifies the security status of any given operating condition in one of the four classes - Secure, Critically Secure, Insecure and Highly Insecure based on the computation of a numeric value called security index. The feature selection stage uses a simple and straightforward forward sequential method to select the best feature set from a large set of variables. The static security classifier is designed by a multi-class SVM with different parameter tuning methods. The proposed approach is implemented in New England 39 bus and IEEE 118 bus systems and the results are validated. techniques and expert systems like fuzzy set theory has been proposed for security assessment problem, overcoming the pitfalls of traditional method. Literatures have reported the use of Artificial Neural Network techniques (4), (5), fuzzy logic combined with neural network (6), genetic based neural network (7) for static security assessment process. The performance of all these existing techniques are highly problem dependent and hence its suitability cannot be generalized. Nowadays, pattern classification is gaining more importance in solving many power system problems. In this approach, main bulk of work is done off-line to generate sufficient dataset. The classification function, designed based on the train set, helps to access the system security level in a short period of time. This paper addresses security assessment as a pattern classification problem with the classifier function designed by Support Vector Machine (SVM). SVM is a new and promising tool for learning separating functions in PR system with the capability of handling non-linear separability. The SVM classifier is designed for multi-classification based on the calculation of a term called Static Security Index (SSI), for each specified contingency. In this paper, four-class logic is used for the definition of system security viz., secure, critically secure, insecure, highly insecure. An operator likes to know exactly the severity level of disturbances for a given system operating condition. On-line security assessment allows the operator to know the security status and helps to determine the corrective actions. This paper also addresses different heuristic optimization techniques like Particle Swarm Optimization (8), Real Coded Genetic Algorithm (9) and Differential Evolution (10) used in the selection of SVM parameters globally. The classification approach is implemented in New England (NE) 39 bus system and IEEE 118 bus system and the results are compared.

It is important to handle the transmission network expansion planning problem under restructured power systems to allow fair competition in the system. A scenario-based two-stage Transmission Network Expansion Planning (TNEP) is presented in the paper and solved using a meta-heuristic algorithm. Various scenarios, for market-driven power flow patterns is considered in a 6-bus Garver Test System, and Differential Evolution is employed to solve the traditional Transmission Network Expansion planning problems in each scenario. A decision making scheme based on risk analysis is employed to select a plan with minimum risk. In this paper it is assumed that the market players trade only via pool market model, where bid pricing affects the generator loading.

The work presented in this paper addresses the problem of main grid dependability in micro grids using Electric springs. The micro grids which have more penetration of renewable sources are bound to depend on main grids during generation intermittence of the micro sources. In order to reduce the dependability of micro grids on main grid an efficient energy management algorithm has been presented. The proposed algorithm schedules the non-critical loads present in the micro grid using A.C Electric springs and the simulation studies are performed in MATLAB. The reduction in energy consumption from the main grid is achieved by making the non-critical loads consume lesser power during generation intermittence in the micro grid.

This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques.

The paper presents a biogeography based optimization algorithm to design an optimal meter placement scheme which makes the power system network observable. The procedure consists of initially determining the optimal meter set under normal conditions, followed by the optimal reliable meter locations that are obtained under two types of contingencies termed as single meter failure or loss and single branch outages. This is achieved by modifying the derived scheme from the normal condition using penalty functions. The effectiveness of the meter placement algorithm is demonstrated for IEEE standard systems. � 2011 Elsevier B.V. All rights reserved.

Security is recognized as an important problem in planning, design and operation stages of electric power systems. Power system security assessment deals with the system's ability to continue to provide service in the event of an unforeseen contingency. This paper proposes a particle swarm optimization (PSO) based classification for static security evaluation in power systems. A straightforward and quick procedure is used to select a small number of variables as features from a large set of variables which are normally available in power systems. A simple first order security function is designed using the selected features for classification. The training of weights in the classifier function (security function) is carried out by PSO technique. The PSO algorithm has minimized the error rate in classification. The procedure to determine the security function (classifier) is discussed. The performance of the algorithm is tested on IEEE 14 Bus, IEEE 57 Bus and IEEE 118 Bus systems. Simulation results show that the PSO classifier gives a fairly high classification accuracy and less misclassification rate. � 2010 Elsevier B.V. All rights reserved.

Security evaluation is a major concern in real time operation of electric power networks, exhibiting behavioral patterns under abnormal conditions. Security assessment and evaluation can be viewed as a pattern analysis task identifying abnormal patterns of the power system behavior under highly loaded conditions. Traditional method of security evaluation are highly time consuming and infeasible for direct on-line implementation. This paper presents application of pattern directed inference system for static and transient security evaluation and classification. A straightforward and quick procedure called Sequential Forward Selection method is used for feature selection process. The classifier model in the pattern directed inference system is designed using different pattern classifier algorithms, viz., conventional, neural network and machine learning classifiers. Support Vector Machine (SVM), one of the popular machine learning classifier, is recognized as a suitable pattern classifier for security evaluation problem. The generalization performance of SVM classifier is greatly influenced by the proper setting of its parameters. This paper also addresses different heuristic optimization techniques used in the selection of SVM parameters. The design, development and performance of different classifiers for power system security classification are presented in detail. Simulation work is performed on standard New England 39-bus benchmark system and the feasibility of implementation of the proposed SVM based classifier system for on-line security evaluation is also discussed. � 2012 Elsevier Ltd. All rights reserved.

Smart Grid envisions improving the efficiency, reliability, quality, security, economy etc. of the electric energy systems to meet the 21st century demands. As the major concern in 21st century is the environmental impact, it is very essential to make the present grid eco-friendly. This paper presents the research carried out in these issues and also on the latest technologies in smart grid. Smart Grid features such as self-healing, eco-friendliness, are presented in this paper, with detailed discussions on substation automation, demand response, and distributed energy resources

Security assessment is a major concern in planning and operation studies of a power system. Conventional method of security evaluation performed by simulation involves long computer time and generates voluminous results. This paper presents a K-means clustering approach for classifying power system states as secure/insecure under a given operating condition and contingency. This paper demonstrates how the traditional K-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The proposed algorithm combines particle swarm optimization (PSO) with the traditional K-means algorithm to satisfy the requirements of a classifier. The proposed PSO based K-means clustering technique is implemented in IEEE 30 Bus, 57 Bus, 118 Bus and 300 Bus standard test systems for static security and transient security evaluation. The simulation results of the proposed algorithm are compared with unsupervised K-means clustering, which uses different methods for cluster center initialization. � 2011 Elsevier Ltd. All rights reserved.

Security assessment is an important concern in planning and operation studies of an electric power system. Conventional method of security evaluation is performed by simulation consisting of load flow program and transient stability analysis, consuming long computer time and generating voluminous results. This paper presents a practical Pattern Recognition (PR) approach for security assessment in power systems. The problem of security assessment is focused in two modes, viz., static and transient. Static security pertains to the study of violation of system components when subjected to contingencies like line/generator outages. Transient Security study deals with system dynamic behavior when subjected to severe perturbations like three phase faults. A Supervised Fuzzy C-Means (SFCM) algorithm is proposed in the classification phase of PR system for security assessment. The proposed algorithm is tested on 39 Bus New England and IEEE 57 Bus systems. The classification results of the proposed SFCM classifier is compared with the Method of Least Squares (MLS) and Multilayer Perceptron (MLP) classifiers. The results prove that the former gives high classification accuracy and less misclassification rate compared to the latter, enhancing the feasibility and applicability of SFCM algorithm for on-line security evaluation. Keywords: Security Assessment, Static Security, Transient Security, Pattern Recognition, Fuzzy C-Means.

This paper presents a fast hardware-efficient logic for fault detection and classification in transmission lines, implemented using a field-programmable gate array (FPGA). The general-purpose SPARTAN3E FPGA was employed for developing the prototype, with all the coding done using a hardware description language (HDL) called very high speed integrated circuit (VHDL). The proposed logic employs only one-terminal current samples and is based on wavelet analysis. Depending on the amount of high frequency components in the current signals after processing, the faults are classified into ten types. The Real Time Windows Target Toolbox of MATLAB was used to apply the current signal inputs to the prototype in real time. An adaptive threshold value is chosen, rather than a fixed threshold in the case of faults involving the ground, to make the classification reliable and accurate. The fault classification time is 6 ms, which is about 1/3 of the power frequency cycle (20 ms). A high level of computational efficiency is achieved as compared to the other wavelet-transform-based algorithms, since only the high frequency details at first level are employed in this algorithm. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a system modeled in the Electromagnetic Transients Program/Alternative Transients Program. The proposed logic was found to be highly reliable and accurate, even in the presence of fault resistance. � 2009 IEEE.

Decision tree approach for security assessment and evaluation is presented. The general decision tree methodology is proposed for the purpose of predicting the robustness of a power system in the occurrence of severe disturbances, and of discovering appropriate control actions whenever needed. The decision trees are built off-line and are used on-line. Their building calls upon an inductive inference method, which automatically extracts the relevant information from large bodies of simulation data and express in terms of the system controllable variables. In this paper decision trees are used for classifying the operating states of the power systems as either secure/insecure using the MW flows and voltages and the severity level of the violation and the control actions necessary by finding the location of operating point when dropped at the top node of the decision tree.

Artificial neural networks (ANN) based pattern recognition for steady state (SSA), transient (TSA) and dynamic security assessment (DSA) of the power systems is presented. Conventional methods for security assessment take lot of time to assess the security of power systems when all contingencies are considered. Artificial neural networks based pattern recognition is used to solve SSA, TSA and DSA and its effectiveness against conventional methods is presented. In addition contingency ranking and generator severity ranking is used to measure the severity of various contingencies which allows an operator to take preventive control (generator rescheduling) or to be ready for corrective actions. Direct methods are fast compared to numerical methods but proper choice of Lyapunov function is difficult. ANN based PR for SSA, TSA and DSA effectiveness is compared with other conventional methods like pattern recognition which confirms that ANN not only gives accurate results but also is adaptive to the topology changes of the power system. In addition contingency ranking and generator severity ranking has been addressed which allows the operator to take preventive control actions like generator rescheduling.

The controlling functions in the SCADA/EMS system are increasing with the expansion of the power system network as the demand for power increasing. It is difficult to control the functions with the centralized system and rely on a single vendor. Current technologies employ distributed systems that are open (i.e. vendor independent solution) to control the SCADA/EMS functions. With open systems utilities, it tends to incrementally upgrade rather than completely replace the EMS systems. Various open distributed architectures have been proposed to shoulder the SCADA/EMS functions. This paper introduces the concept of open system and examines one of the distributed hardware configurations for open EMS. The proposed work modeled the above contingency analysis procedure for a case study of 6 bus, 11 line network, which was done by a dedicated application server on the pre-specified port.

Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission system.

The demand for electricity is known to vary by the time of the day, week, month, temperature, and usage habits of the consumers. Though usage habit is not directly observable, it may be implied in the patterns of usage that have occurred in the past. A short-term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. How well training data is chosen in an ANN is the defining factor in how well the network's output will match the event being modeled.

Pattern classification is a task common to the areas of artificial intelligence, pattern recognition, neural networks and machine learning. Advanced research in these areas and simillarities in problem solving has led to further investigation into the area of statistcal pattern recognition. Pattern classification approach to diagnosis is a fundamental area of research for problems involving real-time application. Most of the research in pattern classification has evolved from statistical pattern recognition ( bayesian classifiers, linear discriminants ) and neural networks ( back-propagation training algorithm ). An integration of these approaches, termed as “neural network pattern classification” is proposed for efficient classificatory problem solving. Comparitive performance of this approach to conventional classifiers is presented. The suitability of this classifier is tested for two case studies of power systems for discrete and contiouous data training sets.

Real time fault detection and diagnosis (FDD) is an important area of research interest in knowledge based expert systems. Neurocomputing is one of fastest growing areas of research in the fields of artificial intelligence and pattern recognition. The authors explore the suitability of pattern classification approach of neural networks for fault detection and diagnosis. The suitability of using neural networks as pattern classifiers for power system fault diagnosis is described in detail. A neural network design and simulation environment for real-time FDD is presented. An analysis of the learning, recall and generalization characteristic of the neural network diagnostic system is presented and discussed in detail. >