IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

This study proposes a hybrid algorithm combining sequential quadratic programming (SQP) and differential evolution (DE) algorithm for solving the optimal power flow (OPF) problem. In this hybrid method, SQP is used to generate an individual, which is a member of an initial population, for DE algorithm. Having generated an individual by SQP, which will be nearer to the optimal solution, DE algorithm can reach the optimal solution more effectively than the classical evolutionary algorithms can. The proposed method has been used to solve the OPF problem on the standard IEEE 30- and IEEE 118-bus test systems to validate the effectiveness. Two different objectives, namely fuel cost considering valve-point effects and the transmission line losses, have been considered. The simulation results obtained from the proposed hybrid method reveal that this algorithm gives better solution for the problem having more non-convexity. © 2011 The Institution of Engineering and Technology.

Department of Electrical Engineering

IET Generation, Transmission and Distribution

Sequential quadratic programming based differential evolution algorithm for optimal power flow problem

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.

Expert Systems with Applications

Particle swarm optimization based K-means clustering approach for security assessment in power systems

Smart grid enables active participation by consumers which is known as Demand Response. In order to match the power demand and power supply, a new method for Dynamic Demand Control (DDC) with AGC in Smart Grid Environment is proposed. A load frequency control using DDC based on price signals was modeled in this study. When frequency decreases customers are charged for Unscheduled Interchange. The frequency deviation was compared with the frequency deviation obtained when generation control, using PI controller, alone was implemented for frequency control. Thus DDC alone is sufficient to maintain the system frequency, during small load variations. DDC will play a major role in reducing these losses caused to the GENCOs under a Smart Grid environment. © 2011 IEEE.

Proceedings - 2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011

Dynamic Demand Response and Control in Smart Grid environment

Smart grid enables active participation by consumers in Demand Response. In order to match the power demand and power supply, Dynamic Demand Control (DDC) with AGC in Smart Grid Environment is used. A load frequency control using DDC, was modeled for a two area system in this study. The frequency deviation and tie line flow deviation was compared with the deviations obtained when generation control, using PI controller, alone was implemented for frequency control. The PI controller parameters in the new scheme are then optimized using Lyapunov technique. Thus DDC alone is required to maintain the system frequency, during small load variations. DDC will play a major role in the economic operation of GENCOs under a Smart Grid environment. © 2011 IEEE.

2011 IEEE PES International Conference on Innovative Smart Grid Technologies-India, ISGT India 2011

Frequency restoration using Dynamic Demand Control under Smart Grid Environment

This paper presents an implementable distributed state estimation method for online analysis of power systems having multiple, geographically separated areas. Distributed state estimation apart from giving a faster solution, also improves the condition number of the resultant gain matrices. The method proposed here uses the conventional WLS estimator (Gauss-Newton method) with equality constraints forcing the tie line flows as calculated by the adjacent areas to be equal. Based on the topology of the network, the system is partitioned into multiple areas and a processor is assigned to each of these areas for solving the local state estimation problem. Simulations carried out on the IEEE 14, 30 and 118 bus systems show good convergence properties and improvement on the condition number of the gain matrices when compared with centralized algorithms. © 2010 Elsevier Ltd. All rights reserved.

International Journal of Electrical Power and Energy Systems

Tie-line constrained distributed state estimation

Modeling and simulation of nonlinear systems under chaotic behavior is presented. Nonlinear systems and their relation to chaos as a result of nonlinear interaction of different elements in the system are presented. Application of chaotic theory for power systems is discussed through simulation results. Simulation of some mathematical equations, e.g. Vander Pol's equation, Lorenz's equation, Duffing's equation and double scroll equations are presented. Theoretical aspects of dynamical systems, the existence of chaos in power system and their dependency on system parameters and initial conditions using computer simulations are discussed. From the results one can easily understand the strange attractor and transient stages to voltage collapse, angle instability or voltage collapse and angle divergence simultaneously. Important simulation results of chaos for a model three bus system are presented and discussed. © 2010 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Modeling and simulation of chaotic phenomena in electrical power systems

This paper gives a solution for the unit commitment problem (UCP) for an power system to minimize the total production cost over a period of time by considering the load as a random(Probabilistic) variable instead of a deterministic one. The UCP is a mixed integer large scale non linear program. If the number of unit is more and the time horizon is large, then the analysis become more complex. That is the reason still researches are active in finding the solution for unit commitment problem. In this paper the proposed methodology by considering the load as a probabilistic variable instead of regular deterministic one. By considering the load in this way we can solve the problem of UCP as a real time problem. In the proposed methodology LR method is used to separate the problem in to a primal and dual problem and each sub-problem is solved by using dynamic programming. The economic dispatch problem is solved by particle swarm optimization. By comparing the results with conventional method the proposed methodology gives the schedule of generator with minimum production cost. © 2011 IEEE.

School of Electrical Engineering

Vellore Campus

World University root node

2011 International Conference on Power and Energy Systems, ICPS 2011

A solution to unit commitment problem by considering load to be a probabilistic one

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.

Classifier design for static security assessment using particle swarm optimization

This paper proposes a multi-objective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a non-linear constrained multi-objective optimization problem where different objectives and various constraints have been considered into the formulation. Fast elitist non-dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, a fuzzy based mechanism has been used to select a compromise solution from the Pareto set. The proposed MOHS algorithm has been tested on IEEE 30 bus system with different objectives. Simulation results are also compared with fast non-dominated sorting genetic algorithm (NSGA-II) method. It is clear from the comparison that the proposed method is able to generate true and well distributed Pareto optimal solutions for OPF problem. © 2010 Elsevier Ltd. All rights reserved.

Multi-objective harmony search algorithm for optimal power flow problem

This paper presents a method for the use of Supervisory Control and Data Acquisition (SCADA) and synchronized measurements for complete observability of a power system. Under normal operation, both Node Phasor Measurement Unit (NPMU) and SCADA measurements are optimally placed using integer programming and Genetic Algorithm (GA) respectively. The minimum condition number of the Jacobian matrix is used as a criteria in conjunction with GA to obtain a completely determined condition. Next, a triangular factorization approach is used to search for the necessary candidates for single branch outage and single/multiple measurement loss. These candidate measurements are optimized by the binary integer programming method. Numerical results on the IEEE test systems are demonstrated. The results clearly show the robustness of the method to obtain reliable measurements under both normal and contingency conditions. © 2011 Elsevier Ltd. All rights reserved.

Optimal placement of PMU and SCADA measurements for security constrained state estimation

This paper presents a new multi-objective harmony search (MOHS) algorithm for environmental/economic dispatch (EED) problem. The EED problem is formulated as a non linear and constrained optimization problem with competing and non-commensurable objectives. The two competing objectives, fuel cost and emission, were optimized simultaneously using the proposed MOHS algorithm. The MOHS algorithm uses a non dominated sorting and ranking procedure with dynamic crowding distance to develop and maintain a well distributed Pareto-optimal set. The proposed algorithm has been tested on the standard IEEE 30 bus and 118 bus systems. Simulation results are compared with the fast non dominated sorting genetic algorithm (NSGA-II) method. The results clearly show that the proposed method is able to produce a well distributed Pareto-optimal solutions than the NSGA-II method. © 2011 Elsevier B.V. All rights reserved.

Electric Power Systems Research

Environmental/economic dispatch using multi-objective harmony search algorithm

This paper presents an approach of analyzing power system voltage stability based on system potential. This framework provides a simple and better way of understanding voltage stability by visualizing the changes in topological structure of the system potential with respect to the change in power system loading. This paper also investigates the effect of random load variation in the power system stability. The stability margin index being studied is the Mean First Passage Time of the power system from the stable operating point to the closest unstable equilibrium point for small magnitude random load perturbations. The variation of MFPT for change in the loading of the system, Intensity and correlation time of the stochastic load are studied initially for an SMIB system. © 2011 Elsevier Ltd. All rights reserved.

Voltage stability - Case study of saddle node bifurcation with stochastic load dynamics

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to "fit" and "extrapolate" the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price. © 2010 Elsevier Ltd. All rights reserved.

Electricity price forecasting using artificial neural networks

This paper presents a measurement technique suited for real-time transfer, monitoring and processing of state variables in power distribution networks, in terms of synchronized phasors. The proposed technique, different from commercial phasor measurement units (PMUs), is based on general purpose data acquisition hardware and uses network time protocol (NTP) for accurate time-stamping of the measurements. A least square approximation scheme for accurate frequency estimation is proposed, and with this scheme, it has been proved that the range of frequency with zero estimation error increases with the increase in number of re-samplings. © 2010 IEEE.

IEEE Transactions on Power Systems

High precision frequency estimation using internet-based phasor measurement unit

Static Security Assessment (SSA) is a major concern in planning and operation stages of electric power systems. The traditional method used in static security analysis involves solving full AC load flow equations for each contingency. This is highly time consuming and inadequate for real time applications. The Pattern Recognition (PR) approach is recognized as an alternative tool for on-line security evaluation. This paper proposes a recently introduced machine learning tool called Support Vector Machine (SVM) in the classification phase of pattern recognition approach. Many feature selection algorithms are used for selecting optimal feature subset in the design of PR system. The proposed SVM based PR approach is tested on IEEE 14 Bus and IEEE 57 Bus Systems. The performance of SVM classifier are compared with other classifiers like Multilayer Perceptron, Method of Least Squares and Linear Discriminant Analysis classifiers. Simulation results prove that SVM classifier gives a fairly high classification accuracy and less misclassification rate compared to other equivalent classifier algorithms. Copyright © 2010-11.

International Journal of Artificial Intelligence

Support vector machine based pattern recognition approach for static security assessment

This paper develops bidding strategy for operating multiunit pumped-storage power plant in a day-ahead electricity market. Based on forecasted hourly market clearing price, the objective is to self-schedule and maximize the expected profit of the pumped-storage plant, considering both spinning and nonspinning reserve bids and meeting the technical operating constraints. Evolutionary tristate particle swarm optimization (ETPSO) based approach is proposed to solve the problem, combining basic particle swarm optimization (PSO) with tristate coding technique and genetics-based mutation operation. The discrete characteristic of a pumped-storage plant is modeled using tristate coding technique and mutation operation is used for faster convergence. The proposed model is adaptive for nonlinear 3-D relationship between the power produced, the energy stored, and the head of the associated reservoir. The proposed approach is applied for a practical utility consisting of four units. Simulation results for different operating cycles of the storage plant indicate the attractive properties of ETPSO approach with highly optimal solution and robust convergence behavior. © 2010 IEEE.

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Evolutionary tristate PSO for strategic bidding of pumped-storage hydroelectric plant

This paper presents a novel Optimization based corrective strategy based on graph theory to mitigate overloads during contingencies. Generator rescheduling and/or load shedding is performed locally, to restore the system from abnormal to normal operating state. The directed acyclic graph (DAG) technique is used to identify the participating generators and load buses for corrective control action with respect to a contingency. Particle swarm optimization (PSO) is used to compute the optimal adjustments of generation and loads with security constraints such as line flows and bus voltage limits. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 14- and 30-bus systems. The result shows that the proposed approach is computationally fast, reliable and efficient, in restoring the system to normal state after a contingency with minimal control actions. Copyright © 2009 John Wiley & Sons, Ltd.

European Transactions on Electrical Power

Optimization based graph theoretic approach for corrective control strategies to mitigate overloads

This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality. © 2010 Elsevier Ltd.

Energy

Hybrid SOA-SQP algorithm for dynamic economic dispatch with valve-point effects

This paper develops optimal bidding strategies for a pumped-storage plant in a pool-based electricity market. In the competitive regime, when compared to simple hydro electric generator, profit of the pumped-storage plant is maximized by operating it as a generator when market clearing price is high and as a pump when the price is low. Based on forecasted hourly market clearing price, a multistage looping algorithm to maximize the profit of a pumped-storage plant is developed, considering both the spinning and non-spinning reserve bids and meeting the technical operating constraints of the plant. The proposed model is adaptive for the nonlinear three-dimensional relationship between the power produced, the energy stored, and the head of the associated reservoir. Different operating cycles for a realistic pumped-storage plant are considered and simulation results are reported and compared. Crown Copyright © 2009.

Energy Conversion and Management

Bidding strategy for pumped-storage plant in pool-based electricity market

This paper presents novel corrective control actions to alleviate overloads in transmission lines by the Particle Swarm Optimization (PSO) method. Generator rescheduling and/or load shedding is performed locally, to restore the system from abnormal to normal operating state. The appropriate identification of generators and load buses to perform the corrective control action is an important task for the operators. A new Direct Acyclic Graph (DAG) technique for selection of participating generators and buses with respect to a contingency is presented. The effectiveness of the proposed approach is demonstrated with the help of the IEEE 30 bus system. The result shows that the proposed approach is computationally fast, reliable and efficient, in restoring the system to normal state after a contingency with minimal control actions.