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

Artificial neural network approach to the problem of static security assessment of power system is presented. This paper utilizes the artificial neural net of Kohonen's self-organizing feature map (SOFM) technique that transforms input patterns into neurons on the two-dimensional grid to classify the secure/insecure status of the power system. SOFM uses the line flows under different component cases as inputs and self-organizes to obtain the cluster of the components based on their loading limits. The output of SOFM provides information about the violation of the constraints from which the operating state of the power system can be identified, which can be classified as secure or insecure. The proposed method of security assessment was initially demonstrated for a model 3 generator 6-bus system and later extended to IEEE-14, -30 and -57 bus systems. © Indian Institute of Science.

Department of Electrical Engineering

GENERAL

Journal of the Indian Institute of Science

Power system static security assessment using self-organizing neural network

In this paper, simulation and experimental based evaluation of novel lowpass to bandpass transformed PI control strategy for series hybrid active power filter is presented. In the series hybrid topology, the active filter is in series with a capacitor and this whole arrangement comes in parallel with an inductor. This hybrid setup is introduced in series between the source and load. The control structure is derived in the stationary reference frame through lowpass to bandpass transformation of ordinary PI controller transfer function. The effect of controller on system performance as well as its selective harmonic elimination is illustrated. The modelling, simulation and experimental results are presented. © 2005 Elsevier B.V. All rights reserved.

Electric Power Systems Research

A novel lowpass to bandpass transformed PI control strategy for series hybrid active power filter

A new method using artificial neural networks for the solution of the unit commitment (UC) and economic dispatch (ED) using Hopfield neural network (HNN) is presented. Discrete and Continuous Hopfield Networks have been used earlier to solve UC and ED problems separately. But these two problems are completely interdependent. Due to their inseparable nature, both the problems must be solved simultaneously. The difficulty in combining these problems is that while the first one requires a discrete neuron model, the latter requires a continuous neuron model. The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated. The important contribution of this work is the proposal of a new architecture for the discrete HNN for UC and the output of the UC module is used as input to the continuous HNN for ED. The associated advantage of using HNN for the combined solution of UC and ED is the decoupling of their interdependency, i.e., both the UC and ED are iteratively solved using respective HNN for the particular period. The implementation of the proposed method causes a considerable reduction in the HNN size and hence complexity and computation requirements, compared to earlier attempts. The method was successfully tested for different cases (3, 5, 6, 10 and 26 generator units), with varying load pattern of different durations (24 and 168 h) on Matlab on P-IV machine in windows enviroment. Each case study is done with an aim to bring out the important features of the proposed method. The results for the case studies are presented and important observations are discussed. © 2006 Elsevier B.V. All rights reserved.

Neurocomputing

Neural computation using discrete and continuous Hopfield networks for power system economic dispatch and unit commitment

An object-oriented (OO) model for three-phase unbalanced distribution system is proposed and implemented. Single-phase system components are initially modeled using object-oriented methodology. Later, it is extended to unbalanced three-phase distribution system using the "composition" technique of object-oriented design. The main features of object-oriented methodology such as "inheritance", "aggregation", "association" and "polymorphism" have been exploited to obtain a better software model. Three-phase distribution load flow analysis and short circuit analysis modules have been developed using the proposed object model. The performance of the developed software has been tested on IEEE 13-Node, IEEE 34-Node and IEEE 123-Node distribution systems. © 2006 Elsevier B.V. All rights reserved.

Modeling and analysis of unbalanced distribution system using object-oriented methodology

A dynamic topology processor for a radial distribution system using object-oriented methodology is presented. The potential of object-oriented design patterns is also employed to obtain an extendible design. The decorator design pattern is used to separate the topology-based attributes from the attributes representing physical characteristics of the objects. Using decorator pattern, responsibilities can be dynamically attached to or removed from the objects. The state design pattern is used to model the switching states of the tie and sectionalising switches. The proposed topology-processor algorithm is a local updating technique, hence less time consuming. The proposed algorithm has been implemented in C++ and tested with a modified 69-bus system. It has been found that the average CPU time required to modify the links between the objects following a switching operation is only 0.4ms.

IEE Proceedings: Generation, Transmission and Distribution

Dynamic topology processing in a radial distribution system

Energy management engineers are focusing their interest in tapping maximum profit for their system from Substation automation (SSA)/Distribution automation (DA). Volt/Var control through Fixed/Switched capacitors, Transformer taps and Voltage Set points are at different levels of research and implementation. A Neural Network based solution for Voltage- VAR control is proposed with the aim to reduce the real power loss flowing in a power system and subsequently improve the voltage profile. The module consists of two networks. The first network determines the control parameters i.e., generator voltage, transformer taps and shunt capacitance for minimal power loss when the loads at the load buses are specified as inputs. With the obtained parameters, a load flow program is run and power loss is noted and the system is checked for voltage violations. In case of voltage violations, the voltages are fed to the second network, which gives dQ at different buses for voltage violation minimization. These modules are successfully tested for different load patterns on a six-bus system. © 2005 IEEE.

Proceedings - 2005 International Conference on Intelligent Sensing and Information Processing, ICISIP'05

Neural network approach to voltage and reactive power control in power systems

Three different but interrelated phases of object-oriented methodology (OOM) in solving problems of electric power system network are discussed. The object-oriented analysis (OOA) phase of OOM focus on finding objects in the problem domain where objects can be found as naturally occurring entities or items in the domain of interest. Object-oriented design (OOD) for network topology processor (NTP) gives a flexible coding methodology and fast access to the database. The object-oriented programming (OOP) approach can be used in developing the power system analysis programs both for steady-state analysis and dynamic simulations.

IEEE Power and Energy Magazine

Object methodology

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 nil 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. © 2005 IEEE.

Neural network based pattern recognition for power system security assessment

Dynamic Economic Dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a novel methodology for solving dynamic economic dispatch. Ten units test system with smooth and non-smooth fuel cost functions are considered to illustrate the suitability and effectiveness of the proposed method. © 2005 IEEE.

Constrained optimization using evolutionary programming for dynamic economic dispatch

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. © 2005 IEEE.

Decision tree for steady state security assessment and evaluation of power systems

The paper presents a new approach to Power System Automation, Protection, Local control, Operator interfaces, Communication, Remote control and Monitoring functions. The components of the system will have a better communication with each other capturing information through the local area network; transfer the information through out the network using LAN or different communication mediums instead of one sensor per each component as before. To achieve, a better system is required apart from different systems like protection, Communication, RTU's, IED's etc. called as Data acquisition system with out the perfect data communication system the components of the total system cant perform the right tasks at right time because of the disturbance in the collected. To overcome this problem a better Data Acquisition system has been designed with the efficient technology and with the perfect communication systems to transfer the data. The system is termed as "Computerized Data Acquisition System for Power System Automation". The system acquires the data from the power network (data acquisition) for monitoring. The software is developed to perform all the manipulations and the system parameters can be viewed in different forms (analog, digital, graphical). © 2006 IEEE.

2006 IEEE Power India Conference

Computerized data acquisition for power system automation

Optimum location and size of capacitors for a distribution system under APDRP (Accelerated Power Development Programme) is presented. In the present study capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Genetic algorithm is used as an optimization tool, which obtains the optimal values and location of capacitors and minimizes the objective function, which is the power loss in the distribution network under study. A dedicated distribution system load flow is used to calculate power loss and voltage profile of the distribution system. Implementation aspects and important results for a 33 bus, 29 Indian power distribution system and practical 34 bus system have been presented to highlight the working of the algorithm. © 2005 IEEE.

Proceedings of INDICON 2005: An International Conference of IEEE India Council

Optimal capacitor allocation in radial distribution systems under APDRP

This paper proposes a methodology for the development of automatic scheduling techniques, for preventive maintenance of generating units and lines, in a competitive electric energy environment, with the inclusion of transmission constraints and forced outage rates, over a specified operational period. For generator maintenance the objective of the ISO is to maintain adequate level of reliability throughout the operational period (for which Bender's decomposition technique is used) and the objective of the GENCO is to maximize profit or to minimize loss in profit (for which transmission constrained price based unit commitment, TCPBUC, based on Lagrangian relaxation method is used). For line maintenance minimum cost model bender's technique with adequate level of reliability is used. A coordinating technique using penalty factors is incorporated to converge the conflicting objectives. The transmission constraints are modeled using dc sensitivity factors. Case study with a 6 bus, 3 generator, 11 line system is presented and discussed. © 2006 IEEE.

Coordinated maintenance scheduling of gencos and transcos in restructured power systems

Effective power system operation requires engineers and operator to analyze large amounts of multivariate data. Power system studies have a need for effective visualization software to display large amounts of information on the screen. The visualization tools encountered by many power system operators and engineers have evolved little beyond tabular displays and the one-line diagram. Java-based visualization of power system operation is presented. In addition to the single line diagram, various new approaches including contouring, addition of dynamically sized components, adding an extra dimension to the plot using various 3D Application Programming Interfaces (APIs), such as OpenGL are described. Two case studies of Economic Energy Interchange in a Power Pool with Energy brokerage and load flow are considered to illustrate the working of the proposed technique. © 2005 IEEE.

Visualization for power systems operation for economic energy interchange in power pools

This paper presents a detailed report of the performance and impact of UPFC on the power system behavior during fault conditions. A two machine - double line power system with UPFC installed in one of the lines is considered for the study. The idea is to observe the capability of the UPFC to maintain the active and reactive power flows in the compensated line (which includes UPFC) and to diminish the fall-off of the bus voltage when there is a grounding fault in the transmission line. Simulink based power system block set is used for numerical simulations. Simulation results indicate notable improvement in the behavior of the overall system with UPFC in upholding the voltage and power flows even under typical line faults by appropriate injection of series voltage into the transmission line at the point of connection. The extent of UPFC capability in maintaining the power flows in the line (Line 1), even under fault condition in an adjacent parallel line (Line 2) is provided. Minimizing the disturbances in voltages, currents and power flows in the fault-affected line (Line 2) are also discussed. Further the results illustrate how the UPFC contributes dynamically to a faster recovery of the system to the pre-fault conditions. © 2005 IEEE.

Performance of UPFC on system behavior under fault conditions

This paper proposes a method of solving transmission network expansion problem using LP-Based Particle Swarm Optimization. An optimal expansion plan for the existing transmission network is proposed. It formulates the design requirements of network planning as an operational planning model and is solved by an optimization algorithm such that an optimal planning scheme is obtained satisfying all constraints. Particle Swarm Optimization is a population- based search algorithm. The experimental results for linear programming technique, LP-based PSO and DC model are given. © 2006 IEEE.

Transmission expansion planning using LP-based particle swarm optimization

This paper presents an efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with security constraints .The proposed approach employ particle swarm optimization (PSO) algorithm for ELD. Incorporation of Type 1 PSO as a derivative-free optimization technique in solving ELD with voltages and lineflow constraints significantly relieves the assumptions imposed on the optimized objective function. The proposed approach has been tested on three representative systems, i.e. IEEE 14 bus, IEEE 30 bus and IEEE 57 bus systems respectively. The feasibility of the proposed method is demonstrated and the results are compared with Linear Programming, Quadratic Programming and Genetic Algorithm respectively. The developed algorithms are computationally faster(no. of load flows) than the other methods.

Proceedings of International Conference on Intelligent Sensing and Information Processing, ICISIP 2004

Particle Swarm Optimization for Security Constrained Economic Dispatch

An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper. Four modules consisting of the Basic ANN, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner form the integrated method for load forecasting. The Basic ANN uses the historical data of load and temperature to predict the next 24h load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures, respectively. The Averager captures the average variation of the load from the previous load behaviour, while the adaptive combiner uses the weighted combination of outputs from the Basic ANN and the Forecaster, to forecast the final load. The regression based and time series methods are conceptually incorporated into the ANN to obtain an integrated load forecasting approach. © 2004 Elsevier B.V. All rights reserved.

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

VIT Unitversity

Effect of temperature on short term load forecasting using an integrated ANN

A new method for the solution of the problems of Unit Commitment (UC) and Economic Dispatch (ED) using Hopfield Neural Network (HNN) is proposed in this paper. The difficulty in combining these problems is that while the first one requires a discrete neuron model, the latter requires a continuous neuron model. The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated. The important contribution of this work is the proposal of a new architecture for the discrete HNN for UC and the output of the UC module is used as input to the continuous HNN for ED. The associated advantage of using HNN for the combined solution of UC and ED is the decoupling of their interdependency i.e., both the UC and ED are iteratively solved using respective HNN for the particular period. The implementation of the proposed method causes a considerable reduction in the HNN size and hence complexity and computation requirements, compared to earlier attempts. The method was successfully tested for different cases (3,10,11 and 26 generator units), with varying load pattern of different durations (24 and 168 hours).

Hopfield neural network approach to the solution of economic dispatch and unit commitment

Application of Hopfield Neural Networks to Economic Dispatch is presented. Mapping the energy function of the Hopfield Neural Network to the objective function of the economic dispatch provides the basic relations for the parameters of the Hopfield Neural Network, which are tuned for the optimization problem. Adaptive computation of the parameters for the variation in the load demand is proposed and tested for several situations. The working of the Hopfield Network is illustrated for a three-generator system. Simulation results are presented for different loading conditions.