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

The software design of power system computation often requires modifications due to modernization of existing network and automation of the power system operation. Object-oriented design has gained widespread importance and acceptance in software development due to its advantages over other methodologies. An object-oriented design for power system analysis is proposed in this paper with greater concentration on power system equivalents and distribution system. The important contribution of this paper is the development of software objects for various transmission and distribution system components, which can be reused in most of the power system analysis programs. © Indian Institute of Science.

Department of Electrical Engineering

Journal of the Indian Institute of Science

Object-oriented power system analysis

Distribution System has been analyzed in this paper using Object-Oriented Approach. The important contribution of this paper is the development of software objects for various distribution system components in such a way that they can be reused in most of the distribution system analysis programs. The design, proposed in this paper, is used for developing load flow analysis program. Object-Oriented Design replicates the physical system structure exactly in the software. The extensibility of the object-oriented design is exploited to extend the radial load flow analysis module for performing the load flow analysis of weakly meshed system by deriving few specialized objects from the fundamental objects used for radial load flow. Modeled objects are implemented in C++, an object-oriented programming language, and tested with various test systems. The results obtained for 69-bus radial system and 33-bus weakly meshed system are provided. This design is being extended for developing load flow analysis module for 3-phase unbalanced distribution system and distribution system with dispersed generations. © 2004 IEEE.

2004 International Conference on Power System Technology, POWERCON 2004

Distribution system load flow using object-oriented methodology

This paper presents an efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with lineflows and voltage 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 than the other methods.

GENERAL

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Particle swarm optimization for economic dispatch with line flow and voltage constraints

The controlling functions in the SCAD A/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, will tend 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.

Open distributed client server model in an energy management system for power system security

A new formulation of power system state estimation problem and a new solution technique is presented. The formulation allows for inequality constraints such as VAr limits on generator and transformer tap ratio limits. The solution technique uses logarithmic barrier function to treat inequality constraints. Non-quadratic criteria for power system state estimation have to facilitate bad data detection and identification. The weighted least absolute value criteria have desired bad data rejection property. This paper shows that logarithmic barrier function can accommodate inequality constraints and then applying Newton method to solve KKT equation leads to problem structure which is identical to that of non-linear least square problem. The number of iterations needed for convergence does not increase linearly with problem size unlike LP algorithm.

Power system state estimation using ip barrier method

Application of genetic algorithms for the solution of unit commitment with detailed problem formulation, solution methodology and representation is described in this paper. New Encoding and Representation strategy is proposed that can handle large systems with an improvement in solution and faster convergence. The unit commitment problem is formulated as the minimization of the performance index, which is the sum of objectives (fuel cost, startup cost) and constraints (minimum up time (MUT), minimum down time (MDT), spinning reserve). Solution methodology and Simulation Results are provided for a 10-generator unit commitment problem for 24 h duration. © 2003 Elsevier Science Ltd. All rights reserved.

International Journal of Electrical Power and Energy System

A genetic algorithm approach to generator unit commitment

Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented.

IEEE Transactions on Power Systems

Unit commitment solution methodology using genetic algorithm

A short term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach in forecasting load is discussed. This method was used to overcome the drawbacks of traditional methods such as heavy computational time, large amount of memory space, and explicit relationships between different variables. It was found that the forecasted load obtained from the integrated architecture is better than that from the single ANN.

IEEE Computer Applications in Power

Integrated ANN: Approach to forecast load

The key issues in security assessment are fast identification of insecure contingencies and evaluation of their impact on power system operation. As such, an artificial neural network (ANN) aided method for security assessment is proposed and illustrated for a model six-bus power system. Using 15 different line-loading patterns for training, the network successfully classifies the unknown loading patterns. In particular, this powerful and versatile feature is especially useful for power system operation.

ANN approach assesses system security

Generic Algorithm (GA) approach to function optimization consists essentially of minimizing an objective function while gradually satisfying the constraint relations. Suitability of GAs for multi-objective optimization is presented in this paper. Detailed aspects of problem definition, formulation and implementation of GAs to the multi-objective optimization problem, with fuel cost (economy) and NOx emission (pollution) as conflicting objectives are discussed. The GA approach is similar to the fuzzy optimization approach in handling multiple objectives Graphical approach for the determination of the pareto-optimal solution is proposed. In the GA approach, the pareto-optimal solution finally converges to the optimal operating point for a particular load power demand. Environment constrained optimal economic dispatch results for a 6 generator unit of the IEEE 30 bus power are presented for various loading conditions.

World University root node

International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications

Genetic algorithm approach to environmental constrained optimal economic dispatch

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 domain, 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 systems.

Proceedings of the IEEE International Conference on Power Electronics, Drives & Energy Systems for Industrial Growth, PEDES

Fault detection and diagnosis of power converters using artificial neural networks

This paper presents the genetic algorithm approach to adaptive optimal economic dispatch of electrical power systems. Genetic algorithms, also termed as the machine learning approach to artificial intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The unique problem solving strategy of the genetic algorithm and their suitability for power system optimization is described. The advantages of the genetic algorithmic approach in terms of problem reduction, flexibility and solution methodology are also discussed. The suitability of the proposed approach is described for the case of a 15 generator power system. © 1993 IEEE.

Proceedings of the 2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993

Optimal economic power dispatch using genetic algorithms

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. Neural computing is one of the fastest growing areas of artificial intelligence research. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of electric power systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of a model power system. © 1990 IEEE.