R. Bhuvaneswari

Multi-objective parameter estimation of induction motor using particle swarm optimization

In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented. Three different induction motor models such as approximate, exact and deep bar circuit models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. The sensitivity analysis is also performed to identify parameters, which have the most impact on motor performance. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the classical parameter estimation method. Simulation results show that the proposed PSO method was indeed capable of estimating the parameters over a wide operating range of the motor.

Optimization of Three-Phase Induction Motor Design Using Simulated Annealing Algorithm

Three-phase induction motors are designed to meet various special requirements. Irrespective of these requirements, the basic conditions to be fulfilled are (1) The starting torque must be high, and (2) Operating efficiency and power factor must be as high as feasible. This article discusses the formulation of design optimization of three-phase induction motor as a nonlinear multivariable programming problem to meet the above requirements. Three different objective functions were considered. The simulated annealing algorithm was used to obtain an optimum design. The algorithm was implemented on three test motors and the results indicate that the method has yielded a global optimum. The proposed algorithm results are compared with the conventional design results to select a suitable optimal design of the induction motor. The performance of the motor is found to improve with application of this algorithm.

Review: Artificial immune system for parameter estimation of induction motor

The conventional method of determining the steady state equivalent circuit parameters of an induction motor utilizes no-load and locked rotor tests. The values so determined may not give satisfactory results for a wide variation in the operating conditions. This paper presents a new algorithm based on the immune algorithm (IA) to optimize the parameters of three different induction motor models from the manufacturer data and/or from the tests. The non-linear equations of induction motor to be solved for the parameter estimation are formulated as a minimization problem. The equivalent circuit parameters are obtained as the solution of minimization of a normalized square error function of the difference between estimated and manufacturer data. The proposed IA approach has been tested and examined on two different sample motors. The proposed approach results have been compared with the classical parameter estimation technique and the genetic algorithm (GA). The results show the effectiveness and the robustness of the proposed approach.

Intelligent agent based auction by economic generation scheduling for microgrid operation

This paper presents the implementation of distributed controls in a microgrid operation. The approach utilizes the advantages of using Multi Agent Systems technology for controlled operation of a microgrid. The auctioneer aims to optimize the operation of the microgrid by optimizing the production of local distributed generators. An artificial immune system based algorithm is applied on a typical study case network assuming realistic market prices for power and distributed generators bids reflecting realistic operational costs. Simulation results clearly indicate that the agent based control framework is effective in coordinating the various distributed energy resources economically.

Optimal Design of Power Transformer Using Simulated Annealing Technique

The power transformer is one of the most important equipment in a power system. Optimal design of transformer involves determination of design parameters of a power transformer when a chosen objective is optimized, simultaneously satisfying a set of constraints. In this paper simulated annealing (SA) technique is proposed for Optimization of three-phase power transformer design (OPTD). The initial cost of transformer viz., material cost of stampings and cost of copper used for windings is chosen as the objective that is to be minimized. The algorithm is tested on two sample transformers. The results obtained indicate that the method has yielded a global optimum. The computation time and cost of active material are much reduced when compared with conventional design results. The efficiency of transformer is found to improve with application of this algorithm.

Optimal design of single‐phase induction motor using particle swarm optimization

Purpose – This paper aims to employ particle swarm optimization (PSO) technique for optimum design of single‐phase induction motor (SPIM) on the basis of maximizing the efficiency of the motor simultaneously satisfying a set of performance constraints.Design/methodology/approach – The design problem of a SPIM is presented as a nonlinear optimization problem on the basis of maximizing the efficiency of the motor. A set of performance constraints are imposed in the optimization procedure. Particle swarm optimization technique is used as an optimization tool for obtaining the motor dimensions corresponding to maximum efficiency. Incorporation of PSO as a derivative free optimization technique in solving SPIM optimum design problem significantly relieves the assumptions imposed on the optimized objective function.Findings – This approach has been applied to two sample motors and the results are compared with the evolutionary programming (EP) results. It is observed that the proposed approach is effective and ...

Design optimization of three‐phase energy efficient induction motor using adaptive bacterial foraging algorithm

Purpose – The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor.Design/methodology/approach – The induction motor design problem is formulated as a mixed integer nonlinear optimization problem. A set of nine independent variables is selected, and to make the machine feasible and practically acceptable, six constraints are imposed on the design. Two different objective functions are considered, namely, the annual active material cost, and the sum of the annual active material cost, annual cost of the active power loss of the motor and annual energy cost required to supply such power loss. A new adaptive BF algorithm is used for solving the optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling.Findings – The adaptive BF algorithm is validated for two sample motors and benchmarked with the genetic algorithm...

Application of soft computing techniques to induction motor design

Purpose – The purpose of this paper is to present a comparative study of the various soft computing techniques and their application to optimum design of three‐phase induction motor design.Design/methodology/approach – The need for energy conservation is increasing the requirements for increased efficiency levels of induction motor. It is therefore important to optimize the efficiency of induction motor in order to obtain significant energy savings. To optimize the efficiency, design of the induction motor has to be chosen appropriately. In this paper, computational intelligence techniques such as artificial neural network, fuzzy logic, genetic algorithm, differential evolution, evolutionary programming, particle swarm optimization, simulated annealing approach, radial basis function, and hybrid approach are applied to solve the induction motor design optimization problem.Findings – These methods are tested on two sample motors and the results are compared and validated against the conventional Modified H...

Hybrid approach using GA and PSO for alternator design

Electrical machine design optimization problem can be specified as searching for a compromise between requirements of manufacturers and users. This paper presents hybrid approach using genetic algorithm and particle swarm optimization (HGAPSO) as a modern optimization tool to optimize the alternator design. Three different objective functions are considered separately in this article, namely, the cost, the efficiency and the regulation of alternator. The design parameters of an alternator are optimized by using HGAPSO approach. The proposed method combines two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of alternator design The feasibility of HGAPSO approach is demonstrated on two sample alternators and the results are compared with the traditional PSO method. The results indicate the superiority of the HGAPSO approach over the traditional PSO technique in terms of solution quality and convergence rate.

Adaptive Particle Swarm Optimization for the Design of Three-Phase Induction Motor Considering the Active Power Loss Effect

The paper presents an effective evolutionary method to the optimum design of three-phase induction motor using adaptive particle swarm optimization (APSO) technique. To avoid premature convergence of the classical PSO algorithm, the parameters such as inertia weight factor and acceleration factors are made adaptive on the basis of objective functions of the current and best solutions. The optimization algorithm considers the annual cost of the motor including the power loss cost as objective function and six important motor performance indices as inequality constraints. These functions are expressed in terms of motor design variables. The APSO integrates penalty parameter-less constraint handling strategy for handling the constraints. The potential of the proposed approach has been tested on two sample motors, and the results are compared with genetic algorithm, classical PSO and conventional design methods. It is observed that the proposed method is superior in terms of solution quality, robustness and computational efficiency.