Simplified Model and Genetic Algorithm Based Simulated Annealing Approach for Excitation Current Estimation of Synchronous Motor

KAPLAN O., Celik E.

ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING, vol.18, no.4, pp.75-84, 2018 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 4
  • Publication Date: 2018
  • Doi Number: 10.4316/aece.2018.04009
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.75-84
  • Keywords: reactive power compensation, power factor, artificial intelligence, genetic algorithms, simulated annealing, REACTIVE POWER COMPENSATION, SEARCH ALGORITHM, PERFORMANCE ANALYSIS, PID CONTROLLER, OPTIMIZATION


Reactive power demanded by many loads besides active power is one of the important issue in terms of the efficient use of energy. The optimal solution of reactive power demand can be performed by tuning the excitation current of synchronous motor available in power system. This paper presents an effective application of genetic algorithm-based simulated annealing (GASA) algorithm to solve the problem of excitation current estimation of synchronous motors. Firstly, the multiple linear regression model used in a few studies for estimation of excitation current of synchronous motor, is considered and regression coefficients of this model are optimized by GASA algorithm using training data collected from experimental setup performed. The supremacy of GASA over some recently reported algorithms such as gravitational search algorithm, artificial bee colony and genetic algorithm is widely illustrated by comparing the estimation results. Owing to the observation of weak regression coefficient of load current indicating that it is not much beneficial to excitation current, load current is removed from the regression model. Then, the remaining regression coefficients are tuned to accommodate new modification. It is seen from the findings that both training and testing performance of the simplified model are improved further. The major conclusions drawn from this study are that it introduces a new efficient algorithm for the concerned problem as well as the multiple linear regression model, which has the advantages of simplicity and cost-friendliness.