Efficiency determination of in-service induction machines using gravitational search optimization

Abstract Diverse techniques have been put forth having different levels of accuracy and intrusion for evaluating the efficiency of in-service induction machines. An accurate, least intrusive method for assessing the efficiency with restricted measurements and unbalanced working supplies is needed for in-service induction motors without interrupting the electric drive process in the field. This research paper presents gravitational search optimization (GSO) to determine the induction machine efficiency in balanced/unbalanced power supply conditions. The proposed technique does not need intrusive no-load test. Proposed GSO technique utilizes the theory of Newtonian physics where masses act as agents to attain best values. The performance of proposed GSO technique for estimating the efficiency of induction machine at different load points is verified through simulation and established experimentally. Results indicate that the proposed computational intelligence technique performs better as compared to other computational based techniques like genetic algorithm and cuckoo search algorithm, with greater accuracy.