Designing an Energy Efficient Induction Motor: Advanced Computational Intelligence Technique

Abstract

Abstract—This paper presents an advanced computational intelligence technique (CIT), modified particle swarm optimization (MPSO) for designing energy efficient three-phase induction motor (DEEIM) including active power loss effect. PSO performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. Even if PSO algorithm is easy to implement and has been shown to perform well on various power system optimization problems, they may get trapped in a local optimum due to premature convergence when solving the larger constrained problems. In the proposed MPSO algorithm, the PSO parameters such as inertia weight and acceleration factors are made adaptive on the basis of objective function. By adapting the PSO parameters, it not only avoids premature convergence but also explores and exploits the promising regions in the search space successfully. The efficacy of the proposed method has been demonstrated on 5 and 10 HP motors. The results of the proposed approach are compared with those obtained by other CITs (GA and PSO). It is found that the proposed MPSO based approach is able to provide better solution.

Keywords—Computational intelligence technique, modified PSO, induction motor design

Cite this Article

Sakthivel VP. Designing an Energy Efficient Induction Motor: Advanced Computational Intelligence Technique. Current Trends in Information Technology. 2019; 9(2): 5–13p.