Optimization of efficiency and output power of 8/6 switched reluctance motor using new neural network-based adjoint L p metric method

Abstract

Here, the authors present a new constrained multi-objective optimization method for maximizing output power and efficiency of switched reluctance motor (SRM). The constraints play a significant role in this process. Artificial neural network-based models are created to represent the SRM behaviour which is a difficult task. For this reason, a single neural network for each objective is built and the number of training data has been increased to reach a good accuracy. The authors propose a neural network-based adjoint L p metric technique to combine several objective functions and constraints with different weight factors to a single function which should be minimized. To apply the constraints, an adjoint term is added to the original L p function including only objective functions. The flux densities of the magnetic materials are selected as the optimization constraints. When approaching as close as possible to the saturation level, the SRM has a better performance. Adding an adjoint term to the original L p leads to better optimization results which were proved by comparing with a conventional optimization. Also, all obtained results were finally validated by the Ansoft Maxwell software.