Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator. Thus, this paper proposes a Gaussian Process Regression (GPR) model based on Conditional Likelihood Lower Bound Search (CLLBS) to optimize the design of the generator, which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method. Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example. Firstly, this method uses the elementary effect analysis to choose the sensitive variables, combining the evolutionary algorithm to design the super Latin cube sampling plan; Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data. A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established, which combined the chi-square test to optimize the accuracy of the model globally. Secondly, after the model reaches the accuracy, the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint. Last, the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation (CEI) optimization method based on the re-interpolation model, which cross-validated the optimization results of the Gaussian process regression model. The above method increase the efficiency of generator by 0.76% and 0.5% respectively; And this method can be used for rapid modeling and multi-objective optimization of generator systems.