A deep fully connected layer fan controller parameter multi-objective optimization acceleration method

Abstract

This invention proposes a multi-objective optimization acceleration method for wind turbine controller parameters using a deep fully connected layer. This method is applied to multi-objective problems, and during the iteration process, the search agent's position is updated in three directions: proportion, exploration, and development. For situations where the external archive set is crowded, an adaptive grid strategy is adopted to update non-dominated solutions to address the congestion. A roulette wheel strategy is used to select the globally optimal position. A deep fully connected layer model is added during the method iteration process to improve convergence speed. This multi-objective optimization acceleration method for wind turbine controller parameters can solve the bi-objective optimization problem of doubly-fed induction generator (DFIG) wind turbine controller parameters, enabling rapid acquisition of optimal solution sets from multiple schemes. It optimizes the diversity and convergence of the solution set in multi-objective methods, reduces the required optimization time, and improves the running speed of the optimization method.

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