Transfer learning accelerating complex parameters optimizations based on quantum-inspired parallel multi-layer Monte Carlo algorithm: Theory, application, implementation

In doubly-fed induction generator-based wind turbines (DFIG-WTs), the rotor-side controller (RSC) with optimized parameters improves wind energy utilization efficiency. With long optimization times and inadequate exploration and development capabilities, conventional intelligent optimization algorithms are hard to find the controller parameters quickly in the complexity and nonlinearity of DFIG-WTs. A quantum-inspired parallel multi-layer Monte Carlo algorithm accelerated by transfer learning (QPMMCOA-TL) is proposed to shorten the optimization time of parameters and obtain the controller parameters more satisfactorily simultaneously. The QPMMCOA-TL possesses strong optimization capabilities through an accelerated search method based on transfer learning, a diversified population coding way, a parallel multi-layer structure, way of searching in the narrowing feasible region. In the optimization process, the fitness function replaced by trained deep neural networks is transferred to the search process of the QPMMCOA-TL for shorting the optimization time. The QPMMCOA-TL is applied to test two benchmark functions and compared with seven metaheuristic algorithms for completing the validity verification. The optimization time of the QPMMCOA-TL when searching the parameters of the RSC is 1188 s, which is one-tenth or less than other algorithms. Furthermore, the reliability and stability of the optimized controller are comprehensively enhanced. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

In doubly-fed induction generator-based wind turbines (DFIG-WTs), the rotor-side controller (RSC) with optimized parameters improves wind energy utilization efficiency. However, conventional intelligent optimization algorithms face challenges in quickly finding the controller parameters due to long optimization times and inadequate exploration and development capabilities. To address this issue, a quantum-inspired parallel multi-layer Monte Carlo algorithm accelerated by transfer learning is proposed. This algorithm significantly shortens the optimization time and enhances the reliability and stability of the optimized controller.