Research on Parameter Optimization Method of Sliding Mode Controller for the Grid-Connected Composite Device Based on IMFO Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 1; p. 149
• Main Authors: Sun, Ji, Liu, Jiajun, Miao, Miao, Lin, Haokun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.12.2022; MDPI
• Subjects: Algorithms; Analysis; back-to-back MMC-HVDC; Electricity distribution; grid-connected composite device; Methods; moth-flame optimization algorithm; Optimization; Python–PSCAD joint simulation; Simulation methods; sliding mode control; Working conditions; back-to-back MMC-HVDC; sliding mode control; Python–PSCAD joint simulation; grid-connected composite device; moth-flame optimization algorithm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23010149

In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode control (SMC) into GCCD controller. Firstly, the mathematical model of MMC converter is established, and the sliding mode controller is designed based on the SMC principle. Then, aiming at the problems of complex controller structure and difficult parameter tuning in multiple modes of the GCCD, this paper proposes a controller parameter optimization method based on improved Month Flame optimization (IMFO) algorithm. This method improves the MFO algorithm by introducing good point set (GPS) initialization and Levy flight strategy, which accelerates the convergence speed of the algorithm while avoiding falling into local optimization, and realizes the optimization of converter controller parameters. Under a variety of standard test functions, the advantages of the proposed IMFO algorithm are verified by comparing it with the traditional algorithm. Finally, in order to realize the automatic tuning of control parameters, the Python–PSCAD joint simulation method is studied and implemented. Taking the comprehensive integral of time and absolute error (CITAE) index as the objective function, the parameters of the sliding mode controller are optimized. The simulation results show that the controller parameters optimized by the IMFO algorithm can make the GCCD have better dynamic performance.