Weight Optimisation for Model Predictive Control based on Particle Swarm Optimisation
Conference: PCIM Europe digital days 2020 - International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management
07/07/2020 - 07/08/2020 at Deutschland
Proceedings: PCIM Europe digital days 2020
Pages: 7Language: englishTyp: PDF
Authors:
Abdelrahem, Mohamed; Kennel, Ralph (Institute for Electrical Drive Systems and Power Electronics (EAL), Technical University of Munich (TUM), Munich, Germany)
Ismeil, Mohamed A. (Aswan Power Electronics Applications Research Center (APEARC), Aswan University, Aswan, Egypt & Electrical Engineering Department, Faculty of Engineering, South Valley University, Egypt)
Ali, Abdelfatah (Electrical Engineering Department, Faculty of Engineering, South Valley University, Egypt.)
Gaafar, Mahmoud A. (Aswan Power Electronics Applications Research Center (APEARC), Aswan University, Aswan, Egypt)
Abstract:
This paper presents an optimisation-based method to tune the weighting factors of Model Predictive Control (MPC) for a three-phase Split Source Inverter (SSI). Particle Swarm Optimisation (PSO) algorithm is used to optimally solve the optimization model considering system constraints. This technique is a powerful method to get a good tracking response for a reference signal or/and set-point based weight optimization. The proposed method is presented instead of the trial-and-error approach. The MATLAB/SIMULINK is used to validate the proposed technique.