Parameter identification of permanent magnet synchronous motor based on modified- fuzzy particle swarm optimization
Shuai Zhou, Dazhi Wang, Ye Li
Abstract
Accurate estimation of PMSM parameters is beneficial to the high performance operation of PMSM. In order to prevent PSO from falling into the local optimal solution in the PMSM parameter identification process, so as to improve the accuracy of identification results, a modified fuzzy particle swarm optimization (MDFPSO) is proposed, which changes the speed of each particle from only affected by the optimal particle to affected by the surrounding particles, This improvement guarantees the identification accuracy of the algorithm, and introduce the convergence factor to ensure that the MDFPSO can converge. Simulation results show that the MDFPSO algorithm is effective in PMSM parameter identification.
Topics & Concepts
Particle swarm optimizationControl theory (sociology)Identification (biology)Convergence (economics)Permanent magnet synchronous motorProcess (computing)Fuzzy logicComputer scienceEstimation theoryMagnetMathematical optimizationMathematicsAlgorithmEngineeringArtificial intelligenceMechanical engineeringEconomic growthBotanyBiologyOperating systemEconomicsControl (management)Sensorless Control of Electric MotorsElectric Motor Design and AnalysisAdvanced Algorithms and Applications