Parameter Identification of Induction Motor Using Modified Particle Swarm Optimization Algorithm
This work addresses parameter identification for induction motors, which is important for control and efficiency in industrial applications, but it is incremental as it modifies an existing optimization method.
The paper tackled the problem of induction motor parameter identification by proposing a technique using a modified Particle Swarm Optimization (PSO) algorithm to minimize error between measured and simulated startup currents, demonstrating its superiority over other methods like line search, conventional PSO, and Genetic Algorithms.
This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured and the simulated currents. The performance of the modified PSO is compared with other optimization methods including line search, conventional PSO and Genetic Algorithms. Simulation results demonstrate the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the modified PSO over other optimization techniques.