Parameter Estimation of IFOC-IMD using DEKF with Optimized Covariance Matrices

A Discrete-time Extended Kalman filter (DEKF) algorithm is employed for simultaneous multi-parameter estimation of an Indirect Field Oriented Control (IFOC)-based induction motor drive (IMD). The Hybrid PSOGWO optimization is implemented for optimum covariance matrices of DEKF for providing high-performance control in IMD. To resolve the problem that the DEKF is challenging to access the proper system and measurement error noise matrices (Q and R) in parameter estimation. These matrices are evaluated using the trial-and-error process and are intended to be consistent. However, the operating environment has an impact on these covariance matrices. The performance of the proposed system is compared with trial-and-error, PSO, and GWO optimization methods by considering the statistical indices as mean square error for optimum covariance matrices. The HPSOGWO is enhanced to avoid the local optimum by providing extreme interference and optimum fitness. The performance results demonstrate that the proposed technique has a faster nature and thus can precisely estimate the IM parameters.