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Non-Linear State Estimation Using Derivative-Free Filters for a Three-Phase Induction Motor Model

Abstract This paper is intended to generate unbiased state estimates of a three-phase induction motor using derivative-free state estimation algorithms. The use of intrusive sensors located within the air-gap of the machine has typically been plagued by complexities and lack of robustness. Moreover, any eventual damage in the sensor results in the substitution of the whole motor. On the other hand, there have been major efforts to reduce the cost of high performance systems, besides increasing the versatility of the motor that all necessitate the use of state observers, thereby eliminating the sensors and their interfaces. In this work, a comparative analysis of the three derivative-free non-linear filtering schemes to estimate the states of a three-phase induction motor on the simulated model is presented. The efficacy of ensemble Kalman filter (EnKF) against the traditional sampling importance re-sampling particle filter (SIR-PF) and unscented Kalman filter (UKF) is illustrated. Comparative Monte Carlo simulation results are investigated comprehensively with respect to three different scenarios, namely step changes in load torque, speed reversal, and low speed operation. Computer simulations have been carried out in the presence of additive state and measurement uncertainties and from the extensive simulation studies, it is being inferred that the SIR-PF fails to generate accurate state estimates in the presence of step changes in the load torque as it does not take into account the most current observation in the sampling stage, whereas EnKF and UKF work well. Moreover, when compared on the basis of the sum of squares of the estimation errors (SSEE), which is very often used as the performance index, the performance of the unconstrained estimation algorithm using the EnKF is found to be significantly better than those obtained using UKF and SIR-PF formulations for all the scenarios taken for the simulation study. The results throw light on alleviating the intrinsic intricacies encountered in SIR-PF in parlance with the observer theory.