Mitigating the Effects of Poor Power Quality Conditions Using Adaptive Model Predictive Current Control

Abstract

Permanent Magnet Synchronous Motors (PMSMs) are susceptible to performance degradation in applications with poor power quality, such as those found in regions with unstable electrical grids. Conventional control strategies often fail to maintain stability and efficiency under simultaneous voltage sags and harmonic distortions. This paper presents an adaptive Model Predictive Current Control (MPCC) framework designed to enhance the resilience of PMSM drives under such composite disturbances. The proposed strategy integrates three key mechanisms: a real-time voltage sag compensation module that adjusts current references and employs flux-weakening, a multivector-based harmonic mitigation technique embedded within the predictive cost function, and a thermal derating strategy for overload protection. A comprehensive PMSM model, incorporating electrical, mechanical, and thermal dynamics, is developed in MATLAB/Simulink for validation. Comparative analysis with open-loop and conventional MPCC strategies, as well as recent adaptive and thermal-aware MPC approaches from 2022–2024 literature, demonstrates the superior performance of the proposed adaptive MPCC. Under severe combined disturbances (e.g., a 0.74 pu voltage sag with 29.44% harmonic content), the proposed method limits speed droop to 0.4% and torque ripple to 2.368%, outperforming conventional MPCC which achieved 0.8% droop and 2.596% ripple. The speed improvement of 50% (0.8% to 0.4%) and torque ripple reduction of 8.8% (2.596% to 2.368%), though appearing modest in percentage points, represent significant enhancements in dynamic stability and torque quality under extreme grid conditions where conventional controllers struggle to maintain performance. The results validate the proposed controller as a robust solution for PMSM drives in demanding industrial environments.