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.
B. Akinloye, B. G. Ologunwa, A. Okubanjo· Al-Mustaqbal Journal of Sust...· 0 citations
Feature selection plays a critical role in designing efficient and interpretable condition monitoring frameworks for electrical drives. In this paper, a correlation analysis of statistical and spectral features is performed for Permanent Magnet Synchronous Motor (PMSM) fault detection in naval windlass systems. Using both simulated data from a MATLAB/Simulink model and real shipboard current signals acquired from five Nigerian Navy vessels over one-month monitoring periods, higher-order statistical moments (Mean, Variance, Standard Deviation, Skewness, Kurtosis) and the Fault Severity Index (FSI) were computed alongside Total Harmonic Distortion (THD). Pearson correlation coefficients were employed to quantify feature relationships under healthy and faulty operating modes, while scatter-plot clustering was used to visualize feature separability across six fault classes. The dataset comprised 2,880 observation windows per vessel (2 kHz sampling, 60-minute windows with 30-minute overlap), yielding a total analytical corpus of 14,400 windows from ship data and 12,000 high-resolution windows from simulation. Results demonstrated strong correlations between variance, standard deviation, and FSI (r ≥ 0.80–0.99), confirming their redundancy. Kurtosis and skewness exhibited weaker correlations with other first- and second-order features (r < 0.60) but showed a moderate inter-correlation of r = 0.88 with each other, indicating shared higher-order sensitivity. THD demonstrated weak correlations with all time-domain statistical moments (r < 0.65), confirming its role as an independent spectral indicator. All reported coefficients were statistically significant (p < 0.001, df = n − 2). The study highlights the potential for dimensionality reduction in PMSM diagnostic frameworks without compromising detection accuracy, with practical guidance for real-time embedded naval monitoring systems.
Ibrahim Muhammad, B. Akinloye· Mansoura Engineering Journal· 0 citations