Enhancing robustness and control performance of voltage source inverters using Kalman filter adaptive observer and ANN-based model predictive controller

Power electronic converters play a crucial role in integrating distributed generation, renewable energy sources, microgrids, and HVDC transmission networks into the grid. The control technique used in the voltage source inverters (VSI) is essential for handling load variations, system nonlinearity, stability, and fast transient response. This study focuses on improving the robustness and control performance of VSIs by integrating a Kalman filter adaptive observer into a finite control set model predictive control (FCS-MPC), resulting in an improved FCS-MPC strategy (IMPC). The classical FCS-MPC can be affected by inaccuracies due to measurement noise and uncertainties in system models, leading to less accurate predictions and suboptimal control actions. By employing the Kalman filter adaptive observer, real-time estimates of unmeasured variables are provided, compensating for uncertainties, and enhancing control performance. To further enhance flexibility and adaptivity, an artificial neural network (ANN)-based controller is designed. The ANN controller is trained offline using IMPC as baseline thus eliminating the need for online predictions and optimization. The ANN controller directly generates inverter switching configuration states, resulting in high-quality sinusoidal output voltage with low distortions. Comparative analysis is conducted for the classical FCS-MPC, IMPC, support vector machine (SVM), convolutional neural network (CNN), and ANN-based controllers under diverse operating conditions and system parameters. Although it has reduced interpretability, the ANN controller exhibits superior harmonic reduction, outperforming both MPC-based controllers and SVM. Evaluation against CNN-based controls also validates the ANN’s robustness and effectiveness in handling uncertainties, emphasizing its adaptability, efficiency, and practical applicability in power electronic applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.