Robust modified adaptive PI-based controller for managing uncertainties in distributed generation systems of AC microgrids

Abstract

• A modified adaptive PI-based control strategy is developed to enhance stability under uncertain conditions in microgrid systems. • The proposed approach improves voltage regulation to within ±1 % and frequency stability to within ±0.5 %. • Total Harmonic Distortion (THD) values are reduced to 0.71 %−4.35 %, ensuring compliance with IEEE-519 standards. • The controller enhances dynamic response times by 25 % compared to traditional methods. • The strategy is scalable, reliable, and suitable for modern adaptive microgrid systems. Uncertainty refers to the divergence between mathematical models and real-world systems, especially in the context of distributed generation (DG) systems in microgrids (MGs). This study explores the impact of uncertainty in system parameters on the robustness within DG systems in MGs, which is essential for inner control loops (ICL) in MG. Uncertainties are modelled mathematically to deal with the variability of crucial elements, specifically those of the LC filter, such as resistance, inductance, and capacitance. Consequently, the resultant system model helps assess the impact of the uncertainties on the modified adaptive proportional integral-based controller (MAPIC), especially in the inner control loops that deal with voltage and current control. This research aims to contribute to checking and ensuring the reliability of MG systems under uncertainty modelling, which leads to guaranteed equitable power-sharing and robustness for any variation in the system and good performances. MATLAB software is used to simulate and validate the robustness of the MG system. Simulation results demonstrate significant stability improvements. Voltage regulation is maintained within ±1 %, and frequency stability within ±0.5 %. Under ideal conditions, total harmonic distortion (THD) values for current and voltage are recorded at 1.74 % and 4.08 %, respectively. However, under dynamic uncertainties, the THD values reduce to 0.71 % and 4.35 %, reflecting an improvement of 0.8 % in THD reduction, meeting the IEEE 519 standard in both study cases. Furthermore, the system's response time to dynamic load changes and DG unit connections improves by 25 % compared to conventional PI controllers, enhancing the MG's overall reliability and resilience. These findings validate the proposed control strategy as an effective and scalable solution for managing the uncertainties inherent in MG operations.

Topics & Concepts