Citation: Omotoso, H.O.; Al-Shamma’a, A.A.; Alharbi, M.; Farh, H.M.H.; Alkuhayli, A.; Abdurraqeeb, A.M.; Alsaif, F.; Bawah, U.; Addoweesh, K.E. Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode. Sustainability 2023, 15, 8018. https://doi.org/10.3390/su15108018 Academic Editor: J. C. Hernandez Received: 9 April 2023 Revised: 8 May 2023 Accepted: 12 May 2023 Published: 15 May 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode Hammed Olabisi Omotoso 1 , Abdullrahman A. Al-Shamma’a 2 , Mohammed Alharbi 1, * , Hassan M. Hussein Farh 2 , Abdulaziz Alkuhayli 1 , Akram M. Abdurraqeeb 1 , Faisal Alsaif 1 , Umar Bawah 1 and Khaled E. Addoweesh 1 1 Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; homotoso@ksu.edu.sa (H.O.O.); aalkuhayli@ksu.edu.sa (A.A.); amohammed6@ksu.edu.sa (A.M.A.); faalsaif@ksu.edu.sa (F.A.); baumar@ksu.edu.sa (U.B.); khaled@ksu.edu.sa (K.E.A.) 2 Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia; aaalshammaa@imamu.edu.sa (A.A.A.-S.); hhhussein@imamu.edu.sa (H.M.H.F.) * Correspondence: mohalharbi@ksu.edu.sa Abstract: This research paper presents a novel droop control strategy for sharing the load among three independent converter power systems in a microgrid. The proposed method employs a machine learning algorithm based on regression trees to regulate both the system frequency and terminal voltage at the point of common coupling (PCC). The aim is to ensure seamless transitions between different modes of operation and maintain the load demand while distributing it among the available sources. To validate the performance of the proposed approach, the paper compares it to a traditional proportional integral (PI) controller for controlling the dynamic response of the frequency and voltage at the PCC. The simulation experiments conducted in MATLAB/Simulink show the effectiveness of the regression tree machine learning algorithm over the PI controller, in terms of the step response and harmonic distortion of the system. The results of the study demonstrate that the proposed approach offers an improved stability and efficiency for the system, making it a promising solution for microgrid operations. Keywords: machine learning; grid-forming inverters; microgrid 1. Introduction The increasing demand for electricity, coupled with the pressing concerns of global warming, have prompted a substantial increase in the use of renewable energy resources such as photovoltaic (PV) and wind energy. The integration of such resources has ac- celerated the development of microgrids, which represent a form of distributed energy sources [1–3]. A microgrid is essentially a connected unit of the grid, consisting of one or more distributed generation (DG) units in proximity that can operate either in par- allel with or independently of a power utility grid, while providing reliable power to various consumers. The high integration of renewable energy sources poses challenges for power sys- tem operations [4,5]. Although PV/Wind integrated into an MG system have irrefutably brought positive impacts such as voltage regulation, line loss reduction, frequency regula- tion, and a reduction in distribution and transmission congestion [6], they still suffer from serious setbacks. In particular, their failure to ensure a continuous energy supply because their erratic and unstable nature impacts the grid integration. Therefore, voltage and frequency regulation are vital if the appropriate voltage and frequency at the customer’s point of common coupling (PCC) are to be sustained [7]. Inverters serve as a crucial component for the operation of microgrids in both islanded and grid-connected modes. In grid-connected systems, the grid regulates the voltage Sustainability 2023, 15, 8018. https://doi.org/10.3390/su15108018 https://www.mdpi.com/journal/sustainability