Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode

Hammed Olabisi Omotoso, Abdullrahman A. Al-Shamma’a, Mohammed Alharbi (), Hassan M. Hussein Farh, Abdulaziz Alkuhayli, Akram M. Abdurraqeeb, Faisal Alsaif, Umar Bawah and Khaled E. Addoweesh
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Hammed Olabisi Omotoso: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdullrahman A. Al-Shamma’a: Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Mohammed Alharbi: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Hassan M. Hussein Farh: Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Abdulaziz Alkuhayli: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Akram M. Abdurraqeeb: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Faisal Alsaif: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Umar Bawah: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Khaled E. Addoweesh: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

Sustainability, 2023, vol. 15, issue 10, 1-19

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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