Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems

• Published in: IEEE journal of emerging and selected topics in industrial electronics (Print) Vol. 5; no. 3; pp. 1143 - 1153
• Main Authors: Wang, Kangshi, Ma, Jieming, Man, Ka Lok, Huang, Kaizhu, Huang, Xiaowei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Atmospheric modeling; Data models; Domain adaptation; global maximum power point tracking (GMPPT); maximum power point estimation (MPPE); Maximum power point trackers; Maximum power tracking; Nonlinear systems; photovoltaic (PV) system; Photovoltaic cells; Photovoltaic systems; Randomization; Reactive power; Search algorithms; Voltage
• ISSN: 2687-9735; 2687-9743
• DOI: 10.1109/JESTIE.2023.3317803

Simulations of photovoltaic (PV) systems help understand the nonlinear power-voltage characteristics in real-world atmospheric conditions. However, the gaps between simulation and real-world domain are usually significant due to the modeling errors. Therefore, we propose a simulation-to-reality (sim-to-real) global maximum power point tracking (GMPPT) method with domain randomization and adaptation for PV systems. The randomized PV model is adapted to the real-world data domain to estimate the operating point at which the maximum power is drawn, and the sim-to-real gap is bridged via a vortex search algorithm. With the consolidated estimation results, the proposed method can adapt to the dynamics of the real-world environment and accelerate the GMPPT process. Experimental results show that the proposed method can reduce sim-to-real discrepancies and enhance GMPPT efficiency in comparison to the existing methods.