Computational Intelligence for Dynamic Solar Energy Yield Under Partial Shading and Transient Conditions

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Solar photovoltaic arrays rarely operate under ideal, uniform irradiance. Shading from clouds, buildings, and vegetation, as well as soiling, creates spatially non-uniform conditions that produce multiple peaks in the power-voltage curve. Under these circumstances, conventional Maximum Power Point Tracking algorithms often fail to converge, resulting in continuous energy losses. This paper develops a conceptual framework for using computational intelligence to improve dynamic solar energy yield in partial shading and transient environments. The framework examines how to structure the logic underlying unclear logic, artificial neural networks, evolutionary algorithms, and reinforcement learning to navigate complex, nonconvex search spaces in real time. Rather than focusing on a single algorithm, the analysis treats computational intelligence as a layered decision system that balances exploration, exploitation, computational cost, and system stability. The conceptual model links algorithmic behavior to energy yield, inverter stress, and lifecycle cost implications, arguing that adaptive intelligence reduces loss mismatches and improves resilience without requiring hardware changes.

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