Weather type clustering recognition strategy for dual-axis mode of photovoltaic power

For photovoltaic power systems, the recognition of weather type is the basis for working mode switching. The wrong choice of working mode restricts power generation efficiency. A weather type clustering recognition strategy first is proposed, which is based on solar radiation intensity, radiation intensity fluctuation value and wind speed. Then, the weather types are summarized and merged to obtain four broad weather types: sunny, cloudy, rainy and windy. The corresponding working mode is set by four broad weather types for the photovoltaic power system. Finally, the weather recognition dual-axis tracking system is compared with the current mainstream photovoltaic power system. The results show that the weather recognition dual-axis tracking system accurately distinguishes different meteorological types throughout the year. The solar energy utilization efficiency and environmental adaptability are improved for the dual-axis tracking system.