一种基于神经网络的光伏电站发电状态判别方法与装置
By using a neural network-based approach, combined with GWO-SVR and PSO-GWO-SVR algorithms, real-time data of grid-connected equipment and the light intensity of photovoltaic power plants are obtained, enabling a comprehensive and reliable assessment of the status of photovoltaic power plants and solving the problems of reduced photovoltaic power generation and decreased grid connection reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID HENAN INFORMATION & TELECOMM CO
- Filing Date
- 2023-02-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies have failed to effectively monitor solar radiation data of photovoltaic power plants and temperature and humidity data of grid-connected equipment, resulting in reduced photovoltaic power generation and decreased grid connection reliability.
A neural network-based approach is used to acquire real-time temperature and humidity data of grid-connected equipment, combine it with irradiance and grid voltage data of the photovoltaic power station, and use GWO-SVR and PSO-GWO-SVR algorithms to perform state assessment and determine the operating status of the photovoltaic power station.
It improves the comprehensiveness and reliability of the power generation status assessment of photovoltaic power plants, enhances the operating efficiency and reliability of grid-connected equipment, and avoids the need for timely troubleshooting.
Smart Images
Figure CN116362374B_ABST