A novel soft sensing method using intelligent modeling method for solar irradiance and temperature in distributed PV power plant

Abstract

Distributed photovoltaic (PV) power plants often lack solar irradiance monitoring devices, significantly hindering crucial functions such as power forecasting, fault diagnosis, and performance calculation for distributed PV. To address this issue, a real‐time method for soft sensing solar irradiance was proposed in distributed PV. First, we investigated the typical relationship between solar irradiance, ambient temperature, and the electrical characteristics of PV cells. Based on this relationship, we utilized the small sample modeling technique of the Genetic Algorithm‐Support Vector Machine to calculate the ambient temperature. Subsequently, a solar irradiance calculation model based on the backpropagation neural network was developed, taking the PV array voltage, current, calculated ambient temperature, and power as inputs. This approach enables for the estimation of solar irradiance in distributed PV power plants through a simple and efficient calculation process. To demonstrate the reliability and flexibility of the algorithm, we conducted testing with data under various input conditions, such as different power plant configurations, and seasons, coefficient of determination for the proposed model reached 0.95. Overall, the novelty of the proposed method offers a practical solution for soft sensing of solar irradiance in PV power plants, enabling accurate performance analysis and effective operation management without hardware investment.