Dynamic probability modeling of photovoltaic strings and its application in fault diagnosis

Abstract

China’s installed photovoltaic (PV) capacity has surged in recent years, and the intelligent operation of PV power generation is of great significance to improve the generating of PV power stations. As the core of the PV power generation system, PV strings are exposed outdoors all the year round, which is easy to bring safety hazards affecting the safe operation of power stations. Affected by weather conditions, the output of PV strings is fluctuating, random and time-varying, which brings great challenges to fault diagnosis. Therefore, taking the uncertainty of the PV power generation system during its operation into consideration, this paper used the nonparametric kernel density estimation method to fit the probability density curve of the output for PV strings, updated the model with the dynamic time window technology, and eventually established a fault diagnosis method based on the dynamic modeling results of PV strings. Different from the typical real-time diagnosis, by setting a dynamic time window for statistical analysis, the PV strings with degraded performance that fail to be detected in real-time monitoring can be identified, which is conducive to fault diagnosis for PV power stations.

Keywords

• Photovoltaic strings
• Uncertainty analysis
• Nonparametric kernel density estimation
• Dynamic time window
• Fault diagnosis