A Distributed Photovoltaic Data Synchronous Collection and Recognition Method Based on Improved B-Spline Function

The cost of photovoltaic power generation system equipment is high, and the damage or reduced lifespan of its main equipment such as solar cells and batteries will directly harm the interests of users. Due to the initial promotion and use of photovoltaic power generation systems, there are relatively few relevant testing and monitoring equipment in the market. Therefore, it is of great significance to develop a dedicated data synchronization collection and identification system for photovoltaic power generation systems, providing comprehensive characteristic testing for scientific research and ensuring the safe operation of users. Due to the widespread application of distributed photovoltaic power plants in power enterprises, distributed photovoltaic power plants are prone to malfunctions during data collection, which affects the efficiency of data collection in distributed photovoltaic power plants. Therefore, in response to the problems in data collection of distributed photovoltaic power plants, this article proposes a distributed photovoltaic power generation data collection method based on time series and uses an improved B-spline function to reduce transmission energy and information transmission congestion probability. This method first removes abnormal data in distributed photovoltaic power plants, and after removing it, missing data is found. After repairing it, a collection model is constructed using a BP neural network (BPNN) to complete the collection of power generation data. The experimental results show that the method proposed in this article has significant help in effectively collecting power generation data, and it will occupy a place in future power generation data collection methods.