Identification of Abnormal Heating Situation of Photovoltaic Battery Groups under Thermal Coupling Mapping
In order to solve the safety hazards that may arise from the continuous abnormal heating of photovoltaic panels,as well as the deviation problem in the existing situation perception,a method for identifying the abnormal heating situation of photovoltaic panels is proposed.Integrating temperature data points from multiple time periods using sliding windows,constructing a time series photovoltaic cell temperature data matrix,and analyzing the thermal coupling mapping relationship between time data and temperature data.Based on the mapping relationship constraint machine learning algorithm,calculate the abnormal heating score,and determine the abnormal situation by taking the abnormal score value.The experiment shows that the trend display of photovoltaic cell group map-ping data is accurate,there are no omissions,and the error is low.
time seriesmapping perceptionphotovoltaic battery packabnormal warming trendthermal coupling relation-shipautomated identification
万方数据
维普
