Automatic fault detection system of campus distributed photovoltaic power station based on knowledge graph
Aiming at the problems of low classification detection rate and long time-consuming in fault detection of campus distributed photovoltaic power generation automation control system,a detection system based on knowledge map is designed.The system hardware includes STM32F103RCT6 single chip microcomputer,VMS-300AL infrared sensor,temperature sensor,voltage and current sensor,and the communication module is connected with the single chip microcomputer by SPI bus.On the system software level,the top-down knowledge map construction mode is selected,and the data pattern diagram of fault knowledge is given.Based on DNN network model,the characteristics of output set are trained to improve the system classification and detection ability.The experimental results show that the fault classification detection ability of the proposed detection system is strong,and the detection rates for the training set and the fault set are 99.53%and 99.37%respectively,and it takes less time to detect.
knowledge graphdistributed photovoltaic power generationautomated detectionLSTMDNN
万方数据
维普
