Research on Anomaly Identification of PV Power Station Operation Data Based on Fuzzy Association Rules

Aiming at the problem that the operating data of photovoltaic power station is characterized by high suddenness and irregularity, which affects the accuracy of abnormal identification of operating data of photovoltaic power station, the method of abnormal identification of operating data of photovoltaic power station based on fuzzy association rules is studied. Collect the operation data of photovoltaic power station, use the fuzzy C-means clustering algorithm to process the operation data of photovoltaic power station, and build a fuzzy attribute set. The association rule algorithm is used to mine the association rules in the fuzzy attribute set of the operation data of photovoltaic power station, and form the strong association rules for abnormal identification of the operation data of photovoltaic power station; The multi mutation particle swarm optimization algorithm is used to optimize the association rule algorithm, and the optimized fuzzy association rules are used to output the abnormal identification results of the operation data of the photovoltaic power station. The experimental results show that the method can accurately identify the abnormal data in the operation data of photovoltaic power station, and the identification accuracy is higher than 98%.