Application of a Hybrid Model of Big Data and BP Network on Fault Diagnosis Strategy for Microgrid

Aiming at the characteristics of timely transmission, rapid update, and large magnitude of microgrid data, based on the large data samples generated by microgrid operation, a fault diagnosis and analysis method of microgrid systems supported by big data is proposed in this paper. The multisource joint feature vectors of microgrid are extracted using Wavelet transform, Rayleigh entropy, and big data technology, which combine short-circuit current and voltage. The extracted feature dataset is clustered and segmented to realize deep data mining. Combining BP neural network and big data, the fault diagnosis of microgrid is realized. The simulation results show that the BP neural network algorithm based on big data support can accurately identify the type and phase of internal faults in microgrid, which is more suitable for extracting the temporal characteristics of information and spatiotemporal correlation of data to realize the prediction of big data and solve the core problems in the analysis of big data of microgrid faults, and the accuracy is as high as 96.8%.