AUTHOR=Zhang Daohua , Jin Xinxin , Shi Piao 

TITLE=Research on power system fault prediction based on GA-CNN-BiGRU

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1245495

DOI=10.3389/fenrg.2023.1245495

ISSN=2296-598X

ABSTRACT=This paper presents a power system fault prediction method based on a GA-CNN-BiGRU model, which combines a genetic algorithm (GA), convolutional neural network (CNN), and bi-directional gated recurrent unit network (BiGRU) to predict and analyze power system faults accurately. The model uses a genetic algorithm for structural search and parameter tuning and optimizes the model structure, a convolutional neural network for feature extraction, and then a bi-directional gated recurrent unit network for sequence modeling, which can better capture the correlations and dependencies in time series data and effectively improve the prediction accuracy and generalization ability of the model. In the experimental validation, the prediction accuracy and generalization ability of the method on multiple data sets are better than those of traditional methods and other deep learning-based methods, so the method proposed in this paper can effectively predict and analyze power system faults and provide important support and help for the operation and management of power systems.