Fault Diagnosis Of Power Grid Based On Deep Learning

The development of modern social productivity demands better reliability of grid power supply.How to accurately determine the grid fault component when the grid system fails is a major challenge for grid system fault diagnosis.However,the traditional power grid fault diagnosis method still has the following defects:(1)The power structure information is not fully utilized,resulting in poor portability of the diagnostic method;(2)The input characteristics depend on artificial experience and with low degree of abstraction,which leads to poor robustness of the diagnostic method;(3)The complex logic of the model and the multiclassification diagnosis model make it difficult to solve the multi-fault diagnosis problem of power grid.In view of the above problems,this paper studies the power grid fault diagnosis technology based on deep learning.The main works are as follows:Aiming at the poor portability and poor robustness of traditional power grid fault diagnosis methods,this paper designs an adaptive feature extraction method of power grid.The grid structure is transformed into a special undirected graph structure by adopting a novel knowledge representation of the grid structure.On this basis,the grid structure and fault alarm information are used efficiently,and the iterative update method is adopted to design a Deep Iterative Network(DIN)which is suitable for grid structure,which is used to extract structural abstract features and enhance portability and robustness of fault diagnosis method.Traditional power grid fault diagnosis method is difficult to effectively solve the problem of power grid multi-fault diagnosis.Based on the feature extracted by deep iterative network,this paper designs a power grid fault diagnosis model based on supervised learning(AFD_SL)and a power grid fault diagnosis model based on reinforcement learning(AFD_SL).AFD_SL uses one-by-one diagnosis to design the model,which is suitable for power grid fault diagnosis scenarios with sufficient labeled data;AFD_RL transforms the fault diagnosis problem into a sequential decision-making process,which is suitable for power grid fault diagnosis scenarios without labeled data.The above two models can be used in different scenarios to solve the problem of power grid fault diagnosis efficiently.On the high-performance computing platform of the laboratory,with the experimental data generated based on the IEEE standard grid structure,the power grid fault diagnosis model designed by this paper is tested and analyzed,and compared with the traditional grid fault diagnosis method.The experimental results show that the power grid fault diagnosis methods designed in this paper have good model portability and robustness while dealing with multifault diagnosis problems.