A fault diagnosis method of double-layer LSTM for 10 kV single-core cable based on multiple observable electrical quantities

At present, research of cable fault diagnosis based on artificial intelligence mainly takes statistical characteristics as inputs, which means the appropriateness of statistical characteristic selection is directly related to the diagnosis accuracy and the identification results may have certain contingency. Further, most of these methods do not consider the correlation of signals in time. Therefore, this paper proposes a novel diagnosis method for 10 kV single-core cable based on Double-Layer Long Short Term Memory (D-LSTM) network considering timing relationship of multiple observable electrical quantities. Firstly, analysis object is expanded from single electrical quantity to multiple observable electrical quantities, and the relationships among these quantities are analyzed. Secondly, characteristic matrix of combined time series is constructed by time series pairs extracted from multiple observable electrical quantities. Thirdly, the D-LSTM network for processing sequenced input is established according to the features of characteristic matrix. Then, adaptive moment estimation (Adam) method is applied to model training under supervised learning and the model of fault diagnosis is obtained. Finally, recognition experiments are carried out by the proposed method with sample data obtained by simulation of three cable faults and load disturbance. Results show the diagnosis accuracy of proposed method can achieve 99.06%.