Random Forest and Data Mining-Based Fault Identification Method for Transmission Lines

Transmission lines are the main lifeline of power grids. Under the background of continuous construction of China's new power system, accurate fault location and fault type identification of lines are of great significance for line inspection and fault handling. This paper proposes a transmission line fault identification method based on random forest. Firstly, wavelet transform is used to preprocess the line fault data and extract 24 sets of characteristic quantities that can reflect fault types. Then, the random forest algorithm is employed to conduct deep mining on historical data to complete model training. The accuracy of the training set and test set reaches approximately 83.3 %, indicating that the proposed method can achieve efficient and precise analysis of power grid faults. It effectively solves the complex and high-dimensional analysis challenges of power grid fault data, providing solid technical support for line protection and stable operation of power grids.