Machine Learning and Deep Learning Techniques for Fault Location in Transmission Lines

Fault location in a power system is a sensitive task because of the grid’s complex environment. Traditional fault-location methods that rely only on impedance estimates or traveling waves are less accurate than before because modern transmission networks are more complex due to the integration of wind resources, power electronic interfaces, and changing operating conditions. This study provides an artificial intelligence-based comparative framework for transmission line fault locating based on models such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), convolutional neural network (CNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (BiLSTM). The suggested configuration is tested on both the IEEE 9-bus test system and the IEEE 30-bus test system to explore the impact of the size and complexity of the network on model performance. The test systems are simulated in MATLAB (Version R2024a)/Simulink for various operating conditions, fault locations, and fault types. The faults are measured by three-phase voltage and three-phase current at one end of the transmission line, and the distance to the fault is used as the regression variable for the modeling. The mean square error, root mean square error, mean absolute error, coefficient of determination, as well as training and testing performance in modifying the models, are used for assessing model performance. ANFIS has yielded the best results for the verified fault location of the IEEE 9-bus system, with RMSE, MAE, and R2 equal to 1.880 km, 1.142 km, and 0.996, respectively, and better than the traditional ANN model and other deep learning models. The deep learning technique that performed best on the larger system, the 30-bus IEEE system, was BiLSTM with a testing RMSE of 2.909 km, an MAE of 1.841 km, and an R2 value of 0.998. The results showed that ANFIS shows good performance in the small category of the system due to its nonlinear rule-based learning, while, on the other hand, BiLSTM has good performance in larger-scale and complex systems owing to its learning capability in a bidirectional manner in voltage and current signals. The comparative analysis presented in this work reveals that intelligent regression models can estimate single-ended fault location, are robust, and have great potential for use in real-time transmission line protection systems.