A new intelligent scheme for power system faults detection and classification: A hybrid technique

This paper proposes a hybrid technique to detect and classify the transmission line faults in the power system. The proposed approach is the joint execution of linear discriminant analysis (LDA) and cuttlefish optimizer (CFO) learning process-based random forest algorithm (RFA), ie, named as LDA-CFRFA technique. Here, two modules are utilized for fault analysis in power system: fault detection and fault classification. The first procedure of the proposed method is the power system transmission line parameters in normal and abnormal condition dataset preparation by using LDA. LDA-based dataset preparation process consists of feature extraction of power flow parameters and defines the nature of signals occurred by the system. The extracted dataset is assessed by CFO-based RFA technique for classifying the fault type occurred in the transmission system. By then, the proposed model is executed in MATLAB/Simulink working stage, and the execution is assessed with the existing techniques such as CFO, RFA, Feed forward neural network (FNN), and artificial neural network (ANN). In our research, the faults implemented on test system are phase A, phase B, phase C, phase A to ground (AG), phase B to ground (BG), phase C to ground (CG), phase AB, phase AC and phase BC, phase AB to ground (ABG), phase AC to ground (ACG), and phase BC to ground (BCG). From the results, the proposed technique guarantees the system with less complexity and less consumption time for the detection and classification of the fault, and hence, the accuracy of the system is increased.