Neural network-based fault detection, location, and classification in microgrid
This video explains neural network-based fault classification and location identification methods for microgrids. The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids. Then, the extracted features are normalized and fed to the neural network as an input. The neural network, which consists of multiple hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.