Power System Fault Detection and Classification Using Deep Neural Network=========================================================This video focuses on the d...

A powerline fault detection system invented at RMIT is being rolled out globally thanks to  $50 million in new funding.

Process monitoring is considered to be one of the most important problems in process systems engineering, which can be benefited significantly from deep learning techniques. In this paper, deep neural networks are applied to the problem of fault detection and classification to illustrate their capability. First, the fault detection and classification problems are formulated as neural network-based classification problems. Then, neural networks are trained to perform fault detection, and the effects of two hyperparameters (number of hidden layers and number of neurons in the last hidden layer) and data augmentation on the performance of neural networks are examined. The fault classification problem is also tackled using neural networks with data augmentation. Finally, the results obtained from deep neural networks are compared with other data-driven methods to illustrate the advantages of deep neural networks.Fault detection Accuracy : 100 %; Fault Classification Accuracy : 90.1 %

Fault Detection & Classification By Support Vector Machine | Fault Detection and Classification |SVM=========================================================...

Neural network-based fault detection, location and classification in microgridThis video explains neural network-based fault classification and location iden...

A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power module at randomly varied load currents and augmented image2 generated through image processing techniques such as resizing, rotation, perspective transformation, and bright and contrast adjustment. Since the autoencoder is trained to output images identical to input only for normal samples, it reconstructs images similar to normal ones even when the input images containing faults. A small heater is attached to the circuit board to simulate a fault on a power module, and then thermal images were captured from different angles and positions, as well as various load currents to test the trained autoencoder model. The areas under the curve (AUC) were obtained to evaluate the proposed method. The results show the autoencoder model can detect anomalies with 100% accuracy under given conditions.

High Impedance fault detection classification using Neural Network in MATLAB============================================================You can download a MA...

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.