Fault detection,classification and location for transmission lines and distribution systems:a review on the methods

A comprehensive review on the methods used for fault detection, classification and location in transmissionlines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors tryto discuss them separately, so that one may have a more logical and comprehensive understanding of the conceptswithout getting confused. Great significance is also attached to the feature extraction process, without which themajority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis offeature extraction. After the overall concepts and general ideas are presented, representative works as well as newprogress in the techniques are covered and discussed in detail. One may find the content of this study helpful as adetailed literature review or a practical technical guidance.

Learning-based Data Analytics:Moving Towards Transparent Power Grids

this paper,we present the learning-based data analytics moving towards transparent power grids and provide some possible extensions including machine learning,big data analytics,and knowledge transferring.The closed loops of data and knowledge are illustrated and the challenges for establishing the closed loops are discussed.General ideas and recent developments in supervised learning,unsupervised learning,and reinforcement learning are presented together with extensions for power system applications.Furthermore,much emphasis is placed on privacypreserving data analysis,transfer of knowledge,machine learning for causal inference,scalability and flexibility of data analytics,and efficiency and reliability of computation.Existing integrated solutions in the industry featuring the Industrial Internet and the digital grid are also introduced.

Method of inter-turn fault detection for nextgeneration smart transformers based on deep learning algorithm

In this study,an inter-turn fault diagnosis method is proposed based on deep learning algorithm.12-channel data is obtained in MATLAB/Simulink as the time-domain monitoring signals and labelled with 16 different fault tags,including both primary and secondary voltage and current waveforms.An auto-encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms.The overall waveforms compose a two-dimension data matrix and the auto-encoder is trained to extract the features in the multi-channel waveforms.The selected features are convoluted with the original data,generating a one-dimensional vector as the input to the softmax classifier.Variables such as type,activation function and depth of auto-encoder,sparsity of sparse auto-encoder,number of features and pooling strategies are studied,which gives an intuitive process to train a proper learning model.The overall recognition accuracy reaches 99.5%.Signal characteristics such as channel selection,time span of the input signal and signal sampling frequency are studied to find the best solution for the interturn fault detection of the three-phase transformer.The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis,indicating its potential and prospect in the next-generation smart transformers.