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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.

Multicast plays an important role in ad hoc networks, and multicast algorithms have the goal of directing traffic from sources to receivers maximizing some measure of network performance combining the processes of routing and resource reservation. This paper discusses some current literatures about multicast routing in mobile ad hoc networks. Further, by inves tigating the swarm-based routing method and the multiagent reinforcement learning applications, this paper analyses the possibility and merit of adopting reinforce ment learning nietliod in multicast routing protocol for wireless ad hoc networks. And based on the above, this paper presents a novel multicast routing method, the Q-MAP algorithm, that ensures the reliability of the resource reservation in the wireless mobile ad hoc networks. The features and efficiency of the Q-MAP multicast routing method are also illustrated in this paper.