AI in Power Maintenance

Machine learning or deep learning methods, e.g. deep neural networks (DNNs), support vector machine (SVM), random forest (RF), et al. can be used to solve classification or regression problems which exist in the intelligent maintenance of power system. Some new AI technology, e.g. federated learning (FL), can also be applied to assist power maintenance with obeying data privacy regulations and maximize the value of data.

Recognize towers of transmission lines using convolution neural networks (CNNs)

Squeeze-and-Excitation improved fast unsupervised anomaly detection with generative adversarial network (SE-f-AnoGAN) for power tower anomaly detection from UAV inpection images

Recognize towers of transmission in mountain areas

Federated learning (FL)-based visual classification and detection for processing power inspection images

Federated learning (FL)-based visual classification and detection

Fault diagnosis of on-load tap changers (OLTCs) based on improved semi-supervised ladder networks

Structure of three-layer Ladder Networks for processing vibration signals of OLTCs

Experiment result of Ladder Networks for fault diagnosis of OLTCs

Predict condition of gas-insulated switchgears (GIS) using support vector machine (SVM)

Predict openning travel curves of GIS


. Fault diagnosis method for OLTC based on improved semi-supervised ladder networks. Electric Power Engineering Technology, 2023.

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. Text Recognition of Power Equipment Nameplates Based on Deep Learning. Electric Power Engineering Technology, 2022.

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. Object detection of UAV power line inspection images based on federated learning. 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), 2022.

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. Dynamic Margin for Federated Learning with Imbalanced Data. 2021 International Joint Conference on Neural Networks (IJCNN), 2021.

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. Mechanical life prognosis of high voltage circuit breakers based on support vector machine. 11th International Conference on Natural Computation (ICNC) (Zhangjiajie, China), 2015.

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. Mechanical Life Prognosis of High Voltage Circuit Breaker Based on Support Vector Machine. High Voltage Apparatus (in Chinese), 2015.

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