Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning

Abstract

Transformer is important to the electric power systems, and its accurate fault diagnosis is still hard. In the paper, a novel transformer fault diagnosis method using an Internet of Things (IoT) based monitoring system and an ensemble machine learning (EML) is presented. The monitoring system based on IoT technology consists of two parts: a data measurement subsystem and a data reception subsystem. Firstly, transformer vibration signals are measured by using the data measurement subsystem, and they are sent to the remote server by using the data reception subsystem. Then, an EML composed of deep belief networks (DBNs) and stacked denoising autoencoders (SDAs) with different activation functions, and relevance vector machines (RVMs) is proposed. DBNs and SDAs are respectively used to extract features from the signals, and RVMs are respectively employed as classifier. In order to ensure efficient of the EML, a novel combination strategy is proposed. A transformer fault diagnosis experiment is performed, and the diagnosis results confirm that the designed monitoring system can collect vibration signals effectively and long-distance, and the proposed EML can usefully remedy the inadequate information of features extracted by individual deep learning method, and its RVM classifier is obviously better than other commonly used classifiers.