Research on Substation Monitoring and Fault Diagnosis Based on Distributed Computing and Artificial Neural Network

Abstract

In the Energy Conversion for Next-Generation Smart Cities, intelligent substation plays an important role in the power conversion. As an important guarantee for the stable operation of intelligent substation, the research on fault diagnosis technology is particularly important. In this paper, the acoustic characteristic diagnosis of substation equipment (take transformers for example) is researched and the application of “Voice Recognition + artificial neural network (ANN)” technology in substation fault diagnosis is analyzed. At the same time, the continuous online monitoring of the intelligent substation equipment will produce a large amount of monitoring data, which needs to be analyzed timely and effectively to understand the operating status of the equipment accurately. Because of this, this paper adopts distributed computing by establishing a real-time distributed computing platform, using open source technology to store the online monitoring of sound data into the computing platform for data processing to achieve the purpose of automatic fault detection and analysis. The results show that distributed computing can realize the intelligent analysis, storage, and visualization of equipment data in the substation, which provides data support for fault diagnosis. Besides, the fitting accuracy rates of ANN model are 95.123% for training process and the fitting accuracy rates of ANN model are 99.353% for training process and the overall fitting accuracy rates of ANN model are 95.478% and the error between the predicted value and the actual value of the 5 sound signals is within 5% in the fault diagnosis process. Consequently, the ANN model can accurately identify each fault sound of substation and achieve the purpose of fault diagnosis.