Transformer Operating State Monitoring System Based on Wireless Sensor Networks

• Published in: IEEE sensors journal Vol. 21; no. 22; pp. 25098 - 25105
• Main Authors: Chen, Xiaoyu, Hu, Yongtao, Dong, Zuolin, Zheng, Pengfei, Wei, Jiahong
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Clustering; Correlation analysis; Data collection; Data transmission; Dissolved gases; Genetic algorithms; grey model; Grey prediction; Mathematical models; Monitoring; Multidimensional data; Multivariate analysis; Oil insulation; operation status monitoring; Parameters; Particle swarm optimization; Prediction algorithms; Prediction models; Predictive models; Sensors; Time series; Transformer operation; Transformers; Windows (intervals); Wireless sensor networks
• DOI: 10.1109/JSEN.2021.3050763
• ISSN: 1530-437X

Transformer operating state prediction is an important module of the transformer operating state maintenance system. Since the analysis of dissolved gas in oil is the prerequisite for realizing the analysis of transformer operating status, the key to predicting the transformer operating status is to predict the content of dissolved gas in oil. Before optimizing the parameters <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> of the improved grey prediction model GM(1,1,B), this article determines that the average relative error of the model fitting is the objective function. It is stipulated that the search space for the optimal solutions of <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> are both [0~1], and the hybrid algorithm of genetic algorithm and particle swarm optimization is used to optimize the model parameters <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula>. By analyzing the characteristics of different types of abnormal values in transformer online monitoring data such as oil chromatogram and oil temperature, a fast analysis and detection method of online monitoring data stream based on multivariate time series and correlation analysis is proposed. For multi-dimensional monitoring data, from the perspective of data association and time series analysis, a sliding time window is used to record the occurrence time and type of abnormal points, establish a judgment model for candidate abnormal data sets, and use clustering algorithms to analyze candidate abnormalities. The data collection performs comprehensive abnormality judgment of multi-dimensional data. Experiments show that this method can detect abnormal operating states in online monitoring data streams in real time, and has high application value.