Mr. Junchang Huang Profile

Junchang Huang

at China Electric Power Research Institute

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Proceedings Article | 14 February 2020 Paper

A novel neural network approach for error transfer analysis in electronic instrument transformer

Xiaofei Li, Rui Pan, Xiang Peng, Zhou Feng, Nie Qi, Haoliang Hu, Junchang Huang

Proceedings Volume 11430, 1143022 (2020) https://doi.org/10.1117/12.2541931

KEYWORDS: Data modeling, Neural networks, Principal component analysis, Error analysis, Data mining, Transformers, Data processing, Statistical analysis, Performance modeling, Binary data

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With the continuous updating of communication network technology and the influence of different factors (such as humidity, specific gravity, temperature, etc.), the monitoring data acquired by the grid equipment is exponentially increasing and the complexity of the data is also continuously improving. Taking full advantages of these big data, studying the measurement characteristics of electronic transformers in operation and discovering the relationship of environment, load and other factors will help optimize the performance of electronic transformers, give users a better experience and improve the benefits of the companies. However, the emergence of massive data makes traditional data analysis methods unable to meet the accuracy and real-time performance of data processing. Therefore, how to effectively and accurately solve the big data analysis and processing problems is particularly urgent. To effectively process this data, we have chosen the popular data mining method. Compared to traditional machine learning, we choose a relatively simple deep learning network for data mining. A feed forward neural network is used for classification. On the basis of classification, a new network is established to perform nonlinear regression prediction on the data, then an error transfer model is established. In the regression prediction problem, due to the high dimensionality and high computational complexity of the original data, we use the PCA method to reduce the feature dimension, which is also helpful to establish a nonlinear relationship between the learning characteristics of the deep neural network and the predicted values. Compared with the traditional feed forward neural network, the accuracy of our network has been significantly improved.

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