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

doi:10.1117/12.2541931

14 February 2020 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, MIPPR 2019: Pattern Recognition and Computer Vision; 1143022 (2020) https://doi.org/10.1117/12.2541931
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

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.

Citation Download Citation

Xiaofei Li, Rui Pan, Xiang Peng, Zhou Feng, Nie Qi, Haoliang Hu, and Junchang Huang "A novel neural network approach for error transfer analysis in electronic instrument transformer", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143022 (14 February 2020); https://doi.org/10.1117/12.2541931

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KEYWORDS

Data modeling

Neural networks

Principal component analysis

Data mining

Error analysis

Transformers

Data processing

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