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Proceedings Paper

A novel neural network approach for error transfer analysis in electronic instrument transformer
Author(s): Xiaofei Li; Rui Pan; Xiang Peng; Zhou Feng; Nie Qi; Haoliang Hu; Junchang Huang
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Paper 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.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143022 (14 February 2020); doi: 10.1117/12.2541931
Show Author Affiliations
Xiaofei Li, China Electric Power Research Institute (China)
Rui Pan, China Electric Power Research Institute (China)
Xiang Peng, China Electric Power Research Institute (China)
Zhou Feng, China Electric Power Research Institute (China)
Nie Qi, China Electric Power Research Institute (China)
Haoliang Hu, China Electric Power Research Institute (China)
Junchang Huang, China Electric Power Research Institute (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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