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Online Operation Risk Assessment of the Wind Power System of the Convolution Neural Network (CNN) Considering Multiple Random Factors

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
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Processes 2019, 7(7), 464; https://doi.org/10.3390/pr7070464
Received: 15 June 2019 / Revised: 15 July 2019 / Accepted: 16 July 2019 / Published: 19 July 2019
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)
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Abstract

In order to solve the problem of the inaccuracy of the traditional online operation risk assessment model based on a physical mechanism and the inability to adapt to the actual operation of massive online operation monitoring data, this paper proposes an online operation risk assessment of the wind power system of the convolution neural network (CNN) considering multiple random factors. This paper analyzes multiple random factors of the wind power system, including uncertain wind power output, load fluctuations, frequent changes in operation patterns, and the electrical equipment failure rate, and generates the sample data based on multi-random factors. It uses the CNN algorithm network, offline training to obtain the risk assessment model, and online application to obtain the real-time online operation risk state of the wind power system. Finally, the online operation risk assessment model is verified by simulation using the standard network of 39 nodes of 10 machines New England system. The results prove that the risk assessment model presented in this paper is more rapid and suitable for online application. View Full-Text
Keywords: online operation risk assessment; uncertain wind power output; load fluctuations; operation pattern; equipment failure rate; CNN online operation risk assessment; uncertain wind power output; load fluctuations; operation pattern; equipment failure rate; CNN
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Gong, Q.; Tan, S.; Wang, Y.; Liu, D.; Qiao, H.; Wu, L. Online Operation Risk Assessment of the Wind Power System of the Convolution Neural Network (CNN) Considering Multiple Random Factors. Processes 2019, 7, 464.

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