Intelligent wind farm state mixed sensing and intelligent warning system

Dantian Zhong; Yijin Huang; Jinhe Tian; Shihai Ma

doi:10.1117/12.3000987

9 August 2023 Intelligent wind farm state mixed sensing and intelligent warning system

Dantian Zhong, Yijin Huang, Jinhe Tian, Shihai Ma

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127821D (2023) https://doi.org/10.1117/12.3000987
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

With the vigorous development of artificial intelligence and related technologies, domestic power generation enterprises have also promoted smart wind power projects. In this paper, a wind turbine condition monitoring system based on adaptive neuro fuzzy interference system (ANFIS) is proposed for the state perception of wind farms. A normal behavior model of ANFIS is established based on common monitoring and data acquisition (SCADA) data to detect abnormal behavior of captured signals and to indicate component failure or malfunction using predictive errors. At the same time, according to the theory of wind farm accident warning, this paper adopts the NJW spectral clustering method for the first time, and implements the group classification of wind field fans. Then, the Elman neural network model is adopted for any unit in a certain group, so as to determine the working conditions of all units in a certain group. This method can effectively improve the efficiency of wind farm accident early warning, and is of great significance for the development of intelligent wind field.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dantian Zhong, Yijin Huang, Jinhe Tian, and Shihai Ma "Intelligent wind farm state mixed sensing and intelligent warning system", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127821D (9 August 2023); https://doi.org/10.1117/12.3000987

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