会议论文详细信息
2018 3rd International Conference on Reliability Engineering | |
Research on Condition Monitoring of Key Components in wind Turbine based on Cointegration Analysis | |
工业技术(总论) | |
Sun, Xide^1 ; Xue, Dong^2 ; Li, Runfei^3 ; Li, Xiaogang^1 ; Cui, Liye^1 ; Zhang, Xishan^4 ; Wu, Wei^4 | |
Jilin Electric Power Technology Development Branch Co., Ltd, Jilin | |
130015, China^1 | |
School of Automation Engineering, Northeast Electric Power University, Jilin | |
132012, China^2 | |
Hebei Jinglong Sun Equipment Co. Ltd, Hebei | |
055550, China^3 | |
Jilin CPI New Energy Co., Ltd., Jilin | |
130012, China^4 | |
关键词: Co-integration tests; Cointegration analysis; Novel methodology; On condition monitoring; Operating condition; Optimal combination; Temperature trends; Traditional models; | |
Others : https://iopscience.iop.org/article/10.1088/1757-899X/575/1/012015/pdf DOI : 10.1088/1757-899X/575/1/012015 |
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学科分类:工业工程学 | |
来源: IOP | |
【 摘 要 】
Traditional models based on temperature trend can not comprehensively monitor the condition of wind turbine, besides, the threshold obtained from model can cause fluctuation when wind turbine operates under different conditions. In view of this, this paper presents a novel methodology based on cointegration analysis for continuously monitoring the operating conditions of wind turbine. The first step is to determine the optimal combination of parameters from normal SCADA sample data, by using the cointegration test. Then, perform the method of cointegration analysis to calculate the cointegration residuals and stationary threshold line under normal working space. When wind turbine component is operating out of the normal track, the data sample in cointegration residuals will exceed the stationary threshold line, and send the alert signals. The method is tested using SCADA data of wind turbine with known faults, the results demonstrate the proposed method can effectively monitor the abnormal state of generator and gear box, and provide the function of early warning.【 预 览 】
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Research on Condition Monitoring of Key Components in wind Turbine based on Cointegration Analysis | 1056KB | download |