Real-time evaluation method for health state of wind turbine unit

Abstract

The invention discloses a real-time evaluation method for the health state of a wind turbine unit. The method comprises the steps that firstly, on the basis of historical running data of the wind turbine unit, running work condition dividing of the wind turbine unit is achieved by applying a clustering technology, and standard state cloud models of the wind turbine unit under all work conditions are calculated; secondly, work condition identification is conducted on real-time data stream of the wind turbine unit through a stream-type clustering algorithm, and cloud models of the real-time states of the unit are calculated; thirdly, deviation values between the cloud models of the real-time states and the standard state cloud models are calculated and taken as health indexes of the wind turbine unit; lastly, the health state of the wind turbine unit is evaluated according to the magnitude of the health indexes. According to the method, the running states of the wind turbine unit are described through the cloud models, the health state and the development tendency of the wind turbine unit are acquired by introducing a time window method, therefore, the uncertainty of state monitoring information of the wind turbine unit is fully taken into account, the accuracy of an evaluation result is greatly improved, and powerful support can be provided for making a wind turbine unit maintenance plan.

Description

technical field [0001] The invention relates to a method for real-time evaluation of the health state of a wind turbine unit in consideration of information uncertainty, and belongs to the technical field of power generation. Background technique [0002] With the advent of the era of power big data, a large number of high-speed real-time data streams have become more and more common, big data analysis technology (such as distributed computing technology, memory computing technology and stream processing technology) provides a more stable development of the power industry , Powerful data analysis capabilities. The current research in the field of data flow analysis mainly focuses on the association analysis, cluster analysis, classification and frequent item mining of data flow. Effective dynamic data streams provide rich status information and decision support information for health status assessment of monitored objects, but unlike traditional analysis methods, analysis m...

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Problems solved by technology

[0004] In short, the shortcomings of existing methods are mainly manifested in two aspects: (1) In the environment of continuous real-time data flow, it is difficult to implement health status assessment, resulting in low accuracy of assessment; (2) Failure to fully consider information Effect of Uncertainty on Equipment Health Assessment

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