Wind turbine generator set mechanical equipment state diagnosis method based on multivariate statistical analysis

Abstract

The invention discloses a wind turbine generator set mechanical equipment state diagnosis method based on multivariate statistical analysis. The method is characterized by utilizing a sensor to collect state information generated by wind power equipment; performing feature extraction, signal analysis and state identification on the state information based on the multivariate statistical analysis; with low-dimension principal component feature expression technology expressing and classifying wind turbine generator set mechanical state, establishing an average correlation law to assess the ability, for describing the wind turbine generator set mechanical state, of each principal component; and selecting low-dimension principal component feature to express the comprehensiveness for the wind power equipment state features and the diagnosis of the wind power equipment state is realized. According to the wind turbine generator set mechanical equipment state diagnosis method based on the multivariate statistical analysis, early failure of the wind power equipment can be found and failure conditions can be accurately judged, utilization rate of the wind turbine generator set is improved, and cycle period and financial costs of maintenance and service are reduced as possible as one could; and the method can ensure safe, stable and reliable operation of the wind turbine generator set, and has great acceleration effect.

Description

technical field [0001] The invention belongs to a state fault diagnosis method of wind power equipment, in particular to a state diagnosis method of wind power set mechanical equipment based on multivariate statistical analysis. Background technique [0002] Wind energy is a clean and renewable energy that is developing rapidly around the world. According to the statistics: In 2010, China's new installed capacity was 18,927.99MW, a year-on-year increase of 73.3%, and the cumulative installed capacity reached 44,733.29MW, ranking first in the world in both indicators. In 2011, China's new wind turbine installed capacity was 20665MW (20.665 million kilowatts), an increase of 9.18% compared with 2010, and continued to rank first in the world. Since wind farms are generally located in harsh environments and the working conditions are extremely unstable, online monitoring and fault diagnosis of the wind turbine operating status are carried out to understand the operating status ...

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Problems solved by technology

[0005] Vibration signals in engineering applications are the coupling of various vibration signals. Vibration signal analysis is used for fault diagnosis. There are many types of information and a wide range of values. The state regularity, sensitivity, and clustering and separability in the mode space reflected by them are not the same, and there will be some changes with different operating conditions

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