Fault Diagnosis and Operation and Maintenance Matching of PMSG for Wind Power Generation

2019 ◽  
Author(s):  
Changjie Liu ◽  
Bin Duan ◽  
Tao Li
Keyword(s):  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Power ◽  
Wind Power Generation ◽  
Operation And Maintenance

Application of Multiple Level Rules Set in Fault Diagnosis of the Wind Power Generation System

2012 ◽  
Vol 512-515 ◽  
pp. 679-685
Author(s):  
Gui Mei Gu
Keyword(s):  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Power ◽  
Comprehensive Evaluation ◽  
Wind Power Generation ◽  
Decision Table ◽  
Multiple Level ◽  
Generation System ◽  
Power Generation System ◽  
Rules Set

For the incompletion problem of sensors’ collected data in fault diagnosis of the wind power system, this article puts forward a kind of multiple level rules set based on rough set. First, let the sensors’ collected data go through Fourier transform and extract its feature attributes as well as discrete them. Establish the decision table of fault diagnosis according to attribute values. Then set out from the decision table to establish a multiple level set of nodes with diverse reduced levels and deduce the rules of each node, which has a corresponding belief level. When in reasoning and decision-making of the new data using the multiple level rules set, match the information of the new data with the rule of its corresponding node. Finally, achieve the fault diagnosis of wind power generation system by choosing comprehensive evaluation algorithm. The result of the diagnosis example shows the reliability and accuracy of this method in the diagnosis of fault types for wind power generation system.

Statistical Method for Rotating Machine Fault Diagnosis

2011 ◽  
Vol 383-390 ◽  
pp. 1406-1410
Author(s):  
Zhe Min Zhuang ◽  
Fen Lan Li
Keyword(s):  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Power ◽  
Time Domain Analysis ◽  
Wind Power Generation ◽  
Multivariate Statistic ◽  
Vibration Signals ◽  
Time Frequency ◽  
Rotating Machine ◽  
Squared Prediction Error

In this paper, a time-domain analysis method based on multivariate statistic is presented for wind power generation fault diagnosis. Generally, the sound and vibration signals obtained from wind power generation are time-variant since they are strongly related to the rotational speed which is not constant even in the macro steady state. Since the mostly used signal processing method, the Fourier analysis, is only suitable for stationary signals, the development of the joint time-frequency analysis is demanded. Here, Q statistic (also referred as squared prediction error, SPE) is introduced, it is used to monitor the vibration signals and three-phase currents. The control limit of the Q statistics is calculated to decide the state of the rotating machine, and the contribution plot of SPE is used to find the fault source. The method can efficiently detect faint change and the validity of the method is proved by experiments.

Novel Synchronous Generator for Wind Power Generation

2014 ◽  
Vol 2 ◽  
pp. 170-173
Author(s):  
Tsuyoshi Higuchi ◽  
Yuichi Yokoi
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Synchronous Generator ◽  
Wind Power Generation

Development of a City Type Compact Wind Power Generation System

2005 ◽  
Vol 125 (11) ◽  
pp. 1016-1021 ◽  
Author(s):  
Yoshihisa Sato ◽  
Naotsugu Yoshida ◽  
Ryuichi Shimada
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Power Generation ◽  
Generation System ◽  
Power Generation System
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