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Intelligent diagnosis method for typical transmission faults of wind turbines based on neural network

A technology of wind power generating set and neural network, applied in the field of wind power generation

Active Publication Date: 2017-06-06
北京能高普康测控技术有限公司
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0012] In view of the fact that the manual fault diagnosis and identification method is adopted in the existing wind turbine status monitoring and fault diagnosis technology, it is difficult to realize efficient fault diagnosis and identification for a large number of distributed wind turbines in the wind farm one by one, and there is an urgent need to realize the technical requirements of automatic fault identification. An intelligent automatic identification method for typical transmission faults in wind power generation

Method used

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  • Intelligent diagnosis method for typical transmission faults of wind turbines based on neural network
  • Intelligent diagnosis method for typical transmission faults of wind turbines based on neural network
  • Intelligent diagnosis method for typical transmission faults of wind turbines based on neural network

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Embodiment Construction

[0032] like figure 1 Shown is a schematic flow chart of the intelligent diagnosis method for typical transmission faults of wind turbines based on neural networks. This method can be implemented on but not limited to platforms such as industrial computer platforms, PLC platforms, and embedded system platforms.

[0033] The intelligent diagnosis method of typical transmission faults of wind power generators based on neural network can be realized through the local fault diagnosis system of distributed units, or through the upper monitoring system located in the central control room to perform centralized fault diagnosis for all wind turbines in the wind farm.

[0034] This method mainly includes the following aspects:

[0035] 1: Confirm the corresponding variable of the monitoring information, monitor the relevant components of the wind turbine to obtain the monitoring information, and classify the monitoring information according to the monitoring variable or filter the corre...

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Abstract

The invention relates to a typical transmission fault intelligent diagnosis method based on a neural network wind generating set. According to the method, state monitoring information of a transmission system of the wind generating set is subjected to data processing and characteristic extraction, on the basis of the neural network clustering technology, intelligent fault diagnosis and recognition are achieved according to fault typical characteristic information, the fault severity degree is subjected to quantitative evaluation, faults of the transmission system of the wind generating set can be automatically diagnosed effectively, and the operation maintenance efficiency of a wind power plant is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and relates to a neural network-based intelligent diagnosis method for typical transmission faults of wind power generating sets. Background technique [0002] The unit power generation cost of wind power is basically close to the cost of thermal power generation, and it is currently the most commercially valuable and most popular energy type among all renewable energy sources; [0003] Wind power is a kind of clean energy, which has important economic and social benefits for energy conservation and environmental protection; [0004] Wind power generation is the key direction of the national energy field's medium and long-term planning. my country's existing total installed capacity has reached 74 million kilowatts, accounting for 7% of the total installed capacity of the power grid. At the end of the 13th Five-Year Plan, the total installed capacity of wind power will reach 150-200...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/02
Inventor 贾利民刘展庞宇雷涛童亦斌
Owner 北京能高普康测控技术有限公司