Wind turbine generator gearbox fault monitoring method and system based on deep neural network

A deep neural network and wind turbine technology, applied in the field of wind turbine gearbox fault monitoring based on deep neural network, can solve the problems affecting the monitoring results, the influence of the surrounding environment and noise, etc., and achieve sensitive and high accuracy of process monitoring Effect

Inactive Publication Date: 2017-09-12
LONGYUAN BEIJING WIND POWER ENG TECH
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Problems solved by technology

The oil temperature of the gearbox lubricating oil is the main monitoring index of the gearbox during monitoring, but the ...

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  • Wind turbine generator gearbox fault monitoring method and system based on deep neural network
  • Wind turbine generator gearbox fault monitoring method and system based on deep neural network
  • Wind turbine generator gearbox fault monitoring method and system based on deep neural network

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Embodiment Construction

[0023] The present invention provides a wind turbine gearbox fault monitoring method and system based on a deep neural network. The method and system can monitor gearbox faults in a timely and effective manner, and the monitoring results are less affected by the external environment and have high accuracy.

[0024] see figure 1 As shown, the gear box fault monitoring method of the present invention mainly includes the following steps: obtaining the relevant data of the gear box state in the SCADA real-time data of the wind turbine, including the gearbox lubricating oil pressure, the gearbox lubricating oil temperature, the gearbox bearing temperature and the unit Output power; input the gearbox lubricating oil temperature, gearbox bearing temperature and unit output power into the prediction model based on the deep neural network to obtain the predicted value of the gearbox lubricating oil pressure output by the prediction model; calculate the gearbox lubricating oil pressure ...

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Abstract

The invention discloses a wind turbine generator gearbox fault monitoring method and system based on a deep neural network. The method comprises the following steps: A. acquiring gearbox state-related data in wind turbine generator SCADA real-time data, including gearbox lubricating oil pressure, gearbox lubricating oil temperature, gearbox bearing temperature and unit output power; B, inputting the gearbox lubricating oil temperature, the gearbox bearing temperature and the unit output power into a prediction model based on the deep neural network, and acquiring a prediction value of the gearbox lubricating oil pressure outputted by the prediction model; C, calculating a fitting error between an actual value of the gearbox lubricating oil pressure and a predicted value of the gearbox lubricating oil pressure; and D, monitoring the fitting error so as to determine a gearbox state of a wind turbine generator, and triggering the alarm when the gearbox state of the wind turbine generator is determined as a fault state. By adopting the gearbox fault monitoring method and system, a gearbox fault can be effectively monitored in real time, a monitoring result is less influenced by an external environment, and the accuracy is high.

Description

technical field [0001] The invention relates to the technical field of wind turbine fault early warning technology, in particular to a fault monitoring method and system for a wind turbine gearbox based on a deep neural network. Background technique [0002] As the service life of wind farms increases, the cost control of wind farm operation and maintenance becomes more important. Several main subsystems of wind turbines, such as gearboxes, generators, bearings, etc., are the main application objects of condition monitoring and fault warning. With early warning of faults, wind farm operators can effectively adjust the operation mode and perform equipment maintenance and replacement in advance, which can significantly reduce operating costs. For gearboxes, which account for most of the cost share of wind turbines, failures will cause excessive downtime, so the development of accurate and efficient gearbox fault detection models is essential. [0003] The traditional method ...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06N3/08
Inventor 刘瑞华胥佳李韶武朱孟喆
Owner LONGYUAN BEIJING WIND POWER ENG TECH
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