Wind power intelligent fault diagnosis method based on big data

A technology of fault diagnosis and big data, applied in wind turbines, monitoring of wind turbines, instruments, etc., can solve problems such as long diagnosis time, weak anti-interference ability, high manual experience requirements, etc., to achieve rapid acquisition and strong anti-interference ability Effect

Inactive Publication Date: 2020-08-11
SHANGHAI ELECTRIC WIND POWER GRP CO LTD
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Problems solved by technology

[0004] However, the existing fault diagnosis methods all have deficiencies: the wavelet analysis method needs to use the vibration monitoring data of wind power, cannot give the diagnosis result in real time, and requires a long time for diagnosis; the neural n

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  • Wind power intelligent fault diagnosis method based on big data
  • Wind power intelligent fault diagnosis method based on big data
  • Wind power intelligent fault diagnosis method based on big data

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Embodiment Construction

[0031] A kind of wind power intelligent fault diagnosis method based on big data of the present invention is described in detail below in conjunction with accompanying drawing:

[0032] Step 1, collection of fault list and corresponding data files and data arrangement:

[0033] Step 1a, based on the fault list, perform main fault marking and fault chain vector sorting.

[0034] Collected through the Scada system such as figure 2 The list of faults shown. Since the alarm of the main fault is often associated with a large number of other non-main fault alarms, experienced engineers are required to mark the main fault according to manual experience. figure 2 The framed part in , that is, the real main fault is marked out of a large number of faults generated in a similar time.

[0035] All fault codes generated in a similar time period are used to form a fault chain as a training sample for the fault sorting algorithm model. In order to facilitate calculation and storage, a...

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Abstract

The invention discloses a wind power intelligent fault diagnosis method based on big data. The wind power intelligent fault diagnosis method comprises the steps of: subjecting a fault list and data files corresponding to the fault list to collection and data arrangement; establishing a main fault sorting model based on a Bayesian network; establishing a main fault positioning model based on a deeplearning algorithm; and performing real-time fault diagnosis and positioning based on the main fault sorting model and the main fault positioning model. According to the wind power intelligent faultdiagnosis method, the Bayesian network algorithm and the deep learning algorithm are combined, the problem of manual feature screening is avoided, the fault diagnosis result can be quickly obtained based on a fault sorting result and a fault positioning result, and the wind power intelligent fault diagnosis method has high anti-interference capability.

Description

technical field [0001] The invention relates to the technical field of wind power, in particular to an intelligent fault diagnosis method for wind power based on big data. Background technique [0002] Wind power plants are generally built in remote areas, and many major equipment are located in the nacelle, at a height of more than 80 meters from the ground, which increases the difficulty of wind power troubleshooting. When an actual fault occurs, the alarm of the main fault will be accompanied by a large number of other non-main fault alarms. In actual production, determining the main fault in the entire fault alarm sequence is the most urgent and basic requirement. Building a wind power fault diagnosis model can quickly and accurately locate major faults, reduce operation and maintenance costs, and improve operation and maintenance efficiency. [0003] At present, the methods of fault diagnosis mainly include: (1) Wavelet analysis method. As a brand-new time-frequency ...

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Application Information

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IPC IPC(8): G06K9/62F03D17/00
CPCF03D17/00G06F18/24155G06F18/214
Inventor 丁丹玫黄猛吴祎黄永民刘永鹏王娜张志俊成骁彬
Owner SHANGHAI ELECTRIC WIND POWER GRP CO LTD
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