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Intelligent monitoring and diagnosis method for fault state of wind turbine generator system

A technology for wind turbines and fault status, applied in the field of wind power, can solve problems such as protracted maintenance work, heavy losses, and inability to fully and timely understand equipment conditions.

Active Publication Date: 2019-03-08
HUNAN UNIV
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Problems solved by technology

Routine maintenance is carried out after planned maintenance runs for 2500h or 5000h, and it is impossible to fully and timely understand the equipment status; after-the-fact maintenance is a protracted maintenance work with heavy losses

Method used

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  • Intelligent monitoring and diagnosis method for fault state of wind turbine generator system
  • Intelligent monitoring and diagnosis method for fault state of wind turbine generator system
  • Intelligent monitoring and diagnosis method for fault state of wind turbine generator system

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Embodiment Construction

[0077] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0078] like figure 1 As shown, a fault state intelligent monitoring and diagnosis method of a wind turbine comprises the following steps:

[0079] Step 1: Establish a nonlinear model of the wind turbine using the partial least squares method. The specific steps are:

[0080] 1-1) Collect normal characteristic data such as wind turbine output power, hub speed, blade pitch angle, pitch motor current, generator current, generator torque, generator stator temperature, and main bearing temperature as historical data, record for x 1 ,x 2 ,...,x q ,y; where x q Indicates the qth feature parameter, x 1 ,x 2 ,...,x q Form the independent variable matrix x, y represents the output power of the wind turbine as the dependent variable;

[0081] 1-2) Since different data have different dimensions, the independent variable matrix x and the dependent variable...

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Abstract

The invention discloses an intelligent monitoring and diagnosis method for a fault state of a wind turbine generator system. The method comprises the following steps of establishing a nonlinear modelof the wind turbine generator system by means of a partial least square method; constructing a fault predicating model according to an extreme learning machine, chaotic mapping and a firefly algorithm; establishing a DBN-ELM fault diagnosis model through deep belief learning and the extreme learning machine; performing state monitoring on the set through calculating a residual error between a nonlinear mathematical model and a prediction model, determining whether a fault of the wind turbine generator system occurs, and starting the fault predicating model for diagnosing and positioning the fault. According to the method of the invention, the nonlinear model of the wind turbine generator system is established by means of the partial least square method; and then the fault diagnosis model is constructed according to the extreme learning machine, chaotic mapping and the firefly algorithm; and fault monitoring is performed through combining the nonlinear model and the fault diagnosis model; once an alarm of the monitoring model occurs, the DBN-ELM model is started for diagnosing and positioning the fault, thereby reducing fault monitoring complexity and improving fault diagnosis correct rate.

Description

technical field [0001] The invention relates to the field of wind power, in particular to an intelligent monitoring and diagnosis method for a fault state of a wind turbine. Background technique [0002] Wind energy is an inexhaustible green energy source. According to data released by the Global Wind Energy Council (GWEC), in 2017, the newly installed capacity of wind power in the world was about 52.57 gigawatts (GW), and the cumulative installed capacity reached 539.58 GW. Last year, my country's new wind power installed capacity reached 19.5GW, accounting for 37.1% of the world's new wind power installed capacity. However, wind power generation systems are often installed in remote, inaccessible areas or areas where the climate is not suitable for long-term human stay. For a long time, the method of planned maintenance and subsequent maintenance has been adopted. Routine maintenance is carried out after planned maintenance runs for 2500h or 5000h, and it is impossible t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/34
CPCG01R31/34
Inventor 于文新黄守道赵延明吴轩
Owner HUNAN UNIV
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