On-line state monitoring and failure diagnosing method of wind turbine generator set

A technology for fault diagnosis of wind turbines, applied in the monitoring of wind turbines, wind turbines, engines, etc., can solve the problems of lack of theoretical support for wind power generation principles in data models and output results, and improve reliability and economy, optimize Effect of designing and improving precision

Inactive Publication Date: 2017-12-29
NANJING UNIV OF SCI & TECH
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Problems solved by technology

This method has high requirements on the health of the selected SCADA historical data, and its data model and output results lack the theoret

Method used

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  • On-line state monitoring and failure diagnosing method of wind turbine generator set
  • On-line state monitoring and failure diagnosing method of wind turbine generator set
  • On-line state monitoring and failure diagnosing method of wind turbine generator set

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Embodiment

[0093] see figure 1 The schematic diagram of the system scheme is shown. The SCADA system can monitor various data during the operation of the wind turbine and save them in low-frequency data mode. The embodiment of the present invention is based on the generator torque T in the SCADA datag The wind rotor speed w is the state quantity, the wind speed v and the pitch angle β are the input quantities, and the generator power p is the measurement quantity. During the operation of the wind turbine, the generator torque T can be obtained by reading the existing data of the SCADA system g and the optimal state estimation value and covariance of the wind rotor speed w at time k-1 to construct the sigma point and the corresponding weight. After the SCADA system finishes sampling and saving the data of the wind turbine at time k, it reads the wind speed and pitch angle at time k as the input of the Unscented Kalman method, and then according to the wind turbine system equation f(x(k) ...

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Abstract

The invention provides an on-line state monitoring and failure diagnosing method of a wind turbine generator set. The on-line state monitoring and failure diagnosing method comprises the following steps: adopting an unscented Kalman method for processing SCADA data serving as the state variables, input quantity and measuring quantity to acquire the current predicted value of the state variables; calculating the difference value between the current predicted value of the state variables and the measured value of the state variables in the SCADA data to serve as a first difference value; and when the first difference value exceeds the threshold value, judging that the current alarm failure needs to be handled. According to the on-line state monitoring and failure diagnosing method of the wind turbine generator set, the data collection quantity is small, failures of the wind turbine generator set can be detected on line and in real time, the reliability is high, and the operating cost is low.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to an online state monitoring and fault diagnosis method for a wind power generating set based on an unscented Kalman method and SCADA data analysis. Background technique [0002] With the increasing global energy crisis and environmental pollution, the development and utilization of renewable energy to achieve sustainable energy development has gradually become an important measure in the energy development strategy of various countries. As one of the renewable energy sources, wind energy has the characteristics of being renewable, widely distributed, non-polluting, and abundant in reserves, and has gradually become one of the hot spots in the development of renewable energy. The 2014 China Wind Power Development Report pointed out that in 2013, the new installed capacity of wind power in the country was 16089MW, an increase of 3130MW compared with 2012. ...

Claims

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Application Information

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IPC IPC(8): F03D17/00
CPCF05B2260/80F05B2270/404
Inventor 邱颖宁曹梦楠冯延晖
Owner NANJING UNIV OF SCI & TECH
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