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A Fault Identification Method for Offshore Doubly-fed Wind Turbine Considering Marine Meteorological Factors

A technology of doubly-fed wind turbines and meteorological factors, applied in the direction of wind turbines, engines, mechanical equipment, etc., can solve the problems of ignoring simultaneity, not considering the actual situation of faults and short-term weather events, etc., to reduce economic losses and avoid catastrophes Failure, the effect of improving the service life

Active Publication Date: 2021-07-16
SHANGHAI UNIVERSITY OF ELECTRIC POWER +1
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Problems solved by technology

But they used the annual average and monthly average weather parameters, did not consider the actual situation and short-term weather events when the failure occurred, and only analyzed the old wind turbine with a rated power of 300 kW
The influence of various environmental factors on the failure behavior of wind turbines in the above studies is modeled separately, and only a single environmental factor is considered each time, ignoring their simultaneity

Method used

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  • A Fault Identification Method for Offshore Doubly-fed Wind Turbine Considering Marine Meteorological Factors
  • A Fault Identification Method for Offshore Doubly-fed Wind Turbine Considering Marine Meteorological Factors
  • A Fault Identification Method for Offshore Doubly-fed Wind Turbine Considering Marine Meteorological Factors

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Embodiment

[0050] The present invention provides a fault discrimination method for offshore doubly-fed wind turbines considering marine meteorological factors. The present invention can be roughly divided into the following steps:

[0051] First, preprocess the wind speed, power, temperature, and turbulence parameters recorded by SCADA, mine the correlation between the data, fit the probability density curve of the parameters, and obtain the marginal distribution value;

[0052] Secondly, calculate the relevant parameters of the five Copula functions of the four parameter combinations, and select the optimal Copula function;

[0053] Finally, combined with Bayesian decision theory, the wind turbine status is judged.

[0054] Specifically:

[0055] 1. Correlation analysis and kernel density estimation of wind turbine state variables and marine meteorological factors

[0056] Obtain the correlation between the wind speed, power, temperature and turbulence parameters recorded by SCADA, an...

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Abstract

The present invention relates to a fault discrimination method for marine doubly-fed wind turbines considering marine meteorological factors, comprising the following steps: 1) obtaining the marginal distribution function corresponding to each variable according to the state variables of marine doubly-fed wind turbines and the historical data of marine meteorological factors, The variables include wind speed, power, temperature and turbulence; 2) construct the Copula density function model of each variable, combine variable historical normal data and fault data, adopt maximum likelihood estimation to carry out parameter estimation to each Copula density function model respectively , to obtain the optimal Copula density function model; 3) The kernel density function value and marginal distribution function value of the data to be tested are used as the input of the optimal Copula density function model, and the status of the fan is judged according to the Bayesian decision theory. Compared with the prior art, the invention has the advantages of comprehensive consideration, accurate prediction, improved service life and the like.

Description

technical field [0001] The invention relates to the field of operation and maintenance of offshore wind turbines, in particular to a fault discrimination method for offshore doubly-fed wind turbines considering marine meteorological factors. Background technique [0002] In recent years, offshore wind power has developed rapidly, showing the characteristics of deep sea and large-scale. The safe and stable operation of wind turbines has attracted people's attention. Compared with onshore wind power, offshore wind power has the advantages of more annual utilization hours, high average wind speed, and large unit capacity. The characteristics lead to a higher failure rate of offshore wind turbines, more difficult maintenance, and greater outage losses. Therefore, it is urgent to identify the early faults of wind turbines in a timely and accurate manner to avoid huge losses caused by fault deterioration, which is of great significance to the safe and stable operation of offshore...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418G05B23/02F03D17/00
CPCF03D17/00G05B19/4183G05B23/0281G05B2219/32404
Inventor 魏书荣王栋悦常彬符杨闫鹤鸣李方媛
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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