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Wind turbine generator dynamic reliability evaluation method based on improved Bayesian network

A Bayesian network, wind turbine technology, applied in computer parts, electrical digital data processing, instruments, etc., can solve the problem of unsatisfactory reasoning effect and difficult to solve the uncertainty of wind turbine "multiple heterogeneous" state information. impact and other issues to achieve the effect of improving the accuracy of the assessment

Active Publication Date: 2021-10-15
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

[0006] 2) On the other hand, because the traditional Bayesian method needs to make certain assumptions about real-time information in actual reasoning, when the number of state variables is large or the correlation between state information is large, the reasoning effect will be unsatisfactory. That is to say, it will be difficult to directly adopt the traditional Bayesian method to solve the uncertain influence of the "multiple heterogeneous" state information of wind turbines and the incomplete coupling and correlation description of unit component failures and state information on the reliability evaluation results.

Method used

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  • Wind turbine generator dynamic reliability evaluation method based on improved Bayesian network
  • Wind turbine generator dynamic reliability evaluation method based on improved Bayesian network
  • Wind turbine generator dynamic reliability evaluation method based on improved Bayesian network

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Embodiment

[0162] In this example, based on the operating data of an offshore wind farm in my country, the gearbox, a key component, is selected as the research object, and the state monitoring information of the gearbox components is extracted as the main source of experimental data.

[0163] 1. Establishment of gearbox pR-BN

[0164] The gearbox fault is determined as the top-level event E of the fault tree; the fault phenomenon that affects the normal operation of the gearbox is regarded as the intermediate event of the fault tree, mainly including: gearbox tooth bending M1, gearbox tooth surface gluing M2 and bearing overheating M3; The basic events of the cause of box failure can be represented by the state observation node, mainly including: impact load displacement X1, sub-bearing state X2, inter-gear lubrication effect X3, lubricating oil temperature X4, bearing lubrication effect X5 and sensor state X6, and this Set as basic random variable X={X 1 ,X 2 ,X 3 ,X 4 ,X 5 ,X 6}...

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Abstract

The invention relates to a wind turbine generator dynamic reliability evaluation method based on an improved Bayesian network. The method comprises the following steps: 1) constructing a pure reliability Bayesian network of a wind turbine generator based on a fault tree and a cloud model; 2) optimizing the simple reliability Bayesian network by adopting a scale-free network to obtain an improved Bayesian network; 3) performing dynamic Bayesian reasoning on the improved Bayesian network; and 4) quantitatively evaluating the reliability of the unit according to the improved Bayesian network and a dynamic reasoning process. Compared with the prior art, the method has the advantages that real-time state information multivariate heterogeneous characteristics and the coupling incidence relation are considered, the reliability change trend is dynamically obtained, and the evaluation accuracy is high.

Description

technical field [0001] The invention relates to the field of wind turbine reliability evaluation, in particular to a dynamic reliability evaluation method for wind turbines based on an improved Bayesian network. Background technique [0002] Wind power, as a form of new energy power generation with mature technology and wide application, has become one of the important ways for my country to achieve the "3060" double carbon target. By the end of 2020, the total installed capacity of wind power in my country has reached 280 million kilowatts, accounting for 12.8% of the total installed capacity of power sources, and the proportion of wind power generation to the total annual power generation has also reached 6.12%. An important form of energy for balance. [0003] In recent years, experts and scholars at home and abroad have carried out extensive and in-depth research on the reliability assessment of wind turbines / wind farms. It is mainly based on the analysis and research o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/17G06K9/62G06F119/02
CPCG06F30/17G06F2119/02G06F18/29Y04S10/50
Inventor 黄玲玲符杨苗育植刘璐洁魏书荣米阳
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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