Intelligent evaluation method and system for high-temperature capacity reduction state of wind turbine generator

Abstract

The invention discloses an intelligent evaluation method for a high-temperature capacity reduction state of a wind turbine generator, and the method comprises the following steps: collecting multi-dimensional feature variables of the wind turbine generator, and forming a training data set of a vin-Copula Bayesian network model based on the multi-dimensional feature variables; establishing a pair-Copula function according to the training data set so as to determine an optimal Copula function between every two of the multi-dimensional characteristic variables; according to the optimal Copula function, obtaining a correlation coefficient of each group of pair-Copula functions in the optimal Copula function, constructing a relationship tree based on the correlation coefficient, and generatinga vine-Copula Bayesian network model by utilizing the relationship tree; and evaluating the test samples in the state point test sample set by using the vine-Copula Bayesian network model. The invention further discloses an intelligent evaluation system for the high-temperature capacity reduction state of the wind turbine generator. According to the method, the vine-Copula Bayesian network model is utilized to comprehensively consider the state parameters of each dimension of the characteristic variables of the wind turbine generator and the correlation of the state parameters, and the occurrence probability of the high-temperature capacity reduction state of the wind turbine generator can be accurately evaluated.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method and system for intelligently evaluating the high-temperature derating state of a wind turbine. Background technique [0002] The working environment of wind turbines is complex. As a mechanical transmission system, it is easily affected by environmental factors, such as random changes in wind speed and wide fluctuations in temperature, so that various system components cannot operate under stable conditions, which leads to wind turbines in some During the period of time, it will be in a sub-healthy state. Although the unit may not be shut down, it will reduce the output and power generation of the unit, thereby affecting the economic benefits of the wind turbine operator. In order to reduce the loss of power generation of wind turbines in a sub-healthy state, it is necessary to identify and evaluate such states of the wind turbines, so as to formulate more ...

AI Technical Summary

Problems solved by technology

[0007] Most of the existing methods for evaluating unit performance use feed-forward neural networks to establish multi-parameter wind turbine state prediction models to evaluate unit performance, but neural network modeling consumes a lot of training time, and the input of the model Sample size affects the training accuracy of the model

[0008] In addition, although the existing SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control) system can provide more unit status parameters, most of them use single-dimensional data for analysis, or use multi-dimensional data without comprehensively considering the relationship between various parameters. The correlation between them, the coupling characteristics between each state parameter cannot be further utilized

[0009] Moreover, among the existing methods for evaluating wind turbines, there are few methods for evaluating the high temperature derating status of wind turbines

[0010] Therefore, it is hoped that there is a wind turbine state assessment method that can solve the problems existing in the prior art for the shortcomings of the traditional state estimation method that cannot fully utilize the coupling characteristics between various state parameters and the time-consuming modeling.

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