Abstract
Most conventional wind turbine fault diagnosis techniques only use a single type of signal as fault feature and their performance could be limited to such signal characteristics. In this paper, multiple types of signals including vibration, temperature, and stator current are used simultaneously for wind turbine misalignment diagnosis. The model is constructed by integrated methods based on Dempster–Shafer (D–S) evidence theory. First, the time domain, frequency domain, and time–frequency domain features of the collected vibration, temperature, and stator current signal are respectively taken as the inputs of the least square support vector machine (LSSVM). Then, the LSSVM outputs the posterior probabilities of the normal, parallel misalignment, angular misalignment, and integrated misalignment of the transmission systems. The posterior probabilities are used as the basic probabilities of the evidence fusion, and the fault diagnosis is completed according to the D–S synthesis and decision rules. Considering the correlation between the inputs, the vibration and current feature vectors’ dimensionalities are reduced by t-distributed stochastic neighbor embedding (t-SNE), and the improved artificial bee colony algorithm is used to optimize the parameters of the LSSVM. The results of the simulation and experimental platform demonstrate the accuracy of the proposed model and its superiority compared with other models.
Highlights
In order to address global warming issues, many countries have reduced carbon emissions year by year as one of their targets for economic and social development
The misalignment of the transmission system can inevitably lead to vibration of the unit, which will reduce the reliability of the power generation system
The belief function and the plausibility function can be obtained by calculating the sum of the basic probability assignment function, and the final decision is made after combining multiple evidences from different sources
Summary
He et al analyzed the vibration characteristics of the transmission chain of a wind turbine based on double-elastic support with natural axial misalignment between the output shaft of gearbox and the shaft of generator causing vibration signals of normal gearbox blend with serious high-order gear mesh frequency and smooth modulation [14] These methods mainly applied rely on single information, and their performance could be limited owing to the limited source of information. In this paper, based on the good theoretical basis and application effect of D–S evidence theory [23,24,25,26,27], it is used to complete decision fusion, which provides a sufficient fault diagnosis solution for wind turbine misalignment fault. The belief function and the plausibility function can be obtained by calculating the sum of the basic probability assignment function, and the final decision is made after combining multiple evidences from different sources. The Platt algorithm calculates the probability formula for each classifier as follows, where pm is the probability that sample x belongs to the i-th class [35]: pm (y m/x)
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