Abstract
Aiming at the problem of operating state prediction of generator bearing, a prediction method based on quantum particle swarm optimization (QPSO) and united least squares support vector machine (ULSSVM) is proposed. Firstly, the time least squares support vector machine (TLSSVM) model is established in accordance with the change law of characteristic parameters over time. Space least squares support vector machine (SLSSVM) model is established in accordance with the law of mutual influence between characteristic parameters. Secondly, the QPSO algorithm is used to optimize the parameters of each least squares support vector machine (LSSVM) model. When the difference between the predicted value and the measured value reaches the minimum, the optimal LSSVM parameter set is output. Then the improved Dempster-Shafer (D-S) theory is used to determine the weights of TLSSVM and SLSSVM. A united model of time LSSVM and space LSSVM is established. The characteristic parameters are predicted. The prediction results and the reference matrix are fused and reduced in dimension. Finally, the generator bearing operating status is predicted based on the location of the prediction results. The results show that the proposed method is helpful to realize the operating state prediction of the wind turbine bearing.
Highlights
Wind turbine has been in a rapid growth mode since the 20th century
In order to improve the accuracy of the characteristic parameter prediction model and avoid the blindness of parameter selection, the quantum particle swarm optimization algorithm is used to optimize the kernel parameter and the penalty parameter of the least squares support vector machine (LSSVM) model
The method based on quantum particle swarm optimization (QPSO) united least squares support vector machine (ULSSVM) yields the best accuracy, significantly higher than time least squares support vector machine (TLSSVM), Space least squares support vector machine (SLSSVM), and ULSSVM
Summary
Wind turbine has been in a rapid growth mode since the 20th century. This rapid growth affects the performance of wind turbines. Least squares support vector machine (LSSVM) is a commonly used method in data prediction [5,6,7]. The change of a certain characteristic parameter will reflect or affect the change of other characteristic parameters to a certain extent Aiming at this problem, a prediction method based united least squares support vector machine (ULSSVM) is proposed. OPERATING STATE PREDICTION FOR WIND TURBINE GENERATOR BEARING BASED ON ULSSVM AND QPSO. Quantum particle swarm optimization (QPSO) is proposed based on PSO [17] This model assumes that particles have quantum behavioral characteristics. An operating state prediction method for wind turbine bearings based on united LSSVM and QPSO is proposed. A case study shows that the prediction results based on this method can realize the prediction of the operating state of the wind turbine bearing
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