Abstract
Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine’s vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.
Highlights
The problem of energy shortage and environmental degradation in the world is becoming more and more serious
Optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another Least Squares Support Vector Machine (LSSVM) optimized by QGA is used as a non-linear combiner
The Results of LSSVM Optimized by QGA
Summary
The problem of energy shortage and environmental degradation in the world is becoming more and more serious. Wind energy as environmentally friendly and renewable energy has attracted increasing attention [1]. The cumulative installed capacity of global wind power has steadily increased in recent years [2]. How to effectively decrease the risk of fault during the operation of wind turbines has become a difficult problem. The misalignment fault of wind turbines belongs to a latent fault [6,7]. This is because when it happens in actual operation, the unit’s operating parameters will not reach their early warning values immediately, but when the fault accumulates to a certain extent, it will seriously damage the unit’s equipment and cause unit shutdown [8]. It is necessary to predict the latent trend of misalignment, which can overcome the blindness of handling the fault and avoid more loss of human and material resources
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