# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 1 Introduction
The combined forecasting model using GM (1,1,0) optimized by Particle Swarm Optimization (PSO) algorithm and SVM optimized by PSO achieved better prediction accuracy for the short-term load of a regional power grid. Because the wind turbine is a complex non-linear system, when the misalignment fault occurs, the fault signals have both certainty and randomness characteristics [23]. In addition, the misalignment fault samples obtained in this paper are relatively few. It can be indicated from Table 1 that the grey \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Scope** & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{Time series prediction} & Stationary random & \multirow{3}{*}{Few samples demand} & Not suitable for \\  & sequence Short-term & & & non-linear systems \\  & forecast & & Simple model & Not suitable for medium \\  & & & & to long-term forecasting \\ \hline \multirow{3}{*}{Support Vector Machine} & Small sample Non-linear & \multirow{3}{*}{High prediction accuracy} & The selection of \\  & system & & & parameters has an \\  & system & & & impact on the accuracy \\ \hline \multirow{3}{*}{Least Squares Support Vector Machine} & Small sample Non-linear & \multirow{3}{*}{Predictive calculation} & The selection of \\  & system & & & parameters has an \\  & system & & SVM Suitable for few \\  & & & samples & of the model \\ \hline \multirow{3}{*}{Neural networks} & Non-linear complex & \multirow{3}{*}{Strong nonlinear mapping ability} & Local minimum problem \\  & system & & Adaptive learning ability & A lot of samples required \\ \hline \multirow{3}{*}{Grey prediction model} & Data with specific trends & \multirow{3}{*}{Few samples} & Incomplete consideration \\  & Short-term forecast & & High modelling accuracy & Not suitable for \\ \cline{1-1}  & & & long-term forecasting \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of common prediction methods. prediction model is suitable for predicting deterministic trends with few samples, while Least Squares Support Vector Machine (LSSVM) is suitable for predicting the non-linear and stochastic trends with few samples and higher speed [24]. Therefore, the grey prediction model and LSSVM are selected to be the prediction methods for the misalignment fault of wind turbines. In the grey prediction models, the MGM (1, \(n\)) can use the multivariate characteristic parameters of the fault state as the inputs of the prediction model [25], which can comprehensively reflect the fault state at the previous moment and establish a more accurate prediction model. Therefore, this paper uses MGM (1, \(n\)) to predict the misalignment faults of wind turbines. Because MGM (1, \(n\)) has the disadvantage of only being suitable for short-term prediction, the rolling prediction method is used to improve MGM (1, \(n\)). Compared with the combination of fixed weight coefficient, LSSVM can assign non-linear weight coefficients to the prediction values of single prediction models dynamically, which makes the weight allocated more reasonably. Therefore, in this paper, a combined forecasting model using LSSVM optimized by quantum genetic algorithm (QGA) as a non-linear and variable weight combiner is proposed. The output prediction values of both the LSSVM model optimized by QGA and the improved Multivariate Grey Model (IMGM (1, \(n\))) are input to the non-linear combiner to get the final predicted values. The vibration and current signals from the misalignment fault simulation model of wind turbine demonstrate that the combined forecasting model has higher prediction accuracy than the single ones.