Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT.

Misalignment is one of the common faults for the doubly-fed wind turbine (DFWT), and the normal operation of the unit will be greatly affected under this state. Because it is difficult to obtain a large number of misaligned fault samples of wind turbines in practice, ADAMS and MATLAB are used to simulate the various misalignment conditions of the wind turbine transmission system to obtain the corresponding stator current in this paper. Then, the dual-tree complex wavelet transform is used to decompose and reconstruct the characteristic signal, and the dual-tree complex wavelet energy entropy is obtained from the reconstructed coefficients to form the feature vector of the fault diagnosis. Support vector machine is used as classifier and particle swarm optimization is used to optimize the relevant parameters of support vector machine (SVM) to improve its classification performance. The results show that the method proposed in this paper can effectively and accurately classify the misalignment of the transmission system of the wind turbine and improve the reliability of the fault diagnosis.

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.Algorithms 2020, 13, 56 For the complex non-linear system, a single forecasting model is not enough to obtain ideal prediction results. Therefore, in order to predict the mechanical fault accurately, the combined forecasting model has attracted more and more attention from scholars. For example, in Ref.[18], the improved Grey Model (GM (1,1)) and the Back Propagation (BP) neural network optimized by Genetic Algorithm (GA) were used as the single forecasting models. The minimum sum of error squares was used as the combination principle to assign appropriate weight coefficients to them. The combined forecasting model had a smaller prediction error. Ref. [19] proposed a calculation method of combined weight coefficients for the unequal weight of error. The combined forecasting model was constructed by Multivariate Grey Model (MGM (1, n)) and Extreme Learning Machine (ELM) neural network. The combined forecasting model was more suitable for predicting the trend of the bearing fault. In Ref.[20], according to the minimum variance principle, Support Vector Machine (SVM) and grey model were combined to make up the shortcomings of single forecasting models. In Ref. [21], SVM was used as the combiner of forecasting models. The Kalman filter, BP neural network and SVM model were used as single forecasting models. The prediction errors of the single forecasting models were larger than that of the combined model. In Ref. [22], the BP neural network was used to determine the weight coefficients of each single forecasting model. The combined forecasting model using GM (1,1,θ) optimized by Particle Swarm Optimization (PSO) algorithm and SVM optimized by PSO achieved better prediction accuracy for the short-term load of...

In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD) wind turbines, this paper proposes a fuzzy proportional integral (PI) controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, k e and k ec , and scale factors, k up and k ui , of the fuzzy PI controller by an improved particle swarm optimization (PSO) method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.