With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed forecasts, and accurate wind speed forecasts are necessary to schedule power system. In this study, an artificial neural networks (NNs) model with a variational mode decomposition (VMD) for a short-term wind speed forecasting was presented. To reduce the non-stationary of wind speed time series, the historical wind speed was decomposed into different intrinsic mode functions (IMFs) by a VMD. The back-propagation NN with Levenberg-Marquardt was adopted to build sub-models according to the different characteristic of each IMF. The sub-models corresponding to different IMFs were superposed to obtain wind speedforecasting models. In the experiment, the proposed forecasting model was compared with an NN with wavelet decomposition and empirical mode decomposition. The performance was evaluated based on three metrics, namely maximum absolute error, root mean square error and the correlation coefficient. The comparison results indicate that significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.
Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme. data mining technologies have made considerable progress [11]. Consequently, data-based methods have received increasing attention in the area of fault detection and location of WTs [12].In data-based research, substantial improvements have been proposed to improve the accuracy of fault identification. Chen et al. [13] proposed a fault prediction method for WTs using an adaptive neuro-fuzzy inference system with a priori knowledge. This method provides a fault instruction and allows the operator to determine the repair plan before the failure worsens. Hu et al. [14] proposed a fault diagnosis method that combines SVM and domain knowledge to improve the identification accuracy for faults in WTs. However, the aforementioned methods depend excessively on expert knowledge, and their identification results are simplistic. Wang et al. [15] proposed an FDI scheme based on a variable selection method using principal component analysis; in this scheme, the result of fault identification is determined by the signal contribution, but the method can only identify an abnormal signal rather than a fault type. Ruiz et al. [16] proposed a fault classification method for WTs that transforms the domain signals of WTs into two-dimensional matrices. Certain similar faults are merged for identification in the experiment, and the separation of similar faults is unwarrantable. Pashazadeh et al. [17] obtained the fault data of WTs from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator and Simulink (in a MATLAB environment) and developed an FDI scheme on the basis of classifier fusion, in which four classifiers are implemented in parallel. However, this m...
A robust fault-detection design based on residual Kullback-Leibler (K-L) divergence, which is applied to a 5 MW offshore wind turbine (WT) benchmark, is presented. The main challenges of the wind turbine fault detection lie in its complex operation conditions and disturbances as well as measurement noise. For overcoming these difficulties, the measured data are divided on the basis of the operation conditions of WT. The robust residual generator based on parity vector is adopted to calculate the residual under different operation conditions. The K-L divergence based on probability density function is employed to measure the residual. Then, the threshold for the fault detection is determined in line with both false alarm rate and missed detection rate. The simulation results show that the performance and effectiveness of the proposed robust fault detection are better than compared with other data-driven fault-detection approaches.
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