Improving the predicted accuracy of wind power is beneficial to maintaining the secure operation and dispatching of the power system. Therefore, a combined model consisting of the variational mode decomposition(VMD), Convolutional Long short memory network(ConvLSTM) and error analysis is conducted for short-term wind power forecasting. Firstly, the VMD algorithm decomposes the wind power signal into an ensemble of components with different frequencies; A novel architecture embedding the convolution operation into LSTM network is procured as the preliminary forecasting engine, which is appropriate for extracting the spatial and temporal characteristics of each sub-series. Afterwards, all the predicted sub-signals would be aggregated to obtain the preliminary forecasting results; For the sake of further mining the unsteady features within the raw wind power series, LSTM modelling the trend of error sequence of the preliminary forecasting result is adopted. Eventually, the final forecasting results is obtained by integrating the forecasting error series and preliminary results. As a result, It can be easily demonstrated that by comparing with the contrastive models, the proposed model achieves the highest prediction performance for wind power series which is difficult to capture.

Wind power plays a crucial role in the secure conversion and management of the power system. Therefore, this study proposes a hybrid model for short-term wind power forecasting, which consists of the variational mode decomposition(VMD), the K-means clustering algorithm and long short term memory(LSTM) network.The combination model is conducted as follows: the VMD decomposes the raw wind power series into a certain number of sub-layers with different frequencies; K-means as a data mining approach is executed for splitting the data into an ensemble of components with similar fluctuant level of each sub-layer; LSTM is adopted as the principal forecasting engine for capturing the unsteady characteristics of each component. Eventually, the forecasting results would be generated by aggregating the predicted components.To evaluate the fitting capacity of the proposed model, seven different models including the back propagation neural network(BP) approach, the Elman neural network(ELMAN), the LSTM approach, the VMD-BP approach, the VMD-Elman approach, the VMD-LSTM approach and the VMD-Kmeans-LSTM approach are implemented on four wind power series for multiple scales. The experimental results demonstrate the best performance in favour of the proposed model.

The larger capacity of power converters increases with the size of wind turbines over the years, which implies more failures occur in the components of power converter. Meanwhile, the strategies of fault diagnosis of power converter in wind turbine are still under discussion and research. The main purpose of this paper is proposing a strategy containing wavelet transform, feature analysis, judgment and back propagation neural network (BPNN) classification (WT-FA-JD-BP) to identify the single and double power components open-circuit faults accurately, happen in grid-side converter (GSC) of permanent magnet synchronous generator (PMSG) wind turbine systems. First, as the original signals, the three-phase bridge legs voltage of the GSC are collected under different faults. Second, wavelet transform is used to decompose and reconstruct the signals. Third, a new method includes feature analysis and judgment is conducted to amplify the divergence of data obtained by wavelet transform. Finally, the data are used as the inputs of BPNN for decision-making and classification. The simulation and experimental results show that the proposed strategy can classify the single and double open-circuit faults, and the accuracy is higher than that without data feature analysis and judgment.

The power converter is a significant device in a wind power system. The wind turbine will be shut down and off grid immediately with the occurrence of the insulated gate bipolar transistor (IGBT) module open-circuit fault of the power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies for the power converter there are few single and double IGBT module open-circuit fault diagnosis methods producing negative results, including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed: Primarily, by collecting the three-phase current varying with a wind speed of 22 states, including a normal state and 21 failure states of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are utilized as the input of the deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

In order to cooperate the wind farm operators with grasping the operation status of wind power converter, a novel reliability assessment strategy is proposed based on supervisory control and data acquisition (SCADA) multistate parameters prediction of permanent magnet synchronous generator (PMSG) wind turbine. The strategy considers “off-line training, on-line matching and assessment”. The operation reliability of wind power converter is obtained via the analysis and weight computing of confidence level, prediction value and actual value of SCADA multistate parameters. In the “off-line training” part, first, the FP-Growth association algorithm is employed to analyze the confidence levels of SCADA variables to the faults of wind power converter. The variables with high confidence level are defined as SCADA multistate parameters. Afterwards, wavelet packet transform (WPT) and K-means algorithm are employed to decompose, reconstruct, normalize and cluster the time series (off-line data) of multistate parameters under normal operation of wind turbine, to improve the generalization capability of long short term memory (LSTM) prediction model. In the part of “on-line matching and assessment”, the actual value time series (on-line data) of multistate parameters are decomposed and reconstructed by WPT. Each normalized sample is matched to the closest cluster centroid, which is generated in the part of “off-line training”. Then the corresponding LSTM model is conducted to predict based on the each sample. The final prediction value is sum of prediction results of entire samples in clusters. Finally, the reliability of wind power converter is assessed by the proposed strategy. The effectiveness of proposed assessment strategy is verified by the experimental results on a PMSG wind power converter.