Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocessed by a box plot and gray correlation analysis. Then, a generalized method is proposed to calculate the natural gradient and the improved natural gradient boosting (NGBoost) model is proposed based on this method. Finally, blending fusion is used in order to enhance the learning effect of improved NGBoost. The model is validated with the help of measured data from Dalian Tuoshan wind farm in China. The results show that under the specified confidence, compared with the single NGBoost metamodel and other short-term direct probability prediction models, the model proposed in this paper can reduce the forecast area coverage probability while ensuring a higher average width of prediction intervals, and can be used to build new efficient and intelligent energy power systems.
Accurate wind speed prediction is of great significance to the stable operation of the power grid when large-scale wind power is connected to the grid. This article proposes a new multi-step wind speed combination prediction model based on gray correlation analysis. First, the gray correlation analysis is performed on wind speed–related attributes, the more relevant attribute factors are selected as the input set of the prediction model, and the regularized extreme learning machine improved by the cuckoo optimization algorithm is used to perform multi-step prediction of wind speed. Then, an error self-tuning model is established to further improve the prediction accuracy. Finally, the measured results of different wind farms and seasons are selected to simulate the prediction effect of the proposed model, and the prediction accuracy and generalization ability of the proposed model are verified through comparative analysis.
This paper presents a gray-box harmonic resonance frequency identification method of multiple-inverter-fed power system, which enables modal analysis oriented to system designers based on only frequency response data provided by diverse vendors or measured by frequency scanning. First, admittance transfer functions of all grid-connected inverters (GCIs) are fitted using Matrix Pencil Method-Vector Fitting (MPM-VF) combined method. Then, node admittance matrix (NAM) is formed according to the topology of whole system. Finally, harmonic resonance frequency along with changes in number of GCIs are identified by NAM-based modal analysis (MA). The proposed gray-box identification method is implemented in a typical multiple-inverter-fed power system. The correctness of harmonic resonance frequency identification results and the effectiveness of the presented method are verified by simulation results obtained in Matlab/Simulink platform and OPAL-RT digital real-time simulation platform. Based on the identification results, a more stable and better power quality multiple-inverter-fed power system can be built by system designers though avoiding the appearance of harmonic sources with corresponding resonance frequency.
To increase the equivalence accuracy of wind farms and expand their applicability under multiple operating conditions, this paper proposes a method combining blending with extreme gradient boosting (XGBoost) to realize clustering index dimensionality reduction and dynamic time warping (DTW) optimization. Density-based spatial clustering of applications with noise (DBSCAN) is aimed at realizing clustering and fusing the clustering results. This approach can help process multi-dimensional time-series feature operation data of wind turbines to formulate accurate and effective wind farm plans for the division of wind turbine clusters onsite. First, the XGBoost-Blending approach is used to select the clustering indicators for dimensionality reduction. Second, a clustering method based on DBSCAN-DTW is established to divide the clusters and perform ensemble clustering. Finally, MATLAB/Simulink is used to build a simulation model. In this manner, a threephase short-circuit is introduced at the grid-connection point of a wind farm. A case study is performed under a variety of wind speed scenarios. The results verify the accuracy and wide applicability of the equivalence model formulated using the proposed method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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