Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.

Abstract:The conductor temperature of an overhead transmission line varies with time and space, which has an important impact on the system operation. In this paper, the conductor temperature is solved iteratively by the CIGRE heat balance equation. The time-space variation of conductor temperature of a 220-kV transmission line is analyzed using real meteorological data from Weihai. Considering the temporal distribution characteristics, the seasonal model of the conductor temperature is given. Considering the spatial distribution, the mean value model, the weight average model, and the segmentation model are established. The system power flow involving the conductor temperature is established based on the relationship between conductor temperature and transmission line parameters. Through the calculation of power flow and the analysis of the maximum power transmission capability, the accuracy of the segmentation model is verified. The results show that the conductor temperature of overhead lines has obvious time-space variation characteristics. It is necessary to consider the time-space variation when analyzing the operation state of power systems.

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