Wind Direction Division of Wind Farm Based on Spontaneous Aggregation Characteristics of Wind-direction Data

Abstract

Wind direction information is of great significance to both wind energy assessment and wind power characteristic analysis. How to divide the wind direction sectors, while taking into account the continuous random fluctuation of thewind direction in the time and space dimension, has become a hot topic worthy of study. This paper presents a wind sector division method considering spontaneous aggregation characteristics of wind-direction measured data. Firstly, the basic knowledge about Markov clustering (MCL) algorithm used in wind direction division is introduced. Secondly, the relative variations of wind direction are calculated for the periodic wind direction data, and its probability distribution is estimated statistically. The uniform division interval for the discrete states of Markov chain is then determined according to the cumulative probability quantile boundary of the relative wind direction variations. Then, the discrete states of wind direction are regarded as the nodes of a directional graph. And the graph using state transition matrix as the connection weight is clustered by the MCL algorithm. The evaluation indexes are also defined to judge the clustering performance. Finally, the actual data of the wind farm is adopted to verify the effectiveness of clustering results. The simulation results show that using MCL algorithm can fully utilize spontaneous aggregation characteristics of the wind direction to obtain effective wind-direction division results.