Open AccessScenario analysis of wind power output based on improved k-means algorithm
Author(s) -
Weiyuan Wang,
Fei Dou,
Xuan Yang,
Gaowei Liu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/675/1/012092
Subject(s) - cuckoo search , randomness , algorithm , computer science , cluster analysis , reliability (semiconductor) , power (physics) , sequence (biology) , wind power , grid , power grid , cuckoo , mathematical optimization , mathematics , artificial intelligence , engineering , particle swarm optimization , statistics , physics , quantum mechanics , electrical engineering , zoology , geometry , biology , genetics
To ensure the safety and reliability of grid operation, an accurate description of wind power output is crucial. In this paper, the Grey Wolf Optimization algorithm (GWO) optimized by the Cuckoo Search algorithm (CS) is proposed to improve the traditional k-means clustering algorithm and the improved k-means algorithm is applied to the scenario analysis of wind power output. The simulation results show that the error between the typical scene and the initial scene is only 4.56% when reduced by the improved k-means algorithm, so the typical scene not only maintains the temporal sequence of the initial scene, but also fits the output fluctuation and randomness of the initial scene well.