摘要
利用优化粒子群K-means混合聚类算法分析大规模风电场的实际运行数据并对其建模。以山西盛风岭风电场作为实例,在大数据下依据其实际运行数据建立风速-功率模型并利用优化粒子群K-means混合聚类算法(IPSO-K-means)进行模型优化。结果显示,对比方法(传统方法、K-means、PSO-K-means)的平均误差分别为46.29%、18.58%、17.30%,而IPSO-K-means方法的平均误差为14.11%,说明所提方法可以大大提高模型的准确性。
The real operation data of large-scale wind farm are analyzed and modeled by using the optimized particle swarm K-means hybrid clustering algorithm.Shanxi Sheng wind ridge wind farm as an example,in the era of big data on the basis of the actual operation data to build wind power model and the optimization of particle swarm K-means hybrid clustering algorithm(IPSO-K-means)is used to optimize the model.The results showed that the comparative method(traditional method,K-means,PSO-K-means)the average error is 46.29%,18.58%and 17.30%respectively,while the average error of IPSO-K-means is 14.11%.The results show that the proposed method can greatly improve the accuracy of the model.
作者
郭敏
赵巧娥
高金城
周斌龙
GUO Min;ZHAO Qiao-e;GAO Jin-cheng;ZHOU Bin-long(Department of Electric Power Engineering,Shanxi University,Taiyuan 030013,China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2019年第1期48-54,共7页
Journal of Harbin University of Science and Technology
基金
国家自然科学基金(U1610116)
