基于优化FCM聚类的RELM风速预测OA北大核心CSCDCSTPCD
Wind Speed Forecasting of Regularized ELM Based on Optimized FCM Clustering
准确的风速预测对大规模风电并网具有重要意义.提出一种基于互信息属性约简优化聚类的正则化极限学习机短期风速预测方法.首先考虑不同属性特征对风速的不同影响,计算风速特征属性序列与风速序列的互信息,并运用最大相关最小冗余算法进行特征选择,然后采用优化的模糊C均值聚类方法对风速样本进行聚类,再对极限学习机进行优化,进而构建风速组合预测模型.最后结合风电场实测数据进行风速预测实验,结果表明该方法具有较高的预测精度.
Accurate wind speed forecasting is of great significance for large-scale wind power integration. In this paper, a new method of short-term wind speed forecasting is put forward based on regularized extreme learning machine (ELM) of optimal clustering and mutual information attribute reduction. Firstly, considering different effects of different attributes on wind speed, mutual information between wind speed characteristic sequence and wind speed sequence are…查看全部>>
潘超;秦本双;何瑶;袁翀;沈清野
东北电力大学电气工程学院,吉林省吉林市 132012东北电力大学电气工程学院,吉林省吉林市 132012东北电力大学电气工程学院,吉林省吉林市 132012国网淳安供电公司,浙江省淳安市 311700国网舟山供电公司,浙江省舟山市 316000
信息技术与安全科学
风速预测最大相关最小冗余模糊C均值聚类正则化极限学习机
wind speed forecastingminimal redundancy maximal relevancefuzzy C-means clusteringregularizationextreme learning machine
《电网技术》 2018 (3)
交直流混杂环境下基于关键参数辨识的变压器直流偏磁保护策略研究
842-848,7
国家863高技术基金项目(SS2014AA052502)国家自然科学基金项目(51507027). Project Supported by the National High Technology Research and Development Program of China (863 Program) (SS2014AA052502) National Natural Science Foundation of China (51507027).
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