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工程科学与技术:2020,52(6):234-241
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基于FCM和SSA–ELM的超短期风功率预测
(华北水利水电大学 电力学院,河南 郑州 450000)
Ultra-short-term Wind Power Prediction Based on Combination of FCM and SSA–ELM
(School of Electric Power, North China Univ. of Water Resources and Electric Power, Zhengzhou 450000, China)
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投稿时间:2020-01-15    修订日期:2020-10-16
中文摘要: 针对风电输出功率波动大、随机性强等特征引起风功率难以预测的问题,提出了基于模糊C均值聚类(fuzzy C-means,FCM)选取相似日和樽海鞘群算法优化极限学习机(SSA–ELM)的风电场超短期风功率预测模型。首先,采用FCM数据聚类方法,筛选出与预测日相关性较大的历史相似日,将其风速、温度、风向、气压等影响风功率的主要因素组成多输入样本集合;其次,通过训练集在训练过程中确定网络参数,利用樽海鞘群算法在迭代过程中不断优化极限学习机的输入权值矩阵及隐含层偏差值,建立樽海鞘群算法优化极限学习机的超短期风功率预测模型;最后,根据超短期风电并网的相关规定,针对河南省某风电场的实际数据,分别从基于相似日超短期预测、具有代表性的四季预测和滚动误差3方面进行仿真实验,并与传统极限学习机(extreme learning machine,ELM)和BP神经网络模型进行对比分析,结果表明本文提出的模型收敛速度快,预测精度较高。证明了基于FCM和SSA–ELM的超短期风功率预测模型具有良好的追踪性和泛化性。
Abstract:In order to solve the problem that wind power is hard to predict due to its characteristics such as large fluctuation and strong randomness, a ultra-short-term wind power prediction model for wind farms was proposed based on fuzzy C-means (FCM), which selects similar daily and salp swarm algorithm to optimize the extreme learning machine (SSA–ELM). Firstly, the FCM data clustering method was used to select similar days with higher correlation with the predicted days, of which the historical wind speed, temperature, wind direction and other climatic factors that are highly correlated with wind power formed a multi-input sample set. Secondly, the network parameters were determined in the training process through the training set, and the input weight matrix and hidden layer deviation of the extremely learning machine were optimized to improve the adaptability and accuracy of the prediction model by using the salp swarm algorithm in an iterative process. Finally, according to the ultra-short-term wind power interconnection related regulations, using the actual data of a wind farm in Henan province from ultra-short-term prediction based on similar days, three aspects of the four seasons and rolling prediction error of the representative simulation experiment, and the extreme learning machine (ELM) and BP neural network model were analyzed. The results show that the proposed model has faster convergence speed and higher prediction precision. It is demonstrated that the proposed ultra-short-term wind power prediction model based on FCM and SSA–ELM has good tracking and generalization ability.
文章编号:202000053     中图分类号:TK89    文献标志码:
基金项目:国家自然科学基金项目(31671580;U1504622)
作者简介:张红涛(1977-),男,教授,博士.研究方向:模式识别及图像处理.E-mail:39583633@qq.com
引用文本:
张红涛,韩婧,谭联,刘鹏,张亮.基于FCM和SSA–ELM的超短期风功率预测[J].工程科学与技术,2020,52(6):234-241.
ZHANG Hongtao,HAN Jing,TAN Lian,LIU Peng,ZHANG Liang.Ultra-short-term Wind Power Prediction Based on Combination of FCM and SSA–ELM[J].Advanced Engineering Sciences,2020,52(6):234-241.