低成本微电网轻量化在线超短期光伏功率预测算法设计  

Design of simplified online ultra-short term photovoltaic output forecasting algorithm for low cost microgrid

作　　者：陈晓科 陈奇芳 何婷[3] 黄锦成 CHEN Xiao-ke;CHEN Qi-fang;HE Ting;HUANG Jin-cheng(Electric Power Research Institute of Guangdong Power Grid Co., Ltd.; Guangdong Province Key Laboratory of Smart Grid Technology, Guangzhou Guangdong 510080, China;State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;Guangdong Key Laboratory of Clean Energy Technology, School of Electric Power,South China University of Technology, Guangzhou Guangdong 510640, China;Guangdong Intework Energy Technology Co., Ltd., Guangzhou Guangdong 511458, China)

机构地区：[1]广东电网有限责任公司电力科学研究院,广东省智能电网新技术企业重点实验室,广东广州510080 [2]华北电力大学新能源电力系统国家重点实验室,北京102206 [3]华南理工大学电力学院,广东省绿色能源技术重点实验室,广东广州510640 [4]广东智造能源科技研究有限公司,广东广州511458

出　　处：《控制理论与应用》2016年第12期1658-1666,共9页Control Theory & Applications

摘　　要：本文结合用户侧光伏微电网的实际工程需求,研究了满足嵌入式应用需求的轻量化在线超短期光伏功率预测算法.采用了核函数极限学习机算法作为在线超短期光伏功率预测算法的核心,通过使用特征序列代替传统时间序列作为训练样本集,实现了数据量的轻量化设计;通过基于趋势加权相似度的训练样本精选,提高了算法精度,简化了计算量,降低了算法计算时间.通过嵌入式系统实验测试结果表明,本文提出的轻量化超短期光伏预测算法在精度、计算时间和数据存储等方面都能满足嵌入式系统低成本应用的需求.Based on the engineering requirement of user side photovoltaic (PV) microgrid, a simplified online short term PV output forecasting algorithm is studied for embedded system application. Extreme learning machine with kernel (ELM K) algorithm is adopted as the main part. The traditional time sequence of training dataset is replaced with characteristic sequence of history data, therefore, the amount of storage place of training data is reduced. Because of the optimal training dataset is selected from original training dataset by trend weighted similarity, the accuracy is improved, also the amount of calculation and runtime of forecasting algorithm is reduced. The test results of embedded system show that the performance of proposed online short term PV output forecasting algorithm on accuracy, runtime and storage occupation can satisfy the requirement of low cost embedded system application.

关 键 词：光伏功率预测 超短期 轻量化 核函数极限学习机 

分 类 号：TM615[电气工程—电力系统及自动化]