电力系统自动化2017，Vol.41Issue(21)：17-24,8.DOI:10.7500/AEPS20170602004
适合于分析广义负荷序列间相关关系的最优延位法
Optimal Time-delay Method for Analyzing Correlation Between Generalized Load Sequences
摘要
Abstract
The traditional method of correlation analysis is limited to obtaining the superficial correlation while incapable of mining the potential correlation information between the sequences,but leading to the loss of the reference information provided to power system for operation and dispatching.For this reason,an optimal time-delay method is proposed to analyze the potential correlation between generalized load sequences.By delaying the sequences properly,the proposed method digs out the indirect correlation between generalized load sequences.Firstly,the time shift range and time monotonicity of sequences are regarded as constraints,then the objective function is built to obtain the maximum Pearson correlation coefficient.Secondly, the calculation strategy of the model is proposed.Finally,the data of a German region is taken to act as an example to analyze and calculate the correlation of each day,each month and whole year respectively,and then the maximum correlation coefficient and the required shift time are statistically obtained.The analysis results show that,compared with the traditional analysis method,the proposed method can dig out the potential delay correlation between sequences.As a result,the method of correlation analysis of generalized sequences,which includes wind energy,photovoltaic energy,load and etcetera,is perfected and provides more comprehensive reference information to power system for operation and dispatching.关键词
相关性分析/最优延位法/广义负荷/时间位移/高比例可再生能源/可再生能源并网Key words
correlation analysis/optimum time-delay method/generalized load/time shift/high proportion renewable energy/renewable energy grid引用本文复制引用
黎静华,梁浚杰..适合于分析广义负荷序列间相关关系的最优延位法[J].电力系统自动化,2017,41(21):17-24,8.基金项目
国家重点研发计划资助项目(2016YFB0900100) （2016YFB0900100）
国家自然科学基金资助项目(51377027).This work is supported by National Key Research and Development Program of China (No.2016YFB0900100) and National Natural Science Foundation of China(No.51377027). （51377027）
