随着新能源及负荷中电力电子装置的广泛应用,电力系统谐波畸变程度不断增加.同时,新能源及负荷显著的不确定特征,进一步增加了谐波分析难度.为有效评估系统不确定谐波状态,充分挖掘源荷实际运行特征,提出一种数据驱动的概率谐波潮流(probabilistic harmonic power flow,PHPF)计算方法.首先,基于实测数据,建立考虑时变特性的源荷动态谐波耦合矩阵模型(dynamic harmonic coupling matrix model,DHCMM),揭示不同时段内谐波电压与谐波电流的相互耦合关系.然后,利用实测数据挖掘源荷时变不确定特征,采用改进点估计法提取统计特性,克服变量间相互影响引起的估计偏差.最后,提出针对源荷不确定性的PHPF计算方法,对系统中时变不确定谐波进行评估.实验结果表明,基于实测数据的谐波耦合矩阵模型能够有效分析谐波源时变特性,结合源荷时变不确定功率状态,所提PHPF计算方法能够对电力系统谐波进行准确评估.
Data-driven Probabilistic Harmonic Power Flow Calculation Based on Source and Load Harmonic Coupling Model
With the wide application of power electronic devices in new energy resources and loads,the harmonic distortion degree in power systems is increasing.Meanwhile,the uncertainty of source and load further increases the difficulty of harmonic analysis.To effectively evaluate the harmonics and fully mine the measured data characteristics,a data-driven probabilistic harmonic power flow(PHPF)calculation considering source and load uncertainties is proposed.First,based on the measured data,the dynamic harmonic coupling matrix model(DHCMM)is established for source and load to reveal the coupling between voltage and current harmonics.Then,the time-varying uncertainty characteristics are determined with the measured data,and the statistical features are extracted by using the improved point estimation method to overcome the estimation deviation caused by the interaction between variables.Finally,a PHPF calculation method considering source and load uncertainties is proposed to evaluate time-varying uncertain harmonics in the power system.According to the analysis results,the effectiveness of data-based DHCMM is verified,and the accuracy of the proposed PHPF can accurately evaluate the harmonics based on time-varying uncertain power states.
data-drivenharmonic coupling matrix modelharmonic evaluationprobabilistic harmonic power flow(PHPF)uncertain characteristic
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