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考虑尾部分布的配电网概率谐波潮流计算方法OA

Probabilistic harmonic power flow calculation method for distribution networks considering tail distributions

中文摘要英文摘要

分布式能源的大规模接入显著增加了电力系统谐波潮流(harmonic power flow,HPF)的不确定性.传统的HPF计算方法难以准确描述系统谐波分布的尾部特征,限制了对HPF的准确评估.针对这一问题,提出了一种基于改进点估计(point estimate method,PEM)的配电网概率谐波潮流(probabilistic harmonic power flow,PHPF)计算方法.首先,利用传统潮流方法计算系统灵敏度判别指标,以识别关键谐波源.其次,使用混合采样策略,在拉丁超立方采样的基础上,对关键谐波源尾部采用重要抽样.最后,对全部样本执行确定性HPF计算,并基于核密度估计法刻画输出量的概率分布.仿真结果表明,所提方法的谐波电压幅值均值误差较传统 PEM 最大减小了约 4.5%,方差误差绝对值最大减小了约30%,并改善了最大10%的尾部分布拟合精度,验证了该方法的有效性.

The large-scale integration of distributed energy resources significantly increases the uncertainty of harmonic power flow(HPF)in power systems.The traditional HPF calculation methods struggle to accurately capture the tail characteristics of harmonic distributions,thereby limiting the accuracy of HPF evaluation.Aiming at this problem,a probabilistic harmonic power flow(PHPF)calculation method for distribution networks based on improved point estimate method(PEM)is proposed.First,the traditional power flow method is used to calculate the system sensitivity discrimination index to identify the key harmonic sources.Second,using the hybrid sampling strategy and based on the Latin hypercube sampling,importance sampling is adopted specifically for the tail regions of critical harmonic sources.Finally,deterministic HPF calculations are performed on all samples,and the probability distributions of the outputs are characterized using kernel density estimation.Simulation results show that the mean error of the harmonic voltage amplitude of the proposed method is reduced by approximately 4.5%compared to the traditional PEM,the absolute value of the variance error is reduced by about 30%,while improving the fitting accuracy of the top 10%tail distribution.These results verify the effectiveness of the proposed method.

马晓阳;曹若琳;袁泽惠

四川大学电气工程学院,四川 成都 610065四川大学电气工程学院,四川 成都 610065四川大学电气工程学院,四川 成都 610065

谐波潮流点估计拉丁超立方采样重要抽样核密度

harmonic power flowpoint estimationLatin hypercube samplingimportance samplingkernel density

《电力系统保护与控制》 2026 (9)

39-50,12

This is supported by the National Natural Science Foundation of China(No.52577128). 国家自然科学基金项目资助(52577128)

10.19783/j.cnki.pspc.251283

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