主动配电网局部不可观相关性分析与谐波源定位
Partial Unobservable Correlation Analysis and Harmonic Source Localization in Active Distribution Networks
张虹 1曹磊 1孙雷 1侯懿纯 2苏欣 3张玉海4
作者信息
- 1. 现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林 132012
- 2. 国网辽宁省电力有限公司大连供电公司,辽宁大连 116085
- 3. 国网辽宁省电力有限公司营口供电公司,辽宁营口 115000
- 4. 国网吉林供电公司,吉林 132012
- 折叠
摘要
主动配电网因量测装置不足会导致局部不可观问题,传统谐波源定位方法难以保证谐波定位的准确性.基于谐波源的稀疏性,该文采用快速稀疏贝叶斯理论解决局部不可观系统谐波源定位.通过对不可观节点进行等值,将其谐波信息附加至可观节点,建立局部不可观等值模型并进行相关性分析;通过复数域下的稀疏贝叶斯理论对等值模型进行求解,并采用基于快速边际似然的超参数迭代算法加快计算速度;通过IEEE33 节点系统进行仿真分析,结果表明所提出的方法在定位准确性和抗干扰能力方面均优于贝叶斯压缩感知算法.
Abstract
Partial unobservability may occur in active distribution networks due to insufficient measuring devices,but with the traditional harmonic source location method it is difficult to ensure the location accuracy of harmonic sources.Based on the sparsity of harmonic sources,a fast sparse Bayesian learning method is proposed to solve the harmonic sources localization for the partial unobservable system.First,by equalizing the unobserved node,the harmonic information of the unobserved node is attached to the unobserved node,and the partial unobserved equivalence model is established and the correlation analysis is carried out.Furthermore,the complex-field sparse Bayesian theory is utilized to solve the equivalence model,and the hyperparameter iteration algorithm based on fast marginal likelihood is employed to speed up the calculation.Finally,the simulation analysis on IEEE33 bus system indicates that the proposed method is superior to the Bayesian compressed sensing algorithm in location accuracy and anti-jamming ability.
关键词
局部不可观/主动配电网/相关性分析/快速稀疏贝叶斯/谐波源定位Key words
partial unobservable/active distribution network/correlation analysis/fast sparse Bayesian/harmonic source location引用本文复制引用
基金项目
吉林省科技厅自然科学基金项目(52207077)
出版年
2024
NETL
NSTL
万方数据