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通过基变换将状态变量表示在稀疏基上,将状态估计问题转化为压缩感知恢复问题,并通过最小绝对偏差估计和正交匹配追踪法求解,同时进行了仿真,以验证在这种情况下稀疏解估计的优越性,并给出了最小测量值和稀疏度的具体值.
Abstract：By applying basis transformation,the state variables are represented on a sparse basis.The state estimation problem is transformed into a compressed sensing recovery problem.This problem is subsequently solved using the Least Absolute Deviation(LAD) estimation and the Orthogonal Matching Pursuit(OMP) algorithm.Furthermore,simulations are conducted to verify the superiority of the sparse solution estimation under this framework.The specific numerical values of the minimum number of measurements and the sparsity are provided.
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基本信息:
DOI：10.16119/j.cnki.issn1671-6876.2026.03.002
中图分类号:TM76
引用信息:
[1]崔俊峰,陶莉.压缩感知恢复电表测量数据缺失下的智能电网状态估计[J].淮阴师范学院学报(自然科学版),2026,25(03):195-200.DOI:10.16119/j.cnki.issn1671-6876.2026.03.002.
基金信息:
江苏省社科应用研究精品工程重点课题（24SYA-046）
2026-09-15
2026-09-15