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• W2570928967 abstract "This paper deals with the power flow (PF) and power system state estimation (PSSE) problems, which play a central role in the analysis and operation of electric power networks. The objective is to find the complex voltage at each bus of a network based on a given set of noiseless or noisy measurements. In this paper, it is assumed that at least two groups of measurements are available: (i) nodal voltage magnitudes, and (ii) one active flow per line for a subset of lines covering a spanning tree of the network. The PF feasibility problem is first cast as an optimization problem by adding a suitable quadratic objective function. Then, the semidefinite programming (SDP) relaxation technique is used to handle the inherent non-convexity of the PF problem. It is shown that as long as voltage angle differences across the lines of the network are not too large (e.g., less than 90° for lossless networks), the SDP problem finds the correct PF solution. By capitalizing on this result, a penalized convex problem is designed to solve the PSSE problem. In addition to a linear term inherited from the SDP relaxation of the PF problem, a cost based on the weighted least absolute value is incorporated in the objective for fitting noisy measurements. The optimal solution of the penalized convex problem is shown to feature a dominant rank-one component formed by lifting the true state of the system. An upper bound on the estimation error is also derived, which depends on the noise power. It is shown that the estimation error reduces as the number of measurements increases. Numerical results for the 1354-bus European system are reported to corroborate the merits of the proposed convexification framework. The mathematical framework developed in this work can be used to study the PSSE problem with other types of measurements." @default.
• W2570928967 created "2017-01-13" @default.
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• W2570928967 date "2016-12-01" @default.
• W2570928967 modified "2023-09-22" @default.
• W2570928967 title "Power system state estimation with line measurements" @default.
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• W2570928967 doi "https://doi.org/10.1109/cdc.2016.7798622" @default.
• W2570928967 hasPublicationYear "2016" @default.
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