Contingency constrained phasor measurement units placement with n − k redundancy criterion: a robust optimisation approach

• Published in: IET science, measurement & technology measurement & technology, 2018-03, Vol.12 (2), p.151-160
• Main Authors: Nikkhah, Saeid, Aghaei, Jamshid, Safarinejadian, Behrooz, Norouzi, Mohammad-Ali
• Format: Article
• Language: English
• Subjects: bilevel programming model; branch outage; contingency constrained phasor measurement unit placement; equivalent single‐level mixed‐integer programming problem; flow measurement; integer programming; linearisation process; linearisation techniques; load flow; modified 118‐bus IEEE test network; modified 2383‐bus IEEE test network; modified 7‐bus test network; nonlinear modelling; nonlinear programming; n‐k redundancy criterion; observability function; optimisation approach; phasor measurement; PMU; power flow measurement; power measurement; power system security; redundancy; Research Article; security criterion; zero injection effect
• ISSN: 1751-8822; 1751-8830; 1751-8830
• DOI: 10.1049/iet-smt.2017.0158

This study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n − k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non-linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi-level programming model is solved by its evolution to an equivalent single-level mixed-integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7-bus test network, 118- and 2383-bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases.