Open AccessA sequential quadratic programming method for contingency‐constrained phasor measurement unit placement
Author(s) -
Theodorakatos Nikolaos P.,
Manousakis Nikolaos M.,
Korres George N.
Publication year - 2015
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2027
Subject(s) - phasor , phasor measurement unit , mathematical optimization , integer programming , electric power system , units of measurement , matlab , observability , computer science , quadratic programming , sequential quadratic programming , linear programming , engineering , control theory (sociology) , power (physics) , mathematics , physics , control (management) , quantum mechanics , artificial intelligence , operating system
Summary The paper proposes a multi‐objective based optimization problem to design the optimal placement of phasor measurement units (PMUs), which make the power system network completely observable. The optimization process tries to attain dual objectives: (i) to minimize the total number of PMUs required and (ii) to maximize the measurement redundancy at all buses in a power system. A sequential quadratic programming algorithm is used to determine the number of PMUs and their optimal locations. Existing conventional measurements and the limited PMU channel capacity can also be incorporated in the proposed PMU placement formulation. When a system is made observable with a minimum number of PMUs, lack of communication facilities in substations or a PMU loss will lead to unobservable buses in the power system. Hence, the communication constraints and loss of a PMU have to be considered in the design stage. The proposed method is successfully applied to IEEE test systems in MATLAB, and the simulation results are presented. The simulation results are compared with a binary integer linear programming (BILP) model, also implemented in MATLAB, in order to demonstrate the effectiveness and accuracy of the proposed methodology. The comparative study shows that the proposed model yields the same number of PMUs as the optimal one found by the BILP model for each case study. The advantage of the proposed optimization scheme is that, starting from an initial point, the method is able to yield different PMU placement sets each one having the same minimum number of PMUs, for each case study. Copyright © 2014 John Wiley & Sons, Ltd.