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The Phasor Measurement Unit (PMU) is a monitoring device capable with high-precision time synchronization that measures magnitudes and phase angle of voltage and current from a specific power network bus. Aiming at achieving the optimal PMU positioning problem (OPP) solving, this study proposes a multi-objective optimization function minimized under a set of 0-1 Boolean observability inequality constraints whereas a binary restriction is satisfied. Genetic algorithms (GAs) in conjunction with a Binary Particle Swarm Optimization (BPSO) are used to solve the multi-objective constraint linear integer program (m-CILP) towards optimality. This GA-PSO probabilistic approach aims simultaneously at minimizing the number of PMUs needed and maximizing the measurement redundancy (MR) index. The success of this heuristic approach is proved by comparing its results with those achieved by a Branch-and-Bound (B&B) algorithm which solves the 0-1 m-CILP towards optimality. The minimization models are tested on standard IEEE power networks to show their applicability regarding this multi-objective optimization making problem. The numerical results derived by the GA-PSO models in MATLAB language show that the optimal solutions with regard to the maximum MR are achieved with efficiency and at once minimizing the whole cost in the optimization process. [ABSTRACT FROM AUTHOR]

Fault location observability is an essential procedure among smart monitoring and outage management system issues used to realize self-healing networks, one of the most important characteristics of modern power systems. Further, the determination of the location of a faulty transmission line in a power grid is a vital topic to facilitate the self-healing network and to maintain the continuity of power supply. The self-healing procedure is to constantly keep the energy supplies to the customers in case of a power system configuration change such as a fault event happening on an electrical power transmission network. Self-healing is an operational aspect of electrical power networks for real-time identification and localization of faulty transmission lines for the whole power network. The implementation of PMUs as an accurate measurement device is a promising key entry for fault location and clearing utilizations. This paper proposes algorithmic models that determine the optimal number of PMUs and their placement sites to achieve a fault observable system whereas the state-estimation issue is examined. The optimization models discussed herein find a configuration of PMU locations with a least number of devices that locates any fault occurring in a transmission power network. An optimization process is presented within a nonlinear programming model coupling a synchronized phasor as well as conventional measurements. As a comparative optimization tool, a zero-one integer constraint linear program is implemented that satisfies global optimality with sufficient precision. Evolutionary algorithms such as a binary-particle swarm optimization and genetic algorithms have been adopted to characterize a fault observable power system. The algorithmic models are illustrated with an IEEE-14 bus system. Simulation results are tested on different size power systems to verify the efficacy of the proposed algorithmic approach without taking into account the fault classification, pre-fault loading conditions or considering the fault resistance. We present that this wide-area measuring implementation and its numerical results is an outstanding approach that improves the state-of-the-art in measurement driven methods regarding the finding of an optimal PMU set solution to pinpoint a fault event occurring anywhere in the power transmission grid. As proved via the numerical optimization, the mathematical and evolutionary algorithms are efficient and robust in finding some new benchmark optimal results for faulty conditions occurring in a modern power grid for any IEEE power system and case study. [ABSTRACT FROM AUTHOR]

This paper studies mathematical algorithms for delivery maximum power network observability using synchronized measurements. This problem is an intrinsic extension of the minimization of phasor measurement units (PMUs) by considering the least cost number of these devices around the power network and maximizing the reliability for better performance of power electrical networks. Optimization properties are investigated and used to lie a Βranch-and-Βound algorithm (BBA) jointly with Successive Quadratic Programming (SQP) and Interior Point Methods (IPMs) to find optimality. Τo evaluate optimal solutions with maximum measurement redundancy, solution algorithms such as BBA, SQP, IPM are used. The nonlinear algorithms mainly rely on local search procedure as the convergence indicator to optimality, whereas BBA is implemented to build a binary-tree to find an optimal solution. Thus, it is reasonable and necessary to compare nonlinear algorithms with BBA to show their difference and their efficiency convergence properties towards global optimality whereas the maximum observability is preserved. Moreover, the polyhedron being constructed for the implementation of the BBA scheme is transformed into a polytope solved by a Semi-Define-Programming approach to find appropriate optimal solutions for further optimization study. Numerical studies show significant improvement about the maximum observability over existing optimization schemes already published in the recent bibliography. [ABSTRACT FROM AUTHOR]