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In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO.

University of CanterburyThis paper presents a new method for an optimal measurement placement of phasor measurement units (PMUs) for power system state estimation. The proposed method considers two types of contingency conditions, i.e. single measurement loss and single branch outage, in order to obtain a reliable measurement system. Firstly, the minimum condition number of the normalized measurement matrix is used as the criteria in conjunction with the sequential elimination approach to obtain a completely determined condition. Next, a sequential addition approach is used to search for necessary candidates for single measurement loss and single branch outage conditions. These redundant measurements are optimized by the binary integer programming. Finally, in order to minimize the number of PMU placement sites, a heuristic technique to re-arrange measurement positions is also proposed. Numerical results on the IEEE test systems are demonstrated.

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This paper presents a new method for an optimal measurement placement of phasor measurement units (PMUs) for power system state estimation. The proposed method considers two types of contingency conditions (i.e., single measurement loss and single-branch outage) in order to obtain a reliable measurement system. First, the minimum condition number of the normalized measurement matrix is used as the criteria in conjunction with the sequential elimination approach to obtain a completely determined condition. Next, a sequential addition approach is used to search for necessary candidates for single measurement loss and single-branch outage conditions. These redundant measurements are optimized by binary integer programming. Finally, in order to minimize the number of PMU placement sites, a heuristic technique to rearrange measurement positions is also proposed. Numerical results on the IEEE test systems are demonstrated.Index Terms-Contingency, measurement placement, phasor measurement units (PMUs), state estimation.

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