Application of Teaching Learning-Based Optimization to the Optimal Placement of Phasor Measurement Units

Application of Teaching Learning-Based Optimization to the Optimal Placement of Phasor Measurement Units

Abdelmadjid Recioui (University of Boumerdes, Algeria)
DOI: 10.4018/978-1-5225-2990-3.ch018
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Abstract

In recent years, the placement of phasor measurement units (PMUs) in electric transmission systems has gained much attention. This chapter presents a binary teaching learning based optimization (BTLBO) algorithm for the optimal placement of phasor measurement units (PMUs). The optimal PMU placement problem is formulated to minimize the number of PMUs installation subject to full network observability at the power system buses. The efficiency of the proposed method is verified by the simulation results of IEEE14-bus, 30-bus, 57-bus-118 bus systems, respectively. The results show that the whole system can be observable with installing PMUs on less than 25% of system buses. For verification of our proposed method, the results are compared with some newly reported methods which show the method as a novel effective solution to obtain system measurements with the least number of phasor measurement units.
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Several works have been done to efficiently place phasor measurement units (PMUs) in terms of both measurement accuracy and cost effectiveness. The problem has been addressed in (Baldwin, 1993). (Phadke et al., 2008) explored the possibility of providing al. the nodes of the system with PMU’s for state estimation purpose. The problem which has been defined in (Phadke, 1993) is to determine the placement of the minimal set of PMU’s which makes the system observable. Attention has been also drawn to the use of evolutionary heuristic algorithms in optimal PMU placement. In (Nuqui and Phadke, 2005) a modified bisecting search and simulated annealing method based on topological observability have been used. In (Melosovic and Begovic, 2003), a genetic algorithm is used to find the optimal PMU locations. In (Ku and Abur, 2004) and (Xu and Abur, 2005), the authors use integer programming to find the minimum number and locations of PMUs. In (Chakrabarti and E. Kyriakides, 2007) and (Chakrabarti and E. Kyriakides, 2008) the authors propose an exhaustive search based methodology to determine the minimum number and optimal locations of PMUs for complete observability of the power system. The particle swarm optimization (PSO) technique has been used successfully in a number of power system applications (Hajian et al., 2007; Sharma and Tyagi, 2011).

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