Optimal power quality monitor placement in power systems using an adaptive quantum-inspired binary gravitational search algorithm

•An adaptive QBGSA search algorithm to solve the optimal PQ monitor placement is proposed.•The topological monitor reach area is introduced to generalize the PQ monitor placement method.•A multi-objective function is created to deal with the monitor overlapping and sag severity.•Adaptive QBGSA gives...

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Vydáno v:International journal of electrical power & energy systems Ročník 57; s. 404 - 413
Hlavní autoři: Ibrahim, Ahmad Asrul, Mohamed, Azah, Shareef, Hussain
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.05.2014
Elsevier
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ISSN:0142-0615, 1879-3517
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Shrnutí:•An adaptive QBGSA search algorithm to solve the optimal PQ monitor placement is proposed.•The topological monitor reach area is introduced to generalize the PQ monitor placement method.•A multi-objective function is created to deal with the monitor overlapping and sag severity.•Adaptive QBGSA gives the best optimal PQM placement results. This paper presents a novel adaptive quantum-inspired binary gravitational search algorithm (QBGSA) to solve the optimal power quality monitor (PQM) placement problem in power systems. In this algorithm, the standard binary gravitational search algorithm is modified by applying the concepts and principles of quantum behavior to improve the search capability with a fast convergence rate. QBGSA is integrated with an artificial immune system, which acts as an adaptive element to improve the flexibility of the algorithm toward economic capability while maintaining the quality of the solution and speed. The optimization involves multi-objective functions and handles the observability constraints determined by the concept of the topological monitor reach area. The objective functions are based on the number of required PQM, monitor overlapping index, and sag severity index. The proposed adaptive QBGSA is applied on several test systems, which include both transmission and distribution systems. To evaluate the effectiveness of the proposed adaptive QBGSA method, its performance is compared with that of the conventional binary gravitational search algorithm, binary particle swarm optimization, quantum-inspired binary particle swarm optimization, and genetic algorithm.
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ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2013.12.019