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

Ahmad Asrul Ibrahim, Azah Mohamed, Hussain Shareef

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)404-413
Number of pages10
JournalInternational Journal of Electrical Power and Energy Systems
Volume57
DOIs
Publication statusPublished - May 2014

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Power quality
Particle swarm optimization (PSO)
Immune system
Observability
Genetic algorithms
Economics

Keywords

  • Artificial immune system
  • Power quality monitor
  • Quantum-inspired binary gravitational search algorithm
  • Topological monitor reach area
  • Voltage sag assessment

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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abstract = "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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N2 - 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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