Abstract
By the employment of quantum mechanics, this paper proposes a Multi-Stage Accelerated Quantum Particle Swarm Optimization (MSAQPSO) to maximize savings due to electrical power losses reduction, which is subject to bus voltage constraints. The methodology incorporates Static Var Compensators (SVCs) integrated in a power system. The optimization problem is solved using a novel algorithm which consist of two stages. The first stage or the outer layer determines the optimum number, sizing, and placement of the SVCs. The second stage or the inner layer determines the optimum operation of the SVCs. The results reveal that the approach is feasible, and the optimization turns out to be fast and robust in comparison to the classical Particle Swarm Optimization (PSO).
Original language | English |
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Title of host publication | 2018 IEEE 3rd Ecuador Technical Chapters Meeting, ETCM 2018 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISBN (Electronic) | 9781538666579 |
DOIs | |
Publication status | Published - 17 Dec 2018 |
Event | 3rd IEEE Ecuador Technical Chapters Meeting, ETCM 2018 - Cuenca, Ecuador Duration: 15 Oct 2018 → 19 Oct 2018 |
Publication series
Name | 2018 IEEE 3rd Ecuador Technical Chapters Meeting, ETCM 2018 |
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Conference
Conference | 3rd IEEE Ecuador Technical Chapters Meeting, ETCM 2018 |
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Country/Territory | Ecuador |
City | Cuenca |
Period | 15/10/18 → 19/10/18 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This study was supported by the Walter Valdano Raffo II program in Escuela Superior Politécnica del Litoral (ESPOL) and the Secretariat of Higher Education, Science, Technology and Innovation of the Republic of Ecuador (Senescyt).
Publisher Copyright:
© 2018 IEEE.
Keywords
- multi-stage accelerated quantum particle swarm optimization
- power losses
- static var compensator
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Energy Engineering and Power Technology
- Biomedical Engineering
- Electrical and Electronic Engineering
- Control and Optimization