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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

Postal Subscription Code 80-972

2018 Impact Factor: 1.701

Front. Energy    2014, Vol. 8 Issue (2) : 254-260    https://doi.org/10.1007/s11708-014-0302-1
RESEARCH ARTICLE
Novel quantum-inspired firefly algorithm for optimal power quality monitor placement
Ling Ai WONG(), Hussain SHAREEF, Azah MOHAMED, Ahmad Asrul IBRAHIM
Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
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Abstract

The application of a quantum-inspired firefly algorithm was introduced to obtain optimal power quality monitor placement in a power system. The conventional binary firefly algorithm was modified by using quantum principles to attain a faster convergence rate that can improve system performance and to avoid premature convergence. In the optimization process, a multi-objective function was used with the system observability constraint, which is determined via the topological monitor reach area concept. The multi-objective function comprises three functions: number of required monitors, monitor overlapping index, and sag severity index. The effectiveness of the proposed method was verified by applying the algorithm to an IEEE 118-bus transmission system and by comparing the algorithm with others of its kind.

Keywords quantum-inspired binary firefly algorithm      topological monitor reach area      power quality     
Corresponding Author(s): Ling Ai WONG   
Issue Date: 19 May 2014
 Cite this article:   
Ling Ai WONG,Hussain SHAREEF,Azah MOHAMED, et al. Novel quantum-inspired firefly algorithm for optimal power quality monitor placement[J]. Front. Energy, 2014, 8(2): 254-260.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-014-0302-1
https://academic.hep.com.cn/fie/EN/Y2014/V8/I2/254
Fig.1  Flowchart of firefly algorithm
Method Quality (fitness) Convergence (iterations) Computational time/s
Best Average Worst Best Average Worst Best Average Worst
BFA 217.68 311.54 363.37 9 107.73 189 2.95 3.03 3.31
QBGSA 26.32 28.97 30.95 60 154.8 200 42.92 47.38 51.83
QBFA 26.26 28.97 31.06 43 101.43 186 31.39 32.53 33.75
Tab.1  Performance of BFA, QBGSA, and QBFA in obtaining optimal PQM placement solution for a 118-bus system
Fig.2  Convergence characteristics of the QBFA, QBGSA and BFA for a 118-bus case study
Fig.3  Optimal location of PQM in a 118-bus power system based on different algorithms
Go Initial gravity constant
Kbest Best applying force
r Cartesian distance between two fireflies
x Coordinate of firefly
alpha Randomization parameter
αc Monitor coverage control parameter
βo Attractiveness at r= 0
α Q-bit individual string
β Q-bit individual string
γ Light absorption coefficient
θ Rotation angle
  
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