Simulated annealing optimization algorithm for power systems quality analysis

Soliman, S. A.; Mantaway, A.H.; El-Hawary, M.E.

doi:10.1016/s0142-0615(03)00068-1

Cited by 22 publications

(3 citation statements)

References 19 publications

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“…SA was applied to evaluate harmonics and frequency for power system quality analysis and frequency relaying (Soliman et al, 2004). The sum of the squares of errors is the objective function to be minimized for evaluating the amplitude and phase angle of each harmonic component as well as the fundamental frequency of the voltage signal.…”

Section: The Other Applicationsmentioning

confidence: 99%

Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Huang¹,

Sun²,

Huang³

2012

Simulated Annealing - Advances, Applications and Hybridizations

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Section: The Other Applicationsmentioning

confidence: 99%

Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Huang¹,

Sun²,

Huang³

2012

Simulated Annealing - Advances, Applications and Hybridizations

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“…Furthermore, another function num, which is the number of baselines, is taken into account. The appropriate "cooling schedule" of simulated annealing governs the convergence of the algorithm [10]. The parameters of the cooling schedule are [11]: initial temperature T 0 , cooling rate α, terminate temperature T end , and a finite length of each homogenous Markov chain L k .…”

Section: Optimization Strategymentioning

confidence: 99%

Optimization of Fourier Plane Coverage of Antenna Arrays for SPORT

Sun¹,

He²,

Wu³

2005

PIERS Online

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The fidelity of image reconstruction of SPORT depends on samplings in the Fourier transform domain of the scene performed by its antennas array. Research in this paper aims at finding the optimal layout of the receiving elements which is fitting for SPORT. Simulated annealing is applied to finding solutions for N = 8, 10, 12, 14. The simulation results show that centers of gravity of optimized antenna arrays are situated on center of the circle. Spatial frequency domain is covered evenly by the sampling points after rotation of the optimized antenna array. Moreover, the redundancy of baselines is minimized. The length values of the baselines of the optimized antenna array are also evenly distributed on the interval [0, 1].

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“…Besides the above digital techniques, some of the intelligent techniques like Artificial Neural Network (ANN), Expert system (ES) has been applied for amplitude, phase and frequency estimation of distorted power signals (Kandil, Sood, Khorasani, & Patel, 1992;Martins, Oliveira, & Goncalves, 2000;Mori, 1992;Osowski, 1992). Soft computing techniques like Genetic Algorithm (GA) (El-Naggar & AL-Hasawi, 2006;El-Zonkoly, 2005), Simulated Annealing (SA) (Soliman, Mantaway, & El-Hawary, 2004) have also been applied for power quality analysis. A new algo-rithm integrating both least square and GA has been reported in (Bettayeb & Qidwai, 2003), where the advantages of both least square and GA have been combined for harmonic estimation.…”

mentioning

confidence: 99%

Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals

Pandi

Panigrahi

2010

International Journal of Applied Evolutionary Computation

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Recently utilities and end users become more concerned about power quality issues because the load equipments are more sensitive to various power quality disturbances, such as harmonics and voltage fluctuation. Harmonic distortion and voltage flicker are the major causes in growing concern about electric power quality. Power quality disturbance monitoring plays an important role in the deregulated power market scenario due to competitiveness among the utilities. This paper presents an evolutionary algorithm approach based on Adaptive Particle Swarm Optimization (APSO) to determine the amplitude, phase and frequency of a power quality signal. In this APSO algorithm the time varying inertia weight is modified as rank based, and re-initialization is used to increase the diversity. In this paper, to the authors highlight the efficacy of different evolutionary optimization techniques like classical PSO, Constriction based PSO, Clonal Algorithm (CLONALOG), Adaptive Bacterial Foraging (ABF) and the proposed Adaptive Particle Swarm Optimization (APSO) to extract different parameters like amplitude, phase and frequency of harmonic distorted power quality signal and voltage flicker.

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Simulated annealing optimization algorithm for power systems quality analysis

Cited by 22 publications

References 19 publications

Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Optimization of Fourier Plane Coverage of Antenna Arrays for SPORT

Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals

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