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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2014 / Cilt: 22 - Sayı: 5

Comparative learning global particle swarm optimization for optimal distributed generations' output

Jasrul Jamani JAMIAN
Hazlie MOKHLIS
Mohd Wazir MUSTAFA
Mohd Noor ABDULLAH
Muhammad Arif BAHARUDIN
Pages
1323–1337
Publication Date
2014-05-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network.
📊
Key Findings & Data:Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value.
💡
Conclusion & Impact:Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Distributed generator · particle swarm optimization · power loss reduction

Abstract

The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.

Özet

The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.

🔖

Cite This Article

JAMIAN, J. J., MOKHLIS, H., MUSTAFA, M. W., ABDULLAH, M. N., BAHARUDIN, M. A. (2014). Comparative learning global particle swarm optimization for optimal distributed generations' output. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 22 - Sayı: 5, 1323–1337. https://ajindex.com/articles/188521

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References (11)

  1. M.R. Haghifam, O.P. Malik, “Genetic algorithm-based approach for fixed and switchable capacitors placement in distribution systems with uncertainty and time varying loads”, IET Generation, Transmission & Distribution, Vol. 1, pp. 244–252, 2007.
  2. B. Venkatesh, R. Ranjan, H.B. Gooi, “Optimal reconfiguration of radial distribution systems to maximize loadabil- ity”, IEEE Transactions on Power Systems, Vol. 19, pp. 260–266, 2004.
  3. C.T. Su, C.F. Chang, J.P. Chiou, “Distribution network reconfiguration for loss reduction by ant colony search algorithm”, Electric Power Systems Research, Vol. 75, pp. 190–199, 2005.
  4. E.J. De Oliveira, J.W. Marangon Lima, K.C. De Almeida, “Allocation of FACTS devices in hydrothermal systems”, IEEE Transactions on Power Systems, Vol. 15, pp. 276–282, 2000.
  5. M.M. El Metwally, A.A. El Emary, F.M. El Bendary, M.I. Mosaad, “Optimal allocation of FACTS devices in power system using genetic algorithms”, 12th International Middle-East in Power System Conference, pp. 1–4, 2008.
  6. G. Carpinelli, C. Noce, D. Proto, A. Russo, P. Varilone, “Single-objective probabilistic optimal allocation of capacitors in unbalanced distribution systems”, Electric Power Systems Research, Vol. 87, pp. 47–57, 2012.
  7. G. Carpinelli, F. Mottola, D. Proto, A. Russo, “Optimal allocation of dispersed generators, capacitors and dis- tributed energy storage systems in distribution networks”, Proceedings of the International Symposium in Modern Electric Power Systems, pp. 1–6, 2010.
  8. S. Ghosh, S.P. Ghoshal, S. Ghosh, “Optimal sizing and placement of distributed generation in a network system”, International Journal of Electrical Power & Energy Systems, Vol. 32, pp. 849–856, 2010.
  9. S.G.B. Dasan, S.S. Ramalakshmi, R.P.K. Devi, “Optimal siting and sizing of hybrid Distributed Generation using EP”, International Conference on Power Systems, pp. 1–6, 2009.
  10. S.M.H. Nabavi, S. Hajforoosh, M.A.S. Masoum, “Placement and sizing of distributed generation units for congestion management and improvement of voltage profile using particle swarm optimization”, IEEE PES in Innovative Smart Grid Technologies Asia, pp. 1–6, 2011. T. G¨ozel, M.H. Hocaoglu, “An analytical method for the sizing and siting of distributed generators in radial systems”, Electric Power Systems Research, Vol. 79, pp. 912–918, 2009.
  11. E. Haesen, J. Driesen, R. Belmans, “A long-term multi-objective planning tool for distributed energy resources”, IEEE PES in Power Systems Conference and Exposition, pp. 741–747, 2009. M.H. Moradi