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All issues Volume 70 (2016) MATEC Web Conf., 70 (2016) 10013 References
Open Access
Issue
MATEC Web Conf.
Volume 70, 2016
2016 The 3rd International Conference on Manufacturing and Industrial Technologies
Article Number 10013
Number of page(s) 6
Section Electronics and Power Systems
DOI https://doi.org/10.1051/matecconf/20167010013
Published online 11 August 2016
  1. Kazmi SAA., Hasan SF., & Shin DR., “Multi Criteria Decision Analysis for Optimum DG Placement in Smart Grids”, IEEE Innovative Smart Grids Technologies Conference (Asia) (2015) [Google Scholar]
  2. Kazmi SA., Hasan SF., & Shin DR.,“Circuit Analysis Approach for Calculating Voltage Stability Index”, IEEE International Conference on Power Electronics and Drive Systems (2015) [Google Scholar]
  3. A.M. El-Zonkoly, “Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation” IET Gener. Transm. Distrib., 5, 7, pp. 760–771, (2011) [CrossRef] [Google Scholar]
  4. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., 4, pp. 1942–1948 (1995) [CrossRef] [Google Scholar]
  5. R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Machine Human Science, pp. 39–43 (1995) [Google Scholar]
  6. Reynolds C. W. Flocks, Herds, and Schools “A Distributed Behavioral Model, in Computer Graphics“, 21(4) (SIGGRAPH) pages 25–34 (1987) [Google Scholar]
  7. R. C. Eberhart and Y. Shi, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization” IEEE (2000) [Google Scholar]
  8. Shi Y. and Eberhart R.C., “A modified particle swarm optimizer”, Proceedings IEEE International Conference on Evolutionary Computation, 69–73. Piscataway, NJ: IEEE Press. (May 1998) [Google Scholar]
  9. S.M. Hakimi and S.M. Moghaddas-Tafreshi, “Optimal sizing of a stand-alone hybrid power system via particle swarm Optimization for Kahnouj area in south-east of Iran”, Renewable Energy, 34 1855–1862 (2009) [CrossRef] [Google Scholar]
  10. Ahmed Hassan, Magdi Saadawi, Mahmoud Kandil, Mohammed Saeed, “Modified particle swarm optimization technique for optimal design of small renewable energy system supplying a specific load at Mansoura University”, IET Renew. Power Genre., 9, 5, pp. 474–483 (2015) [CrossRef] [Google Scholar]
  11. Marco Gori, Alberto Tesi, “On the problem of local minima in back-propagation algorithm”, Neural Netw., 5(4) 465–471 (1992) [CrossRef] [Google Scholar]
  12. M. F. Moller, “A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning”, PB-339 Reprint, Computer science Department, University of Aaur-hus, Denmark, (1990) [Google Scholar]
  13. C.H. Chen, H. Lai, “Anempirical study of the Gradient descentand the conjugate gradient back propagation neural networks”, in: Proceedings OCEANS’ 92 Mastering the Oceans through Technology, 1, 1991, pp. 132–135 (1992) [CrossRef] [Google Scholar]
  14. Haydee Melo, Junzo Watada, “Gaussian-PSO with fuzzy reasoning based on structural learning for traininga Neura lNetwork”, Neurocomputing, 172-405–412 (2016) [CrossRef] [Google Scholar]
  15. Fernando Gaxiola, Patricia Melina, Fevrier Valdez, Juan R. Castro, Oscar Castillo, “Optimization of type-2 fuzzy weights in backpropagation learning forneural networks using GAs and PSO”, Applied Soft Computing, 38-860–871 (2016) [CrossRef] [Google Scholar]
  16. Mohammad Ali Ahmadi, “Connectionist approach estimates gas–oil relative permeability in petroleum reservoirs: Application to reservoir simulation”, Fuel, 140-429–439 (2015) [CrossRef] [Google Scholar]
  17. Mohammad-Ali Ahmadi, Mohammad Reza Ahmadi, Seyed Moein Hosseini, Mohammad Ebadi, “Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physicallogs: Application of artificial intelligence”, Journal of Petroleum Science and Engineering 123-183–200 (2014) [CrossRef] [Google Scholar]
  18. Ren-Jieh Kuoa, Man-Hsin Huangb, Wei-Che Chenga, Chih-Chieh Lincd, Yung-Hung Wue, “Application of a two-stage fuzzy neural network to a prostate cancer prognosis system”, Artificial Intelligence in Medicine 63 -119–133 (2015) [CrossRef] [Google Scholar]
  19. Rajesh K. Agrawal, Narendra G. Bawane, “Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery”, Applied Soft Computing 28217–225 (2015) [Google Scholar]
  20. Florina Rotaru, Gianfranco Chicco, Gheorghe Grigoras, Gheorghe Cartina,“Two-stage distributed generation optimal sizing with clustering-based node selection”, Electrical Power and Energy Systems 40 120–129 (2012) [CrossRef] [Google Scholar]
  21. H. Hassanzadehfard, S.M. Moghaddas-Tafreshi, S.M. Hakimi, “Optimal Sizing of an Islanded Micro-Grid for an Area in North-West Iran Using Particle Swarm Optimization Based on Reliability Concept”, World Renewable Energy Congress –Sweden (2011) [Google Scholar]
  22. S.M. Moghaddas-Tafreshi and S.M. Hakimi, “Optimal Sizing of a stand-alone Hybrid Power System via Particle Swarm Optimization (PSO)”, International Power Engineering Conference -IPEC (2007) [Google Scholar]
  23. H. Valizadeh Haghi, S. M. Hakimi, and S. M. Moghaddas Tafreshi, “Optimal Sizing of a Hybrid Power System Considering Wind Power Uncertainty Using PSO-Embedded Stochastic Simulation”, PMAPS (2010) [Google Scholar]
  24. M. Bashir and J. Sadeh, “Size Optimization of New Hybrid Stand-alone Renewable Energy System Considering a Reliability Index”, IEEE (2012) [Google Scholar]
  25. Bhimsen Tudu, Sibsankar Majumder Kamal K. Mandal, and Niladri Chakraborty, “Comparative Performance Study of Genetic Algorithm and Particle Swarm Optimization Applied on Off-grid Renewable Hybrid Energy System”, Springer-Verlag Berlin Heidelberg (2011) [Google Scholar]
  26. Y. S. Zhao, J. Zhan, Y. Zhang, D. P. Wang and B. G. Zou, “The optimal capacity configuration of an independent wind/Pv hybrid power supply system based on improved pso algorithm” (2009) [Google Scholar]
  27. Arash Navaeefard, S.M. Moghaddas Tafreshi, Mostafa Barzegari, Amir Jalali Shahrood, “Optimal Sizing of Distributed Energy Resources in Microgrid Considering Wind Energy Uncertainty with Respect to Reliability”, International Energy Conference IEEE (2010) [Google Scholar]
  28. Masoud Sharafi, Tarek Y. ELMekkawy,“Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach”, Renewable Energy 68-67–79 (2014) [CrossRef] [Google Scholar]
  29. Kaveh Khalili-Damghani, Amir-Reza Abtahi, Madjid Tavana, “A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems”, Reliability Engineering and System Safety 111-58–75 (2013) [CrossRef] [Google Scholar]
  30. Nguyen Cong Hien, Nadarajah Mithulananthan, Senior Member, IEEE, and R. C. Bansal, Senior Member, IEEE, “Location and Sizing of Distributed Generation Units for Loadabilty Enhancement in Primary Feeder”, IEEE systems journal, 7, 4, (December 2013) [CrossRef] [Google Scholar]
  31. A. M. El-Zonkoly,“Optimal placement of multi-distributed generation units including different load models using particle swarm optimization”, Swarm and Evolutionary Computation 1 50–59 (2011) [CrossRef] [Google Scholar]
  32. M. R. AlRashidi, M. F. AlHajri, “Optimal planning of multiple distributed generation sources in distribution networks: A new approach”, Energy Conversion and Management 52 3301–3308 (2011) [CrossRef] [Google Scholar]
  33. Satish Kansal, B.B.R. Sai, Barjeev Tyagi, Vishal Kumar, “Optimal placement of distributed generation in distribution networks”, International Journal of Engineering, Science and Technology, 3, 3, pp. 47–55 (2011) [Google Scholar]
  34. Satish Kansal, Vishal Kumar, Barjeev Tyagi, “Optimal placement of different type of DG sources in distribution Networks”, Electrical Power and Energy Systems 53-752–760 (2013) [CrossRef] [Google Scholar]
  35. S.P. Singh and A.R. Rao, “Optimal allocation of capacitors in distribution systems using particle swarm optimization”, Electrical Power and Energy Systems 43-1267–1275 (2012) [CrossRef] [Google Scholar]
  36. Samir Dahal, Hossein Salehfar, “Impact of distributed generators in the power loss and voltage profile of three phase unbalanced distribution network”, Electrical Power and Energy Systems 77-256–262 (2016) [CrossRef] [Google Scholar]
  37. M.H. Moradi, M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems”, Electrical Power and Energy Systems 34-66–74 (2012) [CrossRef] [Google Scholar]
  38. Satish Kansal, Vishal Kumar, Barjeev Tyagi, “Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks”, Electrical Power and Energy Systems 75-226–235 (2016) [CrossRef] [Google Scholar]
  39. Shuheng Chen, Weihao Hu, Chi Su, Xiaoxu Zhang, Zhe Chen, “Optimal reactive power and voltage control in distribution networks with distributed generators by fuzzy adaptive hybrid particle swarm optimisation method”, IET Gener. Transm. Distrib., 9, 11, pp. 1096–1103 (2015) [CrossRef] [Google Scholar]
  40. Wen Shan Tan, Mohammad Yusri Hassan, Hasimah Abdul Rahman, Md Pauzi Abdullah, Faridah Hussin,“Multi-distributed generation planning using hybrid particle swarm optimization- gravitational search algorithm including voltage rise issue”, IET Gener. Transm. Distrib., 7, 9, pp. 929–942 (2013) [CrossRef] [Google Scholar]
  41. J. J. Jamian, M. W. Mustafa, H. Mokhlis, “Optimal multiple distributed generation output through rank evolutionary particles warm optimization”, Neurocomputing 152-190–198 (2015) [CrossRef] [Google Scholar]

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