Intelligent Renewable Energy Systems. Группа авторов
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39 Choudhary, T.; Priyadarshi, N.; Kuma,r P.; Azam, F.; Bhoi A.K. (2020) A Fuzzy Logic Control Based Vibration Control System for Renewable Application. in Advances in Greener Energy Technologies. Springer, Singapore. 2020 https://doi.org/10.1007/978-981-15-4246-6_38
40. Priyadarshi, N.; Azam F.; Solanki, S. S.; Sharma, A.K.; Bhoi, A.K.; Almakhles, D.; A Bio-Inspired Chicken Swarm Optimization-Based Fuel Cell System for Electric Vehicle Applications. in Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903. Springer, Singapore. 2021 https://doi.org/10.1007/978-981-15-5495-7_1
41. Gandomkar M.,Vikilian M., and Ehsan M.A. (2005) A genetic-based Tabu search algorithm for optimal DG allocation in distribution systems. Electr. Power Compon. Syst. 33:1351-1362.
42. Jamian J.J., Mustafa M.W., and Mokhlis H. (2015) Optimal multiple distributed generation output through rank evolutionary particle swarm optimization, Neurocomput. 152:190-198.
43. Gomez-Gonzalez M., Lopez A., and Jurado F. (2012) Optimization of distributed generation systems using a new discrete PSO and OPF. Electr. Power Syst. Res. 84 (1): 174-180.
44. Moradi M.H., andAbedini M. (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems.Int. J. Electr. Power Energy Syst. 34(1): 66-74.
45. Abu-Mouti F.S., and El-Hawary M.E. (2011) Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm. IEEE Trans. Power Delivery. 26 (4): 2090-2101.
46. Rao R.S., Ravindra K., Satish K., and Narasimham S. (2012) Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Trans Power Syst. 28: 317–325.
47. Kollu R.,Rayapudi S.R., and Sadhu V.L.N. (2014) A novel method for optimal placement of distributed generation in distribution systems using HSDO. Int.Trans. on Electr. Energy Syst. 24(4): 547-561.
48. Nayak M.R., Dash S.K., and Rout P. (2012) Optimal Placement and Sizing of Distributed Generation in Radial Distribution System Using Different Evolution Algorithm. In. Proc. third international conference on Swarm, Evolutionary, and Memetic Computing.: 133-142.
49. Sultana S., and Roy P.K. (2014) Optimal capacitor placement in radial distribution systems using teaching learning based optimization. Int. J. Electr. Power Energy Syst. 54: 387-398.
50. Sadighizadeh M., Esmaili M., and Esmaili M. (2014) Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distributed systems. Energy 76: 920-930.
51. Doagou-Mojarrad H., Gharehpetian G., Rastegar H., and Olamaei J. (2013) Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy 54: 129–138.
52. Aman M.M.,Jasmon G.B., Bakar A.H.A., and Mokhlis H. (2014) A new approach for optimum simultaneous multi-DG distributed generation units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm. Energy 66: 202-215.
53. Singh A.K., and Parida S.K. (2015) Allocation of distributed generation using proposed DMSP approach based on utility and consumers aspects under deregulated environment. Int. J. Electr. Power Energy Syst. 68: 159-169.
54. Shaaban M.F., Atwa Y.M., and El-Saadany E.F. (2013) DG allocation for benefit maximization in distribution networks. IEEE Trans. Power Syst. 28(2): 639–649.
55. Ettchadi M.,Ghasemi H., and Vaez-Zedah S. (2013) Voltage stability-based DG placement in distribution network. IEEE Trans. Power Delivery 28(1):171-178.
56. Karatepe E.,Ugrandi F., and Hiyama T. (2015) Comparison of single and multiple-distributed generation concepts in terms of power loss, voltage profile and line flows under uncertainty scenarios. Renew. Sustain. Energy Rev. 48: 317-327.
57. Arefifar S.A., Mohamed Y.A.I., El-Fouly T.H.M. (2012) Supply-adequacybased optimal construction of micro grids in smart distribution systems. IEEE Trans. Smart Grids 3(3):1491–1502.
58. Singh R.K., and Goswami S.K. (2010) Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction and voltage improvement including voltage rise issue. Int. J. Electr. Power Energy Syst. 32(6): 637-644.
59. Prenc R., Skrlec D., and Komen V. (2013) Distributed generation allocation based on average daily load and power production curves. Int. J. Electr. Power Energy Syst. 53: 612-622.
60. Gampa S.R., and Das D. (2015) Optimum placement and sizing of DGs considering average hourly variation of loads. Int. J. Electr. Power Energy Syst. 66:25-40.
61. Jamil M., and Anees A.S. (2016) Optimal sizing and location of SPV (solar photovoltaic) based MLDG (multiple location distributed generator) in distribution system for loss reduction, voltage profile improvement with economical benefits. Energy 103:231-239.
62. Garcia J.A.M., and Mena A.J.G. (2013) Optimal distributed generation location and sizing using a modified teaching-learning based optimization algorithm. Int. J. Electr. Power Energy Syst. 50: 65-75.
63. Sultana U., Khairuddin A.B., Mokhtar A.S., Zareen N., and Sultana B. (2016) Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system. Energy 111:525-536.
64. Devi S., and Geethanjali M. (2014) Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation. Expert Syst. Appl. 41(6):2772-2781.
65. Das B., Mukherjee V., and Das D. (2016) DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization. Appl. Soft Comput. 49:920-936.
66. Das B., Mukherjee V., and Das D. (2020) Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Adv. Engg. Soft. 146: 102804.
67. Sugantham P.N, Hansen N., Liang J.J., Deb K., Chen Y.P., Auger A., Tiwari S. (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Nanyang Technol. Univ., Singapore, Tech. Rep. KanGAL #2005005, IIT Kanpur, India.
68. Barik S., Das D., and Bansal R. C. (2020) DG investment and allocation in active distribution networks, in Uncertainties in Modern Power Systems, Editor-Ahmed F. Zobaa, Shady H.E. Abdel Aleem, Academic Press, Elsevier, pp. 343-394.
69. Clerc, Maurice (2010) Particle swarm optimization. John Wiley & Sons 93.
70. Rao, R. Venkata (2016) Teaching-learning-based optimization algorithm. In Teaching learning based optimization algorithm, Springer, Cham, pp. 9-39.
71. Liao, Tianjun, Daniel Molina, Marco A. Montes de Oca, and Thomas Stützle (2014) A note on bound constraints handling for the IEEE CEC’05 benchmark function suite. Evolutionary computation 22(2): 351-359.
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