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Abstrakty
The notion of Distributed Generation (DG) refers to the production of power at the level of consumption. Production of energy on-site, instead of offering it centrally, reduces the cost, internal dependencies, difficulties, inefficiencies, and risks that are related to transmission and distribution systems. In case DG is realized with fuel cells, several issues exist in respect to allocating and sizing of these fuel cells in the system. For solving those issues, dual stage intelligent technique is employed in this paper. First, the Neural Networks (NN) technique is adopted for determining the required location to place the fuel cells. Secondly, an enhanced version of Self Improved Fire-Fly (SIFF) algorithm is adopted for finding the optimal size of the fuel cells. The implemented technique is simulated in four IEEE benchmark test bus systems, and the respective performance analysis along with statistical analysis serves for validation purposes. The here proposed technique is compared with six other known algorithms, namely Particle Swarm Optimization (PSO), Firefly (FF) algorithm, Artificial Bee colony (ABC) algorithm, Improved Artificial Bee colony algorithm (IABC), Genetic Algorithm (GA) and Global Search Optimizer (GSO). The results obtained from the comparative analysis show the enhanced performance of the proposed mechanism.
Czasopismo
Rocznik
Tom
Strony
357--381
Opis fizyczny
Bibliogr. 30 poz., rys.
Twórcy
autor
- Department of EEE, JNTU College of Engineering, Hyderabad, India
autor
- Department of EEE, JNTU College of Engineering, Hyderabad, India
autor
- T.K.R. Engineering College, Meerpet, Hyderabad, India
Bibliografia
- [1] Aghtaie, M. and Dehghanian, P. (2011) Optimal Distributed Generation Placement in a Restructured Environment via a Multi-Objective Optimization Approach. IE E Power Engineering Society Summer Meeting, 1–6.
- [2] Arya, L. and Koshti, S. C. A. (2012) Distributed generation planning using differential evolution accounting voltage stability consideration. Int. J. Elect. Power Energy Syst., 42(1), 196–207.
- [3] Basu, M. (2015) Group search optimization for economic fuel scheduling. International Journal of Electrical Power & Energy Systems, 64, 894-901.
- [4] Brown, R. E., Pan, J., Feng, X. and Koutlev, K. (2001) Siting distribution generation to defer T&D expansion. IEEE transmission and distribution conference and exposition, IEEE, 2, 622-627.
- [5] Cali, G. and Pillow, F. (2001) Optimal distributed generation allocation in MV distribution networks. IEEE power engineering society international conference on power industry computer applications, IEEE, 81-86.
- [6] Chakravorty, M. and Das, D. (2001) Voltage stability analysis of radial distribution networks. International Journal of Electrical Power & Energy Systems, 23(2), 129–135. doi:10.1016/s0142-0615(00)00040-5
- [7] Chen, Y., Li, L., Peng, H., Xiao, J. and Shi, Y. (2017) Particle Swarm Optimizer with two differential mutation. Applied Soft Computing, http: //dx.doi.org/10.1016/j.asoc.2017.07.020
- [8] Fister, I., Fister Jr. I., Yang, X.-S. and Brest, J. (2013) A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34-46.
- [9] Foliage, H. and Haghifam, M. (2007) ACO Based algorithm for distributed generationsources allocation and sizing in distribution systems. IEEE Lausanne Power Tech, 555–560.
- [10] Gandomkar, M., Vakiliyan, M. and Ehsan, M. (2005) A combination of genetic algorithm and simulated annealing for optimal DG allocation in distribution networks. Canada Conference on Electrical and Computer Engineering, Saskatoon, May, 645–648.
- [11] Kansal, S., Kumar, V. and Tyagi, B. (2016) Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks. International Journal of Electrical Power and Energy Systems, 75, 226– 235.
- [12] Kobayashi, M. (2017) Uniqueness theorem for quaternionic neural networks. Signal Processing, 136, 102-106.
- [13] Lee, Y-J.., Rhee, S.-B., Lee, S.-K. and You, S.K. (2002) Dispersed generator placement using fuzzy-ga in distribution systems. IEEE Power engineering society summer meeting, Chicago, USA, IEEE, 3, 1148–1153.
- [14] Mart´ınez-Ca˜nada, P., Morillas, C., Plesser, H. E., Romero, S. and Pelayo, F. (2017) Genetic algorithm for optimization of models of the early stages in the visual system. Neurocomputing, 250, 101-108.
- [15] Mendez, V. H., Rivier, J., Fuente, J. I. D. L., Gomez, T., Arceluz, J., Marin, J. et al. (2006) Impact of distributed generation on distribution investment deferral. Int J Elect Power Energy Syst, 28(4): 244–52.
- [16] Mohandas, N., Balamurugan, R. and Lakshminarasimman, L. (2015) Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos. International Journal of Electrical Power and Energy Systems, 66, 41–52.
- [17] Mojarrad, H. D., Gharehpetian, G. B., 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.
- [18] Moradi, M. H. and Abedini, M. (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Electric Power Energy Syst., 34(1): 66–74.
- [19] Moradi, M. H., Tousi, S. M. R. and Abedini, M. (2014) Multi-objective PFDE algorithm for solving the optimal siting and sizing problem of multiple DG sources. International Journal of Electrical Power and Energy Systems, 56, 117–126.
- [20] Murty, V. V. S. N. and Kumar, A. (2015) Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth. International Journal of Electrical Power and Energy Systems, 69, 246–256.
- [21] Poornazaryan, B., Karimyan, P., Gharehpetian, G. B. and Abedi, M. (2016) Optimal allocation and sizing of DG units considering voltage stability, losses and load variations. International Journal of Electrical Power and Energy Systems, 79, 42–52.
- [22] Raj, P. A. D. V., Senthilkumar, S., Raja, J., Ravichandran, S. and Palanivelu, T.G. (2008) Optimization of distributed generation capacity for line loss reduction and voltage profile improvement using PSO. Elektrika: Journal of Electrical Engineering, 1, 41-48.
- [23] Rao, T. C. S., Ram, S. S. T. and Subrahmanyam, J. B. V. (2017) An effective technique for fault detection and classification in distribution system with the aid of DWT and ANFIS. International Journal of Automation and Control, 11(4): 411-427.
- [24] Rao, T. C. S., Ram, S. S. T. and Subrahmanyam, J. B. V. (2018) Fault Signal Recognition in Power Distribution System using Deep Belief Network. Journal of Intelligent Systems. DOI: https://doi.org/10.1515/jisys- 2017 0499.
- [25] Siemens, A.G. (2015) Planning of Electric Power Distribution Technical Principles, Energy Management. Medium voltage and systems.
- [26] Subramanyam, T. C., Ram, S. S. T. and Subrahmanyam, J. B. V. (2018) Dual stage approach for optimal sizing and siting of fuel cell in distributed generation systems. Computers & Electrical Engineering, 69, 676-689.
- [27] Subramanyam, T. C., Ram, S. S. T. and Subrahmanyam, J. B. V. (2016) Optimal Location for Fixing Fuel Cells in a Distributed Generation Environment using Hybrid Technique. International Journal on Electrical Engineering and Informatics, 8(3), 567-587.
- [28] Subramanyam, T. C., Ram, S. S. T. and Subrahmanyam, J. B. V. (2018) Optimal Placement and Sizing of DG in a Distributed Generation Environment with Comparison of Different Techniques. Artificial Intelligence and Evolutionary Computations in Engineering Systems, Advances in Intelligent Systems and Computing 668, Springer Nature Singapore Pte Ltd.
- [29] Vaziri, M., Vadhva, S., Oneal, T. and Johnson, M. (2011) Distributed generation issues, and standards. IEEE International conference on IRI, Las Vegas, IEEE, 439-443.
- [30] Yang, X.-S. (2009) Firefly algorithms for multimodal optimization. In: Stochastic Algorithms: Foundations and Applications, SAGA 2009. Lecture Notes in Computer Sciences, 5792, 169-178.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4f151af8-8686-42f4-a128-84de299e357f