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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2019

05.07.2017 | Original Article

Optimal position and rating of DG in distribution networks by ABC–CS from load flow solutions illustrated by fuzzy-PSO

verfasst von: Rajni Bala, Smarajit Ghosh

Erschienen in: Neural Computing and Applications | Ausgabe 2/2019

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Abstract

In this work, load flow problems of both radial distribution networks (RDNs) and mesh distribution networks (MDNs) have been solved using hybrid fuzzy-PSO algorithm. A new voltage stability index (VSI) is also indicated. Based on the suggested load flow, distributed generation (DG) is ready to conduct through the requirement; and with the support of inserting the optimal-sized DG unit in an exact way, the distribution system’s stability is also studied. The exact position of each DG unit has been computed using “loss sensitivity analysis,” whereas the optimal sizing of each DG unit has been done with the help of hybrid artificial bee colony and Cuckoo search algorithm. The suggested method is tested in the regular 33-node and 69-node RDNs as well as in 85-node and 119-node MDNs. The transcendence of the proposed operation has been centered with the aid of comparison to the other existing methods. The suggested VSI is also correlated with other two existing VSIs before and after placement of DG unit(s).
Vorheriger Artikel Traffic identification method based on multiple probabilistic neural network model
Nächster Artikel Three-dimensional neural network tracking control of a moving target by underactuated autonomous underwater vehicles

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Literatur
1.
Chiang HD, Flueck AJ, Shah KS, Balu N (1995) CPFLOW: a practical tool for tracing power system steady-state stationary behavior due to load and generation variations. IEEE Trans Power Syst 10:623–624CrossRef
2.
Schavemaker P, Sluis LVD (2008) Electrical power system essentials. Wiley, New york
3.
Lopes JAP, Hatziargyriou N, Mutale J, Djapic P, Jenkins N (2007) Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr Power Syst Res 77:1189–1203CrossRef
4.
Verma KS, Singh SN, Gupta HO (2001) Location of unified power flow controller for congestion management. Electr Power Syst Res 58:89–96CrossRef
5.
Wood AJ, Wollenberg BF (2012) Power generation, operation, and control. Wiley, New York
6.
Low SH (2013) Convex relaxation of optimal power flow. In: IEEE symposium on bulk power system dynamics and control-ix optimization, security and control of the emerging power grid (IREP)
7.
Chen Y, Das A, Qin W, Sivasubramaniam A, Wang Q, Gautam N (2005) Managing server energy and operational costs in hosting centers. ACM SIGMETRICS Perform Evals Rev 33:303–314CrossRef
8.
Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids. IEEE Power Energy Mag 5:78–94CrossRef
9.
Sadat H (2002) Power system analysis. Tata McGraw Hill Publishing Ltd, India
10.
Teng JH (2002) A modified Gauss–Seidel algorithm of three-phase power flow analysis in distribution networks. Electr Power Energy Syst Res 24:97–102CrossRef
11.
Prakash K, Sydulu M (2008) An effective topological and primitive impedance based distribution load flow method for radial distribution systems. In: IEEE international conference on electric utility deregulation and restructuring and power technologies, pp 1044–1049
12.
Nyns KC, Haesen E, Driesen J (2010) The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25:371–380CrossRef
13.
Carrasco JM, Franquelo LG, Bialasiewicz JT, Galván E, Guisado RCP, Prats MAM, León JL, Moreno-Alfonso N (2006) Power-electronic systems for the grid integration of renewable energy sources. IEEE Trans Ind Electron 53:1002–1016CrossRef
14.
Hung DQ, Mithulananthan N, Bansal RC (2010) Analytical Expressions for DG allocation in primary distribution networks. IEEE Trans Energy Convers 25:814–820CrossRef
15.
García JAM, Mena AJG (2013) Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm. Int J Electr Power Energy Syst 50:65–75CrossRef
16.
Marti JR, Ahmadi H, Bashualdo L (2013) Linear power-flow formulation based on a voltage-dependent load model. IEEE Trans Power Deliv 28:1682–1690CrossRef
17.
Jia H, Qi W, Liu Z, Wang B, Zeng Y, Xu T (2015) Hierarchical risk assessment of transmission system considering the influence of active distribution network. IEEE Trans Power Syst 30:1084–1093CrossRef
18.
Carpinelli G, Caramia P, Varilone P (2015) Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems. Renew Energy 76:283–295CrossRef
19.
Das P (2015) Optimal sizing and placement of distributed generation in a radial distribution system using loss sensitivity factor and firefly algorithm. Int J Sci Res Manag 3(4):2611–2618
20.
Mohamed IA, Kowsalya M (2014) Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evolut Comput 15:58–65CrossRef
21.
Murty VVSN, Kumar A (2015) Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth. Int J Electr Power Energy Syst 69:246–256CrossRef
22.
Hung DQ, Mithulananthan N (2013) Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Energy Convers 60:1700–1708
23.
Kaur S, Kumbhar G, Sharma J (2014) A MINLP technique for optimal placement of multiple DG units in distribution systems. Int J Electr Power Energy Syst 63:609–617CrossRef
24.
Viral R, Khatod DK (2015) An analytical approach for sizing and siting of DGs in balanced radial distribution networks. Int J Electr Power Energy Syst 67:191–201CrossRef
25.
Kansal S, Kumar V, Tyagi B (2016) Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks. Int J Electr Power Energy Syst 75:226–235CrossRef
26.
Saha S, Mukherjee V (2016) Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm. Proc IET (GTD) 10(14):3671–3680
27.
Das B, Mukherjee V, Das D (2016) DG placement in radial distribution network by symbiotic organism search algorithm for real power loss reduction. Appl Soft Comput 49:920–936CrossRef
28.
Chakravorty M, Das D (2001) Voltage stability analysis of radial distribution networks. Int J Electr Power Energy Syst 23:129–135CrossRef
29.
Eminoglu U, Hocaoglu MH (2009) A network topology-based voltage stability index for radial distribution networks. Int J Electr Power Energy Syst 29:131–143
30.
Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180–184
31.
Prakash K, Sydulu M (2007) Particle swarm optimization based capacitor placement on radial distribution system. In: Proceedings of the IEEE power engineering society general meeting, pp 1–5
32.
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652–657CrossRef
33.
Gandomi AH, Xin-She Y, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Int J Eng Comput 29:17–35
34.
Elgerd IO (1971) Electric energy system theory: an introduction. McGrawHill, New York
35.
Ghosh S, Das D (1999) Method for load-flow solution of radial distribution networks. IEE Proc Gener Trans Distrib 146:641–648CrossRef
36.
Augugliaro A, Dusonchet L, Favuzza S, Ippolito MG, Sanseverino ER (2010) A backward sweep method for power flow solution in distribution networks. Int J Electr Power Energy Syst 32:271–280CrossRefMATH
37.
Ghasemi S, Moshtagh J (2013) Radial distribution systems reconfiguration considering power losses cost and damage cost due to power supply interruption of consumers. Int J Electr Eng Inform 5:297–315CrossRef
Metadaten
Titel
Optimal position and rating of DG in distribution networks by ABC–CS from load flow solutions illustrated by fuzzy-PSO
verfasst von
Rajni Bala
Smarajit Ghosh
Publikationsdatum
05.07.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3084-7

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