International Journal of Electronics Engineering Research. ISSN 0975-6450 Volume 9, Number 3 (2017) pp. 323-330 © Research India Publications http://www.ripublication.com
Dragonfly Optimization Based Reconfiguration for Voltage Stability Enhancement in Distribution Systems
T. K. Abhiraj Assistant professor, Department of EEE, Ilahia School of Science and Technology , Pezhakkappilly, Muvattupuzha, Ernakulam, Kerala-685573, India.
Bos Mathew Jos Professor of Electrical and Electronics Engineering Mar Athanasius College of Engineering, Kothamangalam 686 666, Kerala, India.
P. Aravindhababu Professor, Department of Electrical Engineeringm, Annamalai University, Annamalainagar-608 002, Tamil Nadu, India.
Abstract
Voltage stability (VS) has recently become a challenging issue in many distribution networks, especially in industrial areas. The distribution networks are generally reconfigured with a view of reducing the real power loss and offering a better voltage profile for the utilities. This paper applies dragonfly optimization (DO), for reconfiguring distribution systems with a view of enhancing the VS without incurring any additional cost for installation of capacitors and tap-changing transformers. Test results on 33 and 69-node distribution systems exhibit the superiority of the proposed strategy.
Keywords: voltage stability; distribution systems; reconfiguration; dragonfly optimization.
324 T. K. Abhiraj, Bos Mathew Jos and P. Aravindhababu
NOMENCLATURE
C branch-to-node matrix that describes the topological structure of the distribution network DO dragonfly optimization DORS DO based reconfiguration strategy nn number of nodes OS j binary variable that represents status of j -th tie switch
P jQ real and reactive power flow through a branch connected between km km nodes-k and -m r jx resistance and reactance of branch connected between nodes-k and -m km km V voltage at node-m m LVM lowest node voltage seen in the network VS voltage stability VSI voltage stability index LVSI lowest VSI seen in the network
a set of branches, whose current flow exceed the respective thermal limit w penalty factor c
INTRODUCTION Network reconfiguration is a process of altering the topological structures of the distribution feeders by changing the open/close status of the sectionalizing and tie switches. Reconfiguration is performed to reduce network real power loss (RPL), achieve load balancing and relieve network overloads. Many mathematical techniques such as Merlin and Back [1], Benders decomposition [2] and mixed-integer programming [3,4] have been suggested. These algorithms are usually fast but may not achieve the optimal configuration. Therefore, metaheuristic algorithms such as hyper-cube ant colony optimization [8], bacterial foraging optimization algorithm [9], particles swarm optimization [10], artificial immune systems [11], adaptive imperialist competitive algorithm [12], genetic algorithms [13] and biogeography based optimization [14] have been applied for reconfiguration.
In recent years, the distribution networks experience a sharp increase in load demand on account of the extensive growth of the utilities and are operating more closer to the voltage stability (VS) boundaries. In certain industrial areas, it is observed that under certain critical loading conditions, the distribution network suffers from voltage Dragonfly Optimization Based Reconfiguration for Voltage Stability Enhancement 325 collapse [15]. Hence there is an urgent need to explore ways to enhance VS in distribution networks.
Recently, a Dragonfly Optimization (DO), a meta-heuristic optimization technique imitated from the static and dynamic swarming activities of dragonflies, has been outlined for solving optimization problems by Seyedali Mirjalili [16]. It has been applied to a variety of optimization problems [17-19] and found to yield satisfactory results. This paper suggests a strategy to apply DO in solving the reconfiguration problem with a view of enhancing the VS and presents the results of two standard test systems.
PROPOSED METHOD This section describes a DO based reconfiguration strategy (DORS) for enhancing VS. Each dragonfly of the DORS is denoted in vector form to represent the decision variables as
dragonfly OSOS ,, OS 21 n (1) The fitness function representing the problem objective function and constraints as [14,15] Maximize nn mmVSI :1;)(min nn Fitness 2 C ,1 j (2) 1 max c iiw kmkm j1 m
Where 4 2 2 k 4)( kmkm kmkm 4 kmkm kmkm VxQrPrQxPVmVSI k (3)
An initial swarm of dragonflies is formed by generating random values within their respective limits. The fitness function is calculated by altering the network topology according to the status of open-switches of each dragonfly; and the exploration and exploitation phases, which represent social interaction of dragonflies in navigating and searching for foods and avoiding enemies, are performed for all the dragonflies in the swarm with a view of maximizing their finesses. The iterative process is continued till convergence.
SIMULATION The DORM has been applied on 33 and 69 node distribution systems [20, 21]. The 33 node network, operating at 12.66 kV with net loads of 3715 kW and 2300 kVar, contains 5 normally opened switches and 32 normally closed switches. While 69 node network, operating at 12.66 kV with network load of 3802.19 kW and 2694.60 kVar, possesses 5 tie-loops. The results of the DORS have been compared with those of the existing methods published in [22-24].
326 T. K. Abhiraj, Bos Mathew Jos and P. Aravindhababu
The results of both 33 and 69 node networks, containing details of open-switches, LVSI and lowest voltage magnitude (LVM), before and after reconfiguration are compared with those of the existing methods in Table-1 and 2 respectively.
It is very clear from the results that the proposed DORS offers a LVSI of 0.7851, which is higher than those of the existing methods for 33 node system. While for 69 node system, LVSI is 0.8128, which is also higher than the existing approaches. The VP and VSI at all nodes before and after reconfiguration are graphically shown for both the systems in Figs.1 and 2 respectively. It is seen from the figures that there is significant improvement in the VP after reconfiguration. It can also be observed from the figures that there is significant enhancement in VS almost at all nodes.
(a) Voltage profile
(b) VS profile
Fig. 1 Plot of VP and VSI for 33 node network
Dragonfly Optimization Based Reconfiguration for Voltage Stability Enhancement 327
(a) Voltage profile
(b) VS profile
Fig. 2 Plot of VP and VSI for 69 node network
TABLE.1. Results of 33 node network Open Switches LVSI LVM Initial Configuration 33,34,35,36,37 0.6672 0.9038 DORS 7 10 14 32 28 0.7851 0.9412 BBO [14] 7,9,14,32,28 0.7850 0.9413 Sahoo [22] 7,34,35,32,28 0.7203 0.9212 Sivanagaraju [23] 7,34,11,36,28 0.7806 0.9378 Arun [24] 7,34,11,32,28 0.7806 0.9400
328 T. K. Abhiraj, Bos Mathew Jos and P. Aravindhababu
It is very clear from above discussions that the proposed DORS offers a better configuration that enhances the VS for both the systems. Even though VP is not considered as an objective, the proposed DORS is able to offer a reasonably good VP.
TABLE.2. Results of 69 node network
Open switches LVSI LVM Initial Configuration 69,70,71,72, 73 0.6833 0.9092 DORS 14 70 55 5 61 0.8128 0.9495 BBO [14] 14,56,61,4,70 0.8127 0.9495 Sahoo [22] 69,20,12,58,64 0.705 0.916 Sivanagaraju [23] 10,70,14,58,63 0.749 0.929 Arun [24] 69,70,14,58,61 0.754 0.931
CONCLUSION DO is a meta-heuristic optimization technique imitated from the static and dynamic swarming activities of dragonflies. An elegant DO based reconfiguration scheme for VS enhancement of radial distribution networks has been developed. The reconfiguration problem has been modeled as an optimization problem with an objective of enhancing VS and solved by DO. The method is able to offer better VS without any additional infrastructural cost, besides improving the VP. The algorithm is suitable for practical implementation on networks of any size.
ACKNOWLEDGEMENTS The authors gratefully acknowledge the authorities of Annamalai University and Ilahia School of Science and Technology for the facilities offered to carry out this work.
REFERENCES
[1] Merlin A and Back H. (1975). Search for a minimal loss operating spanning tree configuration for an urban power distribution system, Proc of the power systems computation conf (PSCC): 1-18. [2] H. M. Khodr and J. Martinez-Crespo. (2009). Integral methodology for distribution systems reconfiguration based on optimal power flow using benders decomposition technique, IET Gen. Trans. & Dist., 3(6): 521-534. [3] R. A. Jabr, R. Singh and B. C. Pal. (2012). Minimum loss network reconfiguration using mixed-integer convex programming, IEEE Trans on Power Systems, 27 (2): 1106-1115. Dragonfly Optimization Based Reconfiguration for Voltage Stability Enhancement 329
[4] J. A. Taylor and F. S. Hover. (2012). Convex models of distribution system reconfiguration, IEEE Trans. On Power Systems, 27(3): 1407-1413. [5] Civanlar S, Grainger J and Yin H, Lee S .(1988). Distribution feeder reconfiguration for loss reduction, IEEE Trans Power Delivery, 3(3): 1217- 1223. [6] Goswami S and Basu S. (1992). A new algorithm for the reconfiguration of distribution feeders for loss minimization, IEEE Trans Power Delivery, 7(3):1482-1491. [7] Shirmohammadi D and Hong H. (1989). Reconfiguration of electric distribution networks for resistive line loss reduction, IEEE Trans Power Delivery, 4(2):1492-1498. [8] A. Y. Abdelaziz, R. A. Osama and S. M. El-Khodary. (2012). Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimization algorithm, IET Gen. Trans. & Dist., 6(2): 176-187. [9] K. Sathish Kumar and T. Jayabarathi. (2012). Power system reconfiguration and loss minimization for distribution systems using Bacterial Foraging optimization algorithm, Int. J. Electr. Power Energy Syst, 36(1): 13-17. [10] M. R. Andervazh, J. Olamaei and M. R. Haghifam. (2013). Adaptive multi- objective distribution network reconfiguration using multi-objective discrete Particles Swarm Optimisation algorithm and graph theory, IET Gen. Trans. & Dist., 7(12): 1367-1382. [11] L. W. de Oliveira, E. J. de Oliveira, F. V. Gomes, I. C. Silva Jr, A. L. M. Marcato and P. V. C. Resende. (2014). Artificial Immune Systems applied to the reconfiguration of electrical power distribution networks for energy loss minimization, Int. J. Electr. Power Energy Syst., 56: 64-74. [12] S. H. Mirhoseini, S. M. Hosseini, M. Ghanbari and M. Ahmadi. (2014). A new improved adaptive imperialist competitive algorithm to solve the reconfiguration problem of distribution systems for loss reduction and voltage profile improvement, Int. J. Electr. Power Energy Syst., 55: 128-143. [13] N. Gupta, A. Swarnkar and K. R. Niazi. (2014). Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms, Int. J. Electr. Power Energy Syst., 54: 664-671. [14] S. Aruul Vizhiy and R.K.Santhi. (2016) Biogeography Based Reconfiguration Algorithm for Voltage Stability Enhancement of Distribution Networks,” International Journal of Applied Engineering Research, 11(4): 2244-49. [15] R.B. Prada & L.J. Souza. (1998). Voltage stability and thermal limit: constraints on the maximum loading of electrical energy distribution feeders, IEE Proc. Gen. Trans and Dist., 145 (5): 573-77. [16] Seyedali Mirjalili., Dragonfly algorithm, 2015: A new meta-heuristic optimization technique for solving single-objective, discrete and multi- objective problems, Neural Comput and Applic. DOI. 10.1007/s00521-015- 1920-1. [17] Dharmendra Tiwari, Nikhil Pachauri, Asha Rani, Vijander Singh, 2016. Fractional Order PID (FOPID) Controller based Temperature Control of 330 T. K. Abhiraj, Bos Mathew Jos and P. Aravindhababu
Bioreactor, Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). [18] Hamdy, M., Nguyen, AT., and Hensen, J.L.M., 2016. A performance comparison of multi-objective optimization algorithms for solving nearly-zero- energy-building design problems, Energy and Buildings. http://dx.doi.org/10.1016/j.enbuild.2016.03.035. [19] Rakoth Kandan Sambandam and Sasikala Jayaraman. (2016). Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images, Journal of King Saud University-Computer and Information Sciences, http://dx.doi.org/10.1016/j.jksuci.2016.11.002. [20] Baran M and Wu F. (1989). Network reconfiguration in distribution systems for loss reduction and load balancing, IEEE Trans on Power Delivery, 4(2): 1401-1407. [21] Kashem M, Ganapathy V and Jasmon G (2001) A geometrical approach for network reconfiguration based loss minimization in distribution systems, Int J Elect Power Energy Syst, 23(4): 295-304. [22] N.C.Sahoo and K. Prasad. (2006). A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems, Energy Conversion and Management, 47(18): 3288-306. [23] S. Sivanagaraju, N. Visali, V. Sankar and T. Ramana. (2004). Enhancing voltage stability of radial distribution systems by network reconfiguration, Electric power components and systems, 33(5): 539-50. [24] M. Arun and P. Aravindhababu. (2009). A new reconfiguration scheme for voltage stability enhancement of radial distribution systems, Energy Conversion and Management, 50(9): 2148-51