A novel approach for the reconfiguration of distribution systems considering the voltage stability margin

In recent years, the problem of optimum reconfiguration in distribution systems (DSs) has been a task that must be solved in an optimal manner. This paper presents a new approach for the optimal reconfiguration of DSs based on a hierarchical 2-stage optimization problem to improve the power system voltage stability margin and reduce losses incorporating the constraints. The mentioned problem has been modeled as a nonlinear and multiobjective optimization problem. It uses the ability of the developed harmony search algorithm (HSA) as the first stage of the proposed optimization problem to reach the best network configuration. This reconfiguration algorithm starts with a radial topology by a theoretical approach that is based on the graph concept and matroid theory. These concepts are used in order to propose new intelligent HSAs to form a new harmony vector that is well dedicated to the DS reconfiguration problem. Thus, all of the resulting individuals after forming a new harmony vector are claimed to be feasible configurations. Moreover, the presented approach is valid and avoids tedious mesh checks for the topology constraint validation. In the second stage of the proposed approach, the voltage stability index is calculated to evaluate the static voltage stability security margin for each reconfiguration pattern. Hence, a toolbox has been developed to recognize the loadability limit of DSs based on the Lagrangian optimization method. Finally, the proposed method establishes a tradeoff between the security index and power losses to reach a coordinated reconfiguration pattern. To demonstrate the validity of the proposed method, the simulations are carried out on 33- and 69-bus IEEE DSs. The proposed method is finally compared to some previous techniques used by other authors. The results show a good enhancement in the security margin and smaller power losses with considerably less computation effort. To validate the proposed method, the results that were obtained from the HSA are compared with the particle swarm optimization algorithm to ascertain its effectiveness.

A novel approach for the reconfiguration of distribution systems considering the voltage stability margin

In recent years, the problem of optimum reconfiguration in distribution systems (DSs) has been a task that must be solved in an optimal manner. This paper presents a new approach for the optimal reconfiguration of DSs based on a hierarchical 2-stage optimization problem to improve the power system voltage stability margin and reduce losses incorporating the constraints. The mentioned problem has been modeled as a nonlinear and multiobjective optimization problem. It uses the ability of the developed harmony search algorithm (HSA) as the first stage of the proposed optimization problem to reach the best network configuration. This reconfiguration algorithm starts with a radial topology by a theoretical approach that is based on the graph concept and matroid theory. These concepts are used in order to propose new intelligent HSAs to form a new harmony vector that is well dedicated to the DS reconfiguration problem. Thus, all of the resulting individuals after forming a new harmony vector are claimed to be feasible configurations. Moreover, the presented approach is valid and avoids tedious mesh checks for the topology constraint validation. In the second stage of the proposed approach, the voltage stability index is calculated to evaluate the static voltage stability security margin for each reconfiguration pattern. Hence, a toolbox has been developed to recognize the loadability limit of DSs based on the Lagrangian optimization method. Finally, the proposed method establishes a tradeoff between the security index and power losses to reach a coordinated reconfiguration pattern. To demonstrate the validity of the proposed method, the simulations are carried out on 33- and 69-bus IEEE DSs. The proposed method is finally compared to some previous techniques used by other authors. The results show a good enhancement in the security margin and smaller power losses with considerably less computation effort. To validate the proposed method, the results that were obtained from the HSA are compared with the particle swarm optimization algorithm to ascertain its effectiveness.

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  • M. Assadian, M.M. Farsangi, H. Nezamabadi-pour, “GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving”, Energy Conversion and Management, Vol. 51, pp. 418–427, 2009.
  • E. Carpaneto, G. Chicco, “Distribution system minimum loss reconfiguration in the hyper-cube ant colony optimization framework”, Electric Power Systems Research, Vol. 78, pp. 2037–2045, 2008.
  • B. Enacheanu, B. Raison, R. Caire, O. Devaux, W. Bienia, N. Hadjsaid, “Radial network reconfiguration using genetic algorithm based on the matroid theory”, IEEE Transactions on Power Systems, Vol. 23, pp. 186–195, 2008. G.K. Viswanadha Raju, P.R. Bijwe, “Efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization”, IET Generation, Transmission & Distribution, Vol. 2, pp. 7–12, 2008.
  • G.K. Viswanadha Raju, P.R. Bijwe, “An efficient algorithm for minimum loss reconfiguration of distribution system based on sensitivity and heuristics”, IEEE Transactions on Power Systems, Vol. 23, pp. 1280–1287, 2008.
  • S. Bahadoorsingh , J.V. Milanovic, Y. Zhang, C.P. Gupta, J. Dragovic, “Minimization of voltage sag costs by optimal reconfiguration of distribution network using genetic algorithms”, IEEE Transactions on Power Systems, Vol. 22, pp. 2271–2278, 2007.
  • A. Saffar, R. Hooshmand, A. Khodabakhshian, “A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm”, Journal of Applied Soft Computing, Vol. 11, pp. 4032–4028, 2010.
  • S. Jazebi, S.H. Hosseinian, B. Vahidi, “DSTATCOM allocation in distribution networks considering reconfiguration using differential evolution algorithm”, Energy Conversion and Management, Vol. 52, pp. 2777–2783, 2011.
  • M.A. Kashem, V. Ganapathy, G.B. Jasmon, “Network reconfiguration for enhancement of voltage stability in distribution networks”, IEE Proceedings - Generation, Transmission and Distribution, Vol. 147, pp. 171–175, 2000. B. Venkatesh, R. Ranjan, H.B Gooi, “Optimal reconfiguration of radial distribution systems to maximize loadability”, Power Systems, IEEE Transactions, Vol. 19, pp. 260–266, 2004.
  • M.A.N. Guimaraes, J.F.C. Lorenzeti, C.A. Castro, “Reconfiguration of distribution systems for stability margin enhancement using tabu search”, Proceedings of the Power System Technology Power Conference, Vol. 2, pp. 1556–1561, 2004.
  • M. Arun, P. Aravindhababu, “A new reconfiguration scheme for voltage stability enhancement of radial distribution systems”, Energy Conversion and Management, Vol. 50, pp. 2148–2151, 2009.
  • N.C. Sahoo, K. Prasad, “A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems”, Energy Conversion and Management, Vol. 47, pp. 3288–3306, 2006.
  • M.R. Aghamohammadi, M. Mohammadian, “Loadability limit assessment in Iran power network with respect to voltage stability constrains”, Proceedings of the 11th International Power System Conference, pp. 1–12, 1996.
  • M.R. Aghamohamadi, M. Mohammadian, H. Saitoh, “Sensitivity characteristic of neural network as a tool for analyzing and improving voltage stability”, Proceedings of the IEEE PES Transmission and Distribution Conference and Exhibition, Asia Pacific, Vol. 2, pp. 1128–1132, 2002.
  • M. Mohammadian, “Power system voltage stability and security assessment by neural network technique”, MSc, Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran, 1997.
  • M. Mohammadian, M.R. Aghamohammadi, S.M.T. Bathaee, “Power plants generation scheduling constrained to voltage stability limit based on sensitivity characteristic of neural network”, Proceedings of the 17th International Power System Conference, pp. 1–10, 2002.
  • M.R. Aghamohammadi, M. Mohammadian, A. Golkar, “Generation scheduling constrained to voltage stability limit”, Proceedings of the 16th International Power System Conference, pp. 1–10, 2001.
  • M. Rezaie Estabragh, M. Mohammadian, M. Rashidinejad, “An application of elitist-based genetic algorithm for SVC placement considering voltage stability”, International Review on Modeling and Simulations, Vol. 5, pp. 938– 937, 2010.
  • A.R. Abul’Wafa, “A new heuristic approach for optimal reconfiguration in distribution systems”, Electric Power Systems Research, Vol. 81, pp. 282–289, 2011.
  • Z.W. Geem, C. Tseng, Y. Park, “Harmony search for generalized orienteering problem: best touring in China”, Springer Lecture Notes on Computer Science, Vol. 3612, pp. 741–750, 2005.
  • K.S. Lee, Z.W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice”, Computer Methods in Applied Mechanics and Engineering, Vol. 194, pp. 3902–3933, 2005.
  • Z.W. Geem, J.H. Kim, G.V. Loganathan, “A new heuristic optimization algorithm: harmony search”, Transactions of the Society For Modeling and Simulation International, Vol. 76, pp. 60–68, 2001.
  • S. Kulluk, L. ¨ Ozbakir, A. Baykasoglu, “Self-adaptive global best harmony search algorithm for training neural networks”, Procedia Computer Science, Vol. 3, pp. 282–286, 2011.
  • Q.K. Pan, P.N. Suganthan, M.F. Tasgetiren, J.J. Liang, “A self-adaptive global best harmony search algorithm for continuous optimization problems”, Applied Mathematics and Computation, Vol. 216, pp. 830–848, 2010.
  • P. Yadav, R. Kumar, S.K. Panda, C.S. Chang, “An improved harmony search algorithm for optimal scheduling of the diesel generators in oil rig platforms”, Energy Conversion and Management, Vol. 52, pp. 893–902, 2011.
  • V.R. Pandi, P.B. Ketan, “Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm”, Expert Systems with Applications, Vol. 38, pp. 509–8514, 2011.
  • M. Huang, G. Bo, X.W. Wang, W.H. Ip, “The optimization of routing in fourth-party logistics with soft time windows using harmony search”, Proceedings of the 6th International Conference on International Natural Computation, Vol. 8, pp. 4344–4348, 2010.
  • O. Ceylan, A. Ozdemir, H. Dag, “Comparison of post outage bus voltage magnitudes estimated by harmony search and differential evolution methods”, Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems, pp. 1–6, 2009.
  • F. Harrou, A. Zeblah, “Harmony search algorithm optimization for preventive-maintenance-planning for transmission systems”, Proceedings of the International Conference on Advances in Computational Tools for Engineering Applications, pp. 584–590, 2009.
  • J.B. Kruskal Jr, “On the shortest spanning sub-tree of a graph and the traveling salesman problem”, Proceedings of The American Mathematical Society, Vol. 7, pp. 48–50, 1956.
  • R. Wilson, Introduction to Graph Theory, Harlow, Pearson Education Limited, 1996.
  • K.J. Binkley, “New methods of increasing the effectiveness of particle swarm optimization”, PhD, Graduate School of Science and Technology, Keio University, Tokyo, Japan, 2008.
  • H. Whitney, “On the abstract properties of linear dependence”, American Journal of Mathematics, Vol. 57, pp. 509–533, 1935.
  • Z.W. Geen, J.H. Kim, G.V. Loganthan, “A new heuristic optimization algorithm: harmony search”, Simulation, Vol. 76, pp. 60–68, 2001.
  • J.A. Bondy, U.S.R. Murty, Graph Theory with Applications, New York, Elsevier, pp. 134–169, 1976.
  • A.Y. Abdelaziz, F.M. Mohamed, S.F. Mekhamer, M.A.L. Badr, “Distribution system reconfiguration using a modified tabu search algorithm”, Electric Power Systems Research, Vol. 80, pp. 943–953, 2010.
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK