Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

This paper proposes a chaotic stochastic fractal search algorithm (CSFSA) method to solve the reconfiguration problem for minimizing the power loss and improving the voltage profile in distribution systems. The proposed method is a metaheuristic method developed for overcoming the weaknesses of the conventional SFSA with two processes of diffuse and update. In the first process, new points will be created from the initial points by the Gaussian walk. For the second one, SFSA will update better positions for the particles obtained in the diffusion process. In addition, this study has also integrated the chaos theory to improve the SFSA diffusion process as well as increase the rate of convergence and the ability to find the optimal solution. The effectiveness of the proposed CSFSA has been verified on the 33-bus, 84-bus, 119-bus, and 136-bus distribution systems. The obtained results from the test cases by CSFSA have been verified to those from other natural methods in the literature. The result comparison has indicated that the proposed method is more effective than many other methods for the test systems in terms of power loss reduction and voltage profile improvement. Therefore, the proposed CSFSA can be a very promising potential method for solving the reconfiguration problem in distribution systems.

[1]  Zhijian Wu,et al.  Hybrid Differential Evolution Algorithm with Chaos and Generalized Opposition-Based Learning , 2010, ISICA.

[2]  Nikola Rajaković,et al.  Application assessments of distribution network minimum loss reconfiguration , 1997 .

[3]  Adi Soeprijanto,et al.  Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index , 2018 .

[4]  Kwok-Wo Wong,et al.  An improved particle swarm optimization algorithm combined with piecewise linear chaotic map , 2007, Appl. Math. Comput..

[5]  Jae-Chul Kim,et al.  An efficient algorithm for network reconfiguration in large scale distribution systems , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[6]  M. Kowsalya,et al.  A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using Fireworks Algorithm , 2014 .

[7]  J. C. Cebrian,et al.  Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms , 2010 .

[8]  S. M. Elkhodary,et al.  Distribution Systems Reconfiguration Using Ant Colony Optimization and Harmony Search Algorithms , 2013 .

[9]  M. B. Eteiba,et al.  A New Hybrid Technique for Minimizing Power Losses in a Distribution System by Optimal Sizing and Siting of Distributed Generators with Network Reconfiguration , 2018, Energies.

[10]  G. B. Jasmon,et al.  Network reconfiguration for load balancing in distribution networks , 1999 .

[11]  Priti Singh,et al.  Optimal Reconfiguration of Distribution Network Using Modified Culture Algorithm , 2018, Journal of The Institution of Engineers (India): Series B.

[12]  Kazuyuki Aihara,et al.  Chaotic simulated annealing by a neural network model with transient chaos , 1995, Neural Networks.

[13]  Saeid Esmaeili,et al.  A new multiobjective fuzzy shuffled frog-leaping algorithm for optimal reconfiguration of radial distribution systems in the presence of reactive power compensators , 2013 .

[14]  A. Sumper,et al.  Distribution system reconfiguration using genetic algorithm based on connected graphs , 2013 .

[15]  Hamid Salimi,et al.  Stochastic Fractal Search: A powerful metaheuristic algorithm , 2015, Knowl. Based Syst..

[16]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[17]  Alberto LANDEROS,et al.  Distribution network reconfiguration using feasibility-preserving evolutionary optimization , 2019 .

[18]  Mostafa Sedighizadeh,et al.  Optimal multi-objective reconfiguration and capacitor placement of distribution systems with the Hybrid Big Bang–Big Crunch algorithm in the fuzzy framework , 2016 .

[19]  Chun Wang,et al.  Determination of Power Distribution Network Configuration Using Non-Revisiting Genetic Algorithm , 2013, IEEE Transactions on Power Systems.

[20]  M. M. Adibi,et al.  Distribution Feeder Reconfiguration for Service Restoration and Load Balancing , 2000 .

[21]  Dieu Ngoc Vo,et al.  Stochastic fractal search algorithm for reconfiguration of distribution networks with distributed generations , 2020 .

[22]  Jin Xu,et al.  Chaotic Fruit Fly Optimization Algorithm , 2014, ICSI.

[23]  Amit Patra,et al.  Reconfiguration of distribution networks with optimal placement of distributed generations in the presence of remote voltage controlled bus , 2017 .

[24]  M.A.L. Badr,et al.  Distribution system reconfiguration using a modified Tabu Search algorithm , 2010 .

[25]  A. A. M. Zin,et al.  Reconfiguration of Radial Electrical Distribution Network Through Minimum-Current Circular-Updating-Mechanism Method , 2012, IEEE Transactions on Power Systems.

[26]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[27]  Gevork B. Gharehpetian,et al.  Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators , 2008, Appl. Math. Comput..

[28]  Chun Wang,et al.  Optimization of Network Configuration in Large Distribution Systems Using Plant Growth Simulation Algorithm , 2008, IEEE Transactions on Power Systems.

[29]  Reza Noroozian,et al.  Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method , 2016 .

[30]  A. V. Truong,et al.  A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network , 2016 .

[31]  Ying-Yi Hong,et al.  Determination of network configuration considering multiobjective in distribution systems using genetic algorithms , 2005, IEEE Transactions on Power Systems.

[32]  Thang Trung Nguyen,et al.  Multi-objective electric distribution network reconfiguration solution using runner-root algorithm , 2017, Appl. Soft Comput..

[33]  Arun Onlam,et al.  Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping Algorithm , 2019, Energies.

[34]  Y. Huang.  Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration , 2002 .

[35]  Jose Roberto Sanches Mantovani,et al.  Reconfiguracao de sistemas de distribuicao radiais utilizando o criterio de queda de tensao , 2000 .

[36]  N. C. Sahoo,et al.  A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems , 2006 .

[37]  Maheswarapu Sydulu,et al.  Multi-Objective Invasive Weed Optimization – An application to optimal network reconfiguration in radial distribution systems , 2015 .

[38]  Xueying Liu,et al.  Cuckoo search algorithm based on frog leaping local search and chaos theory , 2015, Appl. Math. Comput..

[39]  Mostafa Sedighizadeh,et al.  An Efficient Hybrid Big Bang–Big Crunch Algorithm for Multi-objective Reconfiguration of Balanced and Unbalanced Distribution Systems in Fuzzy Framework , 2013 .

[40]  Bin Wu,et al.  Improved Artificial Bee Colony Algorithm with Chaos , 2011 .

[41]  D. Das.  A fuzzy multiobjective approach for network reconfiguration of distribution systems , 2006, IEEE Transactions on Power Delivery.

[42]  C. Su,et al.  Network Reconfiguration of Distribution Systems Using Improved Mixed-Integer Hybrid Differential Evolution , 2002, IEEE Power Engineering Review.

[43]  Seyed Mehdi Hosseini,et al.  A new improved adaptive imperialist competitive algorithm to solve the reconfiguration problem of distribution systems for loss reduction and voltage profile improvement , 2014 .

[44]  Zhengcai Fu,et al.  An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems , 2007 .

[45]  Javad Olamaei,et al.  Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm , 2012 .

[46]  R. Jabr,et al.  Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming , 2012, IEEE Transactions on Power Systems.

[47]  A. Ahuja,et al.  An AIS-ACO Hybrid Approach for Multi-Objective Distribution System Reconfiguration , 2007, IEEE Transactions on Power Systems.

[48]  J. J. Grainger,et al.  Distribution feeder reconfiguration for loss reduction , 1988 .

[49]  SalimiHamid.  Stochastic Fractal Search , 2015 .

[50]  K. R. Niazi,et al.  Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework , 2010 .

[51]  M. Raju,et al.  Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm , 2011, IEEE Transactions on Power Systems.

[52]  M. Haghifam,et al.  Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory , 2013 .

[53]  Anh Viet Truong,et al.  Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm , 2015 .