Maximizing Power Loss Reduction in Radial Distribution Systems by Using Modified Gray Wolf Optimization

Authors

  • Deepa Nataraj St. Peter’s Institute of Higher Education and Research, Deemed to be University, Chennai, India
  • Rajaji Loganathan ARM College of Engineering and Technology, Chennai, India
  • Moorthy Veerasamy Swarnandhra College of Engineering and Technology, Narsapur Bhimavaram, India
  • Venkata Durga Ramarao Reddy Vishni Institute of Technology, Bhimavaram, India

Keywords:

radial distribution system, network reconfiguration, power loss reduction, modified gray wolf optimization, distributed generation

Abstract

This paper presents an optimal Distribution Network Reconfiguration (DNR) framework and solution procedure that employ a novel modified Gray Wolf Optimization (mGWO) algorithm to maximize the power loss reduction in a Distribution System (DS). Distributed Generation (DG) is integrated optimally in the DS to maximize the power loss reduction. DNR is an optimization problem that involves a nonlinear and multimodal function optimized under practical constraints. The mGWO algorithm is employed for ascertaining the optimal switching position when reconfiguring the DS to facilitate the maximum power loss reduction. The position of the gray wolf is updated exponentially from a high value to zero in the search vicinity, providing the perfect balance between intensification and diversification to ascertain the fittest function and exhibiting rapid and steady convergence. The proposed method appears to be a promising optimization tool for electrical utility companies, thereby modifying their operating DS strategy under steady-state conditions. It provides a solution for integrating more DG optimally in the existing distribution network. In this study, IEEE 33-bus and 69-bus DSs are analyzed for maximizing the power loss reduction through reconfiguration, and the integration of DG is exercised in the 33-bus test system alone. The simulation results are examined and compared with those of several recent methods. The numerical results reveal that mGWO outperforms other contestant algorithms.

References

M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Power Engineering Review, vol. 9, pp. 101-102, April 1989.

H. D. Chiang and R. Jean-Jumeau, “Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results,” IEEE Transactions on Power Delivery, vol. 5, pp. 1568-1574, July 1990.

B. Venkatesh, R. Rakesh, and H. B. Gooi, “Optimal reconfiguration of radial distribution systems to maximize loadability,” IEEE Transactions on Power Systems, vol. 19, pp.260-266, February 2004.

J. Z. Zhu, “Optimal reconfiguration of electrical distribution network using the refined genetic algorithm,” Electric Power Systems Research, vol. 62, pp. 37-42, May 2002.

C. T. Su, C. F. Chang, and J. P. Chiou, “Distribution network reconfiguration for loss reduction by ant colony search algorithm,” Electric Power Systems Research, vol. 75, pp. 190-199, August 2005.

F. Rivas-Davalos and M. Irving, “The edge-set encoding in evolutionary algorithms for power distribution network planning problem part I: Single-objective optimization planning,” Proc. IEEE Con. Electronics, Robotics and Automotive Mechanics Conference, September 2006.

C. Wang and H. Z. Cheng, “Optimization of network configuration in large distribution systems using plant growth simulation algorithm,” IEEE Transactions on Power Systems, vol. 23, pp. 119-126, February 2008.

K. Sathish Kumar and T. Jayabarathi, “Power system reconfiguration and loss minimization for a distribution systems using bacterial foraging optimization algorithm,” International Journal of Electrical Power & Energy Systems, vol. 36, pp. 13-17, March 2012.

T. T. Nguyen and A. V. Truong, “Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm,” International Journal of Electrical Power & Energy Systems, vol. 68, pp. 233-242, June 2015.

A. Swarnkar, N. Gupta, and K. R. Niazi, “Adapted ant colony optimization for efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization,” Swarm and Evolutionary Computation, vol. 1, pp. 129-137, September 2011.

S. H. Mirhoseini, S. M. Hosseini, M. Ghanbari, and M. Ahmadi, “A new improved adaptive imperialist competitive algorithm to solve the reconfiguration problem of distribution systems for loss reduction and voltage profile improvement,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 128-143, February 2014.

S. Naveen, K. Sathish Kumar, and K. Rajalakshmi, “Distribution system reconfiguration for loss minimization using modified bacterial foraging optimization algorithm,” International Journal of Electrical Power & Energy Systems, vol. 69, pp. 90-97, July 2015.

M. Subramaniyan, S. Subramaniyan, V. Jawalkar, and M. Veerasamy, “Adaptive weighted improved discrete particle swarm optimization for optimal distribution network reconfiguration,” Journal of Computational and Theoretical Nanoscience, vol. 14, pp. 3344-3350, July 2017.

C. T. Su and C. S. Lee, “Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution,” IEEE Transactions on Power Delivery, vol. 18, pp. 1022-1027, July 2003.

T. Niknam, “An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration,” European Transactions on Electrical Power, vol. 20, pp. 575-590, July 2010.

T. T. Nguyen, A. V. Truong, Q. T. Nguyen, and T. A. Phung, “Multiobjective electric distribution network reconfiguration solution using runner-root algorithm,” Applied Soft Computing, vol. 52, pp. 93-108, March 2017.

S. Manikandan, S. Sasidharan, J. Viswanatharao, and V. Moorthy, “Fuzzy satisfied multiobjective distribution network reconfiguration: an application of adaptive weighted improved discrete particle swarm optimization,” International Review on Modelling and Simulations, vol. 10, pp. 247-257, August 2017.

A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, “Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 78, pp. 207-214, June 2016.

E. S. Ali, S. M. Abd Elazim, and A. Y. Abdelaziz, “Improved harmony algorithm and power loss index for optimal locations and sizing of capacitors in radial distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 80, pp. 252-263, September 2016.

E. S. Ali, S. M. Abd Elazim, and A. Y. Abdelaziz, “Ant lion optimization algorithm for renewable distributed generations,” Energy, vol. 116, pp. 445-458, December 2016.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, March 2014.

J. Rameshkumar, S. Ganesan, S. Subramanian, and M. Abirami, “Cost, emission and reserve pondered pre-dispatch of thermal power generating units coordinated with real coded grey wolf optimisation,” IET Generation, Transmission & Distribution, vol. 10, pp. 972-985, March 2016.

N. Mittal, U. Singh, and B. S. Sohi, “Modified grey wolf optimizer for global engineering optimization,” Applied Computational Intelligence and Soft Computing, vol. 2016, pp. 1-16, 2016.

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Published

2019-09-10

How to Cite

[1]
D. Nataraj, R. Loganathan, M. Veerasamy, and V. D. R. Reddy, “Maximizing Power Loss Reduction in Radial Distribution Systems by Using Modified Gray Wolf Optimization”, Int. j. eng. technol. innov., vol. 9, no. 4, pp. 327–343, Sep. 2019.

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