Journal of Electrical Engineering and Technology

  • 제12권5호
  • /
  • Pages.1729-1742
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  • 2017
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Special Protection and Control Scheme for Transmission Line Overloading Elimination Based on Hybrid Differential Evolution/Electromagnetism-Like Algorithm

  • Hadi, Mahmood Khalid (Dept. of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM) ;
  • Othman, Mohammad Lutfi (Dept. of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM) ;
  • Wahab, Noor Izzri Abd (Dept. of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM)
  • 투고 : 2016.08.01
  • 심사 : 2017.05.11
  • 발행 : 2017.09.01
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초록

In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

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참고문헌

  1. Hsiao, Tsun-Yu, and Chan-Nan Lu. Application assessment of system protection schemes for power network congestion management. Energy Conversion and Management, 51, no. 12, 2655-2662, 2010. https://doi.org/10.1016/j.enconman.2010.05.031
  2. Cholley P., Crossley P., Van Acker V., Van Cutsem T., Fu W., Soto Idia Oez J. & Piekutowski M. System protection schemes in power networks, CIGRE Technical Brochure, 2001.
  3. R. Vinnakota, M. Yao, D. Atanackovic, Modelling issues of system protection schemes in energy management systems, IEEE Electrical Power & Energy Conf., 2008.
  4. M. Khederzadeh, Distributed energy resources (DER) impacts on the performance of special protection schemes SPS, 21st International Conference on Electricity Distribution, CIRED, Frankfurt Germany, 2011.
  5. Othman, M.L., Aris, I. and Wahab, N.I.A. Modeling and simulation of the industrial numerical distance relay aimed at knowledge discovery in resident event reporting. Simulation, 2014; 90(6): pp.660-686. https://doi.org/10.1177/0037549714532961
  6. Seyedi, H., and M. Sanaye-Pasand. Design of new load shedding special protection schemes for a double area power system. American Journal of Applied Sciences 6.2, 317, 2009. https://doi.org/10.3844/ajassp.2009.317.327
  7. Awais, Muhammad, Abdul Basit, Rana Adnan, Zahoor Ali Khan, Umar Qasim, Tamour Shafique, and Nadeem Javaid. Overload Management in Transmission System Using Particle Swarm Optimization, Procedia Computer Science 52, 858-865, 2015. https://doi.org/10.1016/j.procs.2015.05.143
  8. Devaraj, D., and B. Yegnanarayana. Genetic-algorithmbased optimal power flow for security enhancement, IEE Proceedings in Generation, Transmission and Distribution, vol. 152, no. 6, pp. 899-905, IET, 2005. https://doi.org/10.1049/ip-gtd:20045234
  9. Talukdar, B. K., et al. A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management, International Journal of Electrical Power & Energy Systems, 27.5, 379-388, 2005. https://doi.org/10.1016/j.ijepes.2005.02.003
  10. Awaisa, Muhammad, Abdul Basita, Rana Adnana, Zahoor Ali Khanb, Umar Qasimc, Tamour Shafiqued, and Nadeem Javaida. Overload Management in Transmission System Using Particle Swarm Optimization, Procedia Computer Science 52, 858-865, available online at www.sciencedirect.com, 2015. https://doi.org/10.1016/j.procs.2015.05.143
  11. Pandiarajan, K., and C. K. Babulal. Transmission line management using hybrid differential evolution, with particle swarm optimization, J. Electrical Systems 10, no. 1, 21-35, 2014.
  12. Suganthi, S. T., D. Devaraj, and S. Hosmin Thilagar. An Improved Differential Evolution Algorithm for Congestion Management Considering Voltage Stability, Indian Journal of Science and Technology, no. 24, 2015.
  13. Balaraman Sujatha, and N. Kamaraj. Cascade BPN based transmission line overload prediction and preventive action by generation rescheduling, Neurocomputing 94, 1-12, 2012. https://doi.org/10.1016/j.neucom.2012.04.022
  14. Sharma, Savita, and Laxmi Srivastava. Prediction of transmission line overloading using intelligent technique, Applied Soft Computing 8, no. 1, 626-633, 2008. https://doi.org/10.1016/j.asoc.2007.05.001
  15. Dutta, Sudipta, and S. P. Singh. Optimal rescheduling of generators for congestion management based on particle swarm optimization, IEEE Transactions on Power Systems, 23, no. 4, 1560-1569, 2008. https://doi.org/10.1109/TPWRS.2008.922647
  16. Deb, Sujay, and Arup K. Goswami. Mitigation of congestion by generator rescheduling using Particle Swarm Optimization, In Power and Energy in NERIST (ICPEN), 2012 1st International Conference on, pp. 1-6, IEEE, 2012.
  17. Hagh M. Tarafdar, and S. Galvani, A multi objective genetic algorithm for weighted load shedding, In Electrical Engineering (ICEE), 2010 18th Iranian Conference on, pp. 867-873, IEEE, 2010.
  18. Kamaraj, N. Transmission congestion management using particle swarm optimization, J. Electrical Systems 7, no. 1, 54-70, 2011.
  19. Basu, M. Quasi-oppositional differential evolution for optimal reactive power dispatch. International Journal of Electrical Power & Energy Systems 78, 29-40. 2016. https://doi.org/10.1016/j.ijepes.2015.11.067
  20. Titare, L. S., Pushpendra Singh, L. D. Arya, and S. C. Choube. Optimal reactive power rescheduling based on EPSDE algorithm to enhance static voltage stability, International Journal of Electrical Power & Energy Systems 63, 588-599, 2014. https://doi.org/10.1016/j.ijepes.2014.05.078
  21. Arya, L. D., Pushpendra Singh, and L. S. Titare. Differential evolution applied for anticipatory load shedding with voltage stability considerations, International Journal of Electrical Power & Energy Systems 42, no. 1, 644-652, 2012. https://doi.org/10.1016/j.ijepes.2012.04.006
  22. Arya, L. D., Pushpendra Singh, and L. S. Titare. Optimum load shedding based on sensitivity to enhance static voltage stability using DE, Swarm and Evolutionary Computation 6, 25-38, 2012. https://doi.org/10.1016/j.swevo.2012.06.002
  23. Alsac, O., and B. Stott. Optimal load flow with steadystate security. IEEE Transactions on Power Apparatus and Systems, 3, 745-751, 1974.
  24. Lee, K. Y., Y. M. Park, and J. L. Ortiz. A united approach to optimal real and reactive power dispatch, Power Apparatus and Systems, IEEE Transactions on 5, 1147-1153, 1985.
  25. Birbil, S. Ilker, and Shu-Chering Fang. An electromagnetism-like mechanism for global optimization, Journal of global optimization 25.3, 263-282, 2003. https://doi.org/10.1023/A:1022452626305
  26. Birbil, S. Ilker, Shu-Cherng Fang, and Ruey-Lin Sheu. On the convergence of a population-based global optimization algorithm, Journal of global optimization 30.2-3, 301-318, 2004. https://doi.org/10.1007/s10898-004-8270-3
  27. Kaelo, P., and M. M. Ali. Differential evolution algorithms using hybrid mutation, Computational Optimization and Applications 37.2, 231-246, 2007. https://doi.org/10.1007/s10589-007-9014-3
  28. Muhsen, Dhiaa Halboot, Abu Bakar Ghazali, Tamer Khatib, and Issa Ahmed Abed. Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm, Solar Energy 119, 286-297, 2015. https://doi.org/10.1016/j.solener.2015.07.008
  29. Muhsen, Dhiaa Halboot, Abu Bakar Ghazali, Tamer Khatib, and Issa Ahmed Abed. Parameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithm, Energy Conversion and Management 105, 552-561, 2015. https://doi.org/10.1016/j.enconman.2015.08.023
  30. Vitaliy, F., Differential evolution-in search of solutions, Springer, New York, 2006.
  31. Storn, Rainer, and Kenneth Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, no. 4, 341-359, 1997. https://doi.org/10.1023/A:1008202821328
  32. Ishaque, Kashif, and Zainal Salam. An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE), Solar Energy 85, no. 9, 2349-2359, 2011. https://doi.org/10.1016/j.solener.2011.06.025
  33. Singh, Himmat, and Laxmi Srivastava. Modified differential evolution algorithm for multi-objective VAR management, International Journal of Electrical Power & Energy Systems 55, 731-740. 2014. https://doi.org/10.1016/j.ijepes.2013.10.015
  34. Price, K. V., Storn, R.M. and Lampinen, J. A., Differential Evolution-A Practical Approach to Global Optimization. 2006.
  35. Sen S, Chanda S, Sengupta S, Chakrabarti A, De A. Alleviation of line congestion using Multiobjective Particle Swarm Optimization, In Electrical Engineering and Informatics (ICEEI), International Conference on, Jul 17, pp. 1-5, IEEE, 2011.