Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

• Autorzy: Pandiarajan K. , Babulal C. K.

Identyfikatory

DOI

10.2478/aee-2014-0027

• Języki publikacji: EN

Abstrakty

EN

This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

Słowa kluczowe

EN

non-dominated sorting genetic algorithm; generation rescheduling; particle swarm optimization (PSO); differential evolution; overload index

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2014
• Tom: Vol. 63, nr 3
• Strony: 367--384
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wz.

Twórcy

autor

Pandiarajan K.

• Department of Electrical and Electronics Engineering Pandian Saraswathi Yadav Engineering College, Arasanoor, India

autor

Babulal C. K.

• Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India

Bibliografia

• [1] Alsac O., Scott B., Optimal load flow with steady state security. IEEE Transactions on power systems 93(3): 745-751 (1974).
• [2] Stott B., Hobson E., Power system security control calculations using linear programming. IEEE Transactions on power systems 97: 1713-1931 (1978).
• [3] Bakirtzis A.G., Biskas P.N., Zoumas C.E., Petridis V., Optimal power flow by enhanced genetic algorithm. IEEE Transactions on power systems 17(2): 229-236 (2002).
• [4] Abido M.A., Optimal power flow using particle swarm optimization. International Journal of Electrical Power and Energy Systems 24(7): 563-571 (2002).
• [5] Abou El Ela A.A., Abido M.A., Spea S.R., Optimal power flow using differential evolution algorithm. Electric Power Systems Research 80(7): 878-885 (2010).
• [6] Serhat Duman, Ugur Guvenc, Yusuf Sonmez, Nuran Yorukeren, Optimal power flow using gravitational search algorithm. Energy Conversion and Management 39: 86-95 (2012).
• [7] Nampetch Sinsuphan, Uthen Leeton, Thanatchai Kulworawanichpong, Optimal power flow solution using improved harmony search method. Applied Soft Computing 13: 2364-2374 (2013).
• [8] Rezaei Adaryani M., Karami A., Artificial bee colony algorithm for solving multi-objective optimal power flow problem. Electrical Power and Energy Systems 53: 219-230 (2013).
• [9] Udupa A.N., Purushothama G.K., Parthasarathy K., Thukaram D., A fuzzy control for network overload alleviation. International Journal of Electrical Power and Energy Systems 23(2): 119-128 (2001).
• [10] Yunqiang Lu., Ali Abur., Static Security Enhancement via Optimal utilization of thyristor-controlled series capacitors. IEEE Transactions on power systems 17(2): 324-329 (2002).
• [11] Abou EL Ela A.A., Spea S.R., Optimal corrective actions for power systems using multi-objective genetic algorithms. Electric Power System Research 79(5): 722-733 (2009).
• [12] Hazra J., Sinha A.K., Congestion management using multi objective particle swarm optimization. IEEE Transactions on Power Systems 22(4): 1726-1734 (2007).
• [13] Venkaiah Ch., Vinod Kumar D.M., Fuzzy PSO congestion management using sensitivity-based optimal active power rescheduling of generators. Journal of Electrical Engineering & Technology 6(1): 32-41 (2011).
• [14] Ghahremani E., Kamwa I., Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface. IEEE Transactions on Power Systems 28(2): 764-778 (2013).
• [15] Gnanambal K., Babulal C.K., Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization. Electrical Power and Energy Systems 43(1): 150-155 (2012).
• [16] Kennedy J., Eberhart R., Particle swarm optimization. IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948 (1995).
• [17] Storn R., Price K., Differential Evolution- a simple and efficient Heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4): 341-359 (1997).
• [18] Srinivas N., Deb K., Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation 2(3): 221-248 (1994).
• [19] Kalyanmoy Deb., Amrit Pratap., Sameer Agarwal., Meyarivan T., A Fast Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2):182-197 (2002).
• [20] Brar Y.S., Dhillon J.S., Kothari D.P., Multi-objective load dispatch based on genetic-Fuzzy techniques. Power System Conference and Exposition, pp. 931-936 (2006).
• [21] Bath S.K., Dillon J.S., Kothari D.P., Stochastic multi-objective generation allocation using patternsearch method. IEE Proceedings, Generation, Transmission and Distribution 153: 476-484 (2006).
• [22] Sujatha Balaraman., Kamaraj, N., Transmission congestion management using particle swarm optimization. Journal of Electrical Systems 7(1): 54-70 (2011).
• [23] Devaraj D., Yegnanarayana B., Genetic algorithm based optimal power flow for security enhancement. IEE Proceedings, Generation, Transmission and distribution 152(6): 899-905 (2005).
• [24] Narmatha Banu R., Devaraj D., Multi-objective Evolutionary Algorithm for Security Enhancement. Journal of Electrical Systems5(4), (2009).