The results obtained on standard IEEE 14 bus and 30 bus systems is compared with simple Genetic Algorithm and Particle Swarm Optimization (PSO) to Optimal Power flow and is found that this approach is more efficient, robust and promising.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:This paper presents a novel and superior Genetic Algorithm (GA) based resolver for Optimal Power flow (OPF) problem.
📊
Key Findings & Data:Here, the main contrast to other Genetic Algorithm based approaches is that a novel expert based initial generation of population and adaptive probability approach (variable Cross over probability and mutation probability) is adopted in selection of offspring together with roulette wheel technique which reduces the computation time and increases the quality considerably.
💡
Conclusion & Impact:The results obtained on standard IEEE 14 bus and 30 bus systems is compared with simple Genetic Algorithm and Particle Swarm Optimization (PSO) to Optimal Power flow and is found that this approach is more efficient, robust and promising.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
This paper presents a novel and superior Genetic Algorithm (GA) based resolver for Optimal Power flow (OPF) problem. Here, the main contrast to other Genetic Algorithm based approaches is that a novel expert based initial generation of population and adaptive probability approach (variable Cross over probability and mutation probability) is adopted in selection of offspring together with roulette wheel technique which reduces the computation time and increases the quality considerably. Selection and Placement of Shunt Devices are considered as a variable in this novel approach. Here continuous variables like Voltage Profile and discrete variable like transformer tapings are considered while minimizing the Fuel cost. The results obtained on standard IEEE 14 bus and 30 bus systems is compared with simple Genetic Algorithm and Particle Swarm Optimization (PSO) to Optimal Power flow and is found that this approach is more efficient, robust and promising.
🔖
Cite This Article
MAHESWARAPU, S., BENARJI, M., BHASKAR, M. (2022). An expert based initial generation of genetic algorithm with adaptive probability approach for quadratic OPF. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 10 - Sayı: 2, 1257–1266. https://ajindex.com/articles/543713
J. Carpentier, "Optimal Power Flows: Uses, methods and developments", IFAC Symposium on planning and operation of electric energy systems. Rio de Janeiro, July 1985, Invited survey paper
M. Huneault and F. D. Galiana, “A Survey of the Optimal Power Flow Literature,” IEEE Trans. Power Systems, vol. 6, pp. 762-770, May 1991
Dommel, H.W.; Tinney, W.F., "Optimal Power Flow Solutions," power apparatus and systems, IEEE transactions on, vol.PAS-87, no.10, pp.1866-1876, Oct. 1968 [4] Alguacil, N.; Conejo, A.J., "Multiperiod optimal power flow using Benders decomposition," Power Systems, IEEE Transactions on, vol.15, no.1, pp.196-201, Feb 2000 [5] J. A. Momoh, M. E. El-Hawary, and R. Adapa, 1999, “A Review of Selected Optimal Power Flow Literature to 1993 Parts I & II,” IEEE Trans. on Power Systems, Vol. 14, No. 1, pp. 96–111
Overbye TJ, Cheng Xu, Sun Yan. A Comparison of the AC and DC power flow models for LMP Calculations. In: 37th Hawaii international conference on system sciences, 2004, USA
D. E. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning,Addison Wesley Publishing Company, Ind. USA, 1989
Holland J.H., 1975, 'Adaptation in Natural and Artificial System', Ann Arbor, the University of Michigan Press
M. Mitchell, 1996, 'An Introduction to Genetic Algorithms', Cambridge, MIT Press
M. D. Vose, 'The simple Genetic Algorithm: Foundations and theory', Cambridge, MIT Press, 1999
Holland J.H., 1975,'Adaptation in Natural and Artificial System', Ann Arbor, the University of Michigan Press
D. E Goldberg, “Sizing Populations for Serial and Parallel Genetic Algorithms”, in J.D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, 1989
TarekBouktir, Linda Slimani and M. Belkacemi, “Genetic Algorithm for Solving the Optimal Power Flow Problem,” Department of Electrical Engineering, University of Oum El Bouaghi, 04000, Algeria
L.L. Lai, J. T. Ma, R. Yokoma and M. Zhao, “Improved Genetic Algorithms for Optimal Power Flow Under Both Normal and Contingent Operation States”, Electrical Power& Energy System, Vol. 19, pp. 287-292, 1997
Devaraj, D.; Yegnanarayana, B., "Genetic-algorithm-based optimal power flow for security enhancement," Generation, Transmission and Distribution, IEE Proceedings-, vol.152, no.6, pp. 899-905, 4 Nov. 20