A Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for Solving Optimal Power Flow Problem

Shima RAHMANI, Mohsen NIASATI
2.864 641

Abstract


The gravitational search algorithm is one of the new heuristic search optimization methods which are based on gravity law. Despite having high capability, this approach suffers from low search speed duo to lack of memory. To overcome this problem, the particle swarm optimization method has been used. Therefore, in this paper, hybrid particle swarm optimization and gravitational search algorithm has been used to find the solution of optimal power flow. Performance of the proposed method has been evaluated using different objective functions on the IEEE 30-bus and 57-bus test systems. Comparing the results of this method with other methods shows better performance of the proposed method.


Keywords


Optimal power flow, Hybrid particle swarm optimization and gravitational search algorithm, Cost function

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References


Dommel,H.W., Tinney,T.F., “Optimal power flow solutions”, IEEE Transactions on Power Apparatus System, Vol.87, No.5, pp. 1866–1876, 1968.

Minano, RZ., Van Cutsem, T., Milano, F., Conejo, AJ., “Securing transient stability using time-domain simulations within an optimal power flow”, IEEE Transactions on Power System, Vol.25, No.1.pp.243–53,2010.

Alsac, O., Stott, B., “Optimal load flow with steady state security”, IEEE Transactions on Power Apparatus System, Vol.93, No.3, pp.745–51, 1974.

Shoults, R., Sun, D., “Optimal power flow based on P–Q decomposition”, IEEE Transactions on Power Apparatus System, Vol.101, No.2, pp.397–405, 1982.

Mota-Palomino, R., Quintana, VH., “Sparse reactive power rescheduling by a penalty- function linear programming technique”, IEEE Transactions on Power System, Vol.1, No.3, pp.31–9, 1983.

Burchett, RC., Happ, HH., Vierath, DR., “Quadratically convergent optimal power flow”, IEEE Transactions on Power Apparatus System, Vol.103, pp.3267–76, 1984.

Ambriz-Perez, H., Acha, E., Fuerte-Esquivel, CR., “Advanced SVC models for Newton- Raphson load flow and newton optimal power flow studies”, IEEE Transactions on Power Systems, Vol.15, No.1, pp.129-36, 2000.

Wei, H., Sasaki, H., Kubokawa, J., Yokoyama, R.. “An interior point nonlinear programming for optimal power flow problems whit a novel structure data”, IEEE Transactions on Power System, Vol.13, pp.870–7, 1998.

Yan, X., Quantana, VH., “Improving an interior point based OPF by dynamic adjustments of step sizes and tolerances”, IEEE Transactions on Power System, Vol.14, No.2, pp.709– 17, 1999.

Momoh, JA., Zhu, JZ., “Improved interior point method for OPF problems”, IEEE Transactions on Power System, Vol.14, No.3, pp.1114–20, 1999.

Lai, LL., Ma,JT., “Improved genetic algorithms for optimal power flow under both normal and contingent operation states”, International Journal of Electrical Power& Energy System, Vol.19,No.5, pp.287–92,1997.

Bakirtzis, AG., Biskas, PN., Zoumas, CE., Petridis, V., “Optimal power flow by enhanced genetic algorithm”, IEEE Transactions on Power System,Vol.17.No.2, pp.229–36,2002.

Roa-Sepulveda, CA., Pavez-lazo, BJ., “A solution to the optimal power flow using simulated annealing”, International Journal of Electrical Power& Energy System, Vol.25, No1, pp.47– 57,2003.

Varadarajan, M., Swarup, kS., “Solving multi-objective optimal power flow using differential evolution”, IET Generation Transmission Distribution, Vol.2, No.5, pp.720–30, 2008.

Abou El Ela, AA., Abido, MA., Spea, SR., “optimal power flow using differential evolution algorithm”, Electrical Power System Research Vol.80,

No.7 , pp.878–85, 2010.

Yuryevich, J., Wong, KP., “Evolutionary based optimal power flow algorithm”, IEEE Transactions on Power System, Vol.14, No.4, pp.1245–50, 1999.

Sood, YR., “Evolutionary programming based optimal power flow and its validation for deregulated power system analysis”, International Journal of Electrical Power& Energy System, Vol.29, No.1, pp.65–75, 2007.

Abido, MA., “Optimal power flow using particle swarm optimization”, International Journal of Electrical Power& Energy System, Vol.24, No.7, pp.563–71, 2002.

Kim, JY., Mun, KJ., Kim, HS., Park, JH., “Optimal power system operation using parallel processing system and PSO algorithm”, International Journal of Electrical Power& Energy System, Vol.33, No.8, pp.1457–61, 2011.

Duman, S., Güvenç, U., Sönmez, Y., Yörükeren, N., “Optimal power flow using gravitational search algorithm”, Energy Conversion and Management, Vol.59, pp.86–95, 2012.

Warren Liao, T., “Two hybrid differential evolution algorithms for engineering design optimization”, Applied Soft Computing, Vol.10, No.4, pp.1188–1199, 2010.

Oysu, C., Bingul, Z., “Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining”, Engineering Applications of Artificial Intelligence, Vol.22, pp.389–396, 2009.

He, D., Wang, F., Mao, Z., “A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect”, International Journal of Electrical Power & Energy Systems, Vol.30, No1, pp.31–38, 2008.

Li, C.,Zhou, J., “Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm”, Energy Conversion and Management, Vol. 52, pp. 374-381, 2011.

Rezaei Adaryani, M., Karami, A., “Artificial bee colony algorithm for solving multi- objective optimal power flow problem “, Electrical Power& Energy System, Vol.53, pp. 219–230, 2013.

Ghasemi, M., Ghavidel, M., Rahmani Sh., “A novel hybrid algorithm of imperialist competitive algorithm and Teaching learning algorithm for optimal power flow problem with non-Smooth cost functions”, Engineering Applications of Artificial Intelligence, Vol.29, pp.54-69, 2014.

Niknam, T., Narimani, MR., Jabbari, M., Malekpour, AR., “A modified shuffle frog leaping algorithm for multi-objective optimal power flow”, Energy, Vol.36, pp.6420–32,2011.

Sayah, S., Zehar, Kh., “Modified differential evolution algorithm for optimal power flow with non-smooth cost function”, Energy Conversion and Management , Vol.49, pp.3036– 42,2008.

Bakirtzis, AG., Biskas, PN., Zoumas, CE., Petridis, V., “Optimal power flow by enhanced genetic algorithm”, IEEE Transactions on Power System, Vol17, No.2, pp.229–36, 2002.

Niknam, T., Narimani, M.R., Azizipanah-Abarghooee, R., “A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect”, Energy Conversion and Management, Vol.58, pp.197–206, 2012.

MATPOWER..

Vaisakh, K., Srinivas, L.R., “Evolving ant direction differential evolution for OPF with non- smooth cost functions”, Engineering Applications of Artificial Intelligence, Vol.24, pp.426– 436, 2011.