Optimal Reactive Power Dispatch Based on Mixed Bacterial Chemotaxis Algorithm

Abstract:

Particle Swarm Optimization (PSO) algorithm converges fast but it is easy to fall into local optimum, and bacterial chemotaxis (BC) algorithm prevents premature convergence and prevents falling into local optimum, so a new mixed bacterial chemotaxis (MBC) algorithm is proposed by combining the PSO with BC. The novel algorithm is applied to reactive power optimization on power system. First the PSO is used to find best solution, then BC is used to find the optimal solution among the selected area of previous step, the reserving elite strategy is introduced to enhance the efficiency of the algorithm, and then the optimal solution is obtained. Through the comparison with PSO and BCC in the reactive power optimization of IEEE30-bus system, the results indicate that MBC not only prevents premature convergence to a large extent, but also keeps a more rapid convergence rate than PSO and BCC.

• Periodical: Applied Mechanics and Materials (Volumes 494-495)
• Pages: 1849-1852
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.494-495.1849
• Online since: February 2014
• Authors: Xiao Ying Zhang*, Chen Li, Zhen Li
• Keywords: Convergence, Mixed Bacterial Chemotaxis, Particle Swarm Optimization (PSO), Reactive Power Optimization

References

[1] Zeng Jiajun, Liu Zhigang, He Shiyu : Study of the reactive power optimization based on reactive power optimization based on sub-region particle swarm algorithm turbines, A Power System Protection and Control, Vol. 40, No. 1(2012), pp.37-42.

DOI: 10.38007/dps.2020.010206

[2] Li weiwu, Wang Hui, Zou Zhijun: Function optimization method based on bacterial colony chemotaxis, Circuits and SyStems, Vol. 10, No. 1(2005), pp.58-63.

[3] Huang Wei, Zhang Cong, Yang Jingyan, in: IEEE Transactions on Power Delivery, (2008).

[4] Zeng Ming, Lu Chunquan, Tian Kuo: Least Squares-support Vector Machine Load Forecasting Approach Optimized by Bacterial Colony Chemotaxis Method, Proceedings of the CSEE, Vo1. 31, No. 34(2011)P. 93-98.

[5] Muller S D, J, Airaghi Marchetto S, Koumoutsakos P. Optimization Based on Bacterial Chemotaxis , in: IEEE Transaction of Evolutionary Computation , Vol. 6, No. 1(2002): 16-29.

DOI: 10.1109/4235.985689