A Modified Droop Controller for Micro-Grid Power Quality Improvement Using Artificial Fish Swarm Algorithm (AFSA)

Authors

  • Abdulwahab J. Yusuf Department of Electrical & Computer Engineering; Ahmadu Bello University, Zaria, Nigeria
  • A. A. Olaniyan Department of Electrical/Electronic Engineering Technology; Auchi Polytechnic, Auchi, Nigeria

Keywords:

Droop Control, Micro-grid, Artificial Fish Swarm Algorithm, Frequency Deviation and Voltage Deviation

Abstract

This paper presents a modiï¬ed droop controller for improving the power quality of micro-grid using Artiï¬cial Fish Swarm Algorithm (AFSA). This is necessary in order to reduce the frequency and voltage deviations that occur when the micro-grid changes from grid-connected to an autonomous mode or when the load changes. AFSA was used to optimally select and tune the droop controller gain parameters in order to achieve an improved response in the frequency and voltage outputs of the micro-grid in the course of islanding. The performance of the AFSA-based droop controller was compared with that of the genetic algorithm (GA) based droop controller using the values of frequency and voltage deviations as performance metrics. And from the results obtained, it was observed that the result obtained from the micro-grid when AFSA was used to optimize the droop controller outperforms that obtained when GA was used in terms of frequency and voltage by 0.213% and 1.0453% respectively. All modeling and analysis were implemented in MATLAB 2015b.

 

References

Abdulraheem, B. S., and Gan, C. K. (2016). Power system frequency stability and control: Survey. International Journal of Applied Engineering Research, 11(8), 5688-5695.

Hassan, M., and Abido, M. (2013). Optimal Power Sharing of an Inverter-Based Autonomous Microgrid. IEEE Transactions on Power Delivery.

Hassan, M., and Abido, M. (2014). Real time implementation and optimal design of autonomous microgrids. Electric Power Systems Research, 109, 118-127.

Mohd, A., Ortjohann, E., Morton, D., and Omari, O. (2010). Review of control techniques for inverters parallel operation. Electric Power Systems Research, 80(12), 1477-1487.

Neshat, M., Sepidnam, G., Sargolzaei, M., and Toosi, A. N. (2014). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997.

Pogaku, N., Prodanović, M., and Green, T. C. (2007). Modeling, analysis and testing of autonomous operation of an inverter-based microgrid. IEEE Transactions on Power Electronics, 22(2), 613-625.

Rahmani, R., and Fakharian, A. (2016). New Control Method of Islanded Microgrid System: A GA and ICA based optimization approach. The Modares Journal of Electrical Engineering, 12(4), 43-52.

Razavi, F., Torani, R., Askarian, I., Asgharizadeh, A., and Masoomi, N. (2012). Optimal design of islanded microgrid using genetic algorithm. Paper presented at the International Conference on Genetic and Evolutionary Methods (GEM'12).

Sanjari, M. J., and Gharehpetian, G. B. (2014). Game-theoretic approach to cooperative control of distributed energy resources in islanded microgrid considering voltage and frequency stability. Neural Computing and Applications, 25(2), 343-351.

Vandoorn, T. L., Vasquez, J. C., De Kooning, J., Guerrero, J. M., and Vandevelde, L. (2013). Microgrids: Hierarchical control and an overview of the control and reserve management strategies. IEEE industrial electronics magazine, 7(4), 42-55.

Wen, Liu, and Li, Z. (2015). Droop Control of Parallel Dual-Mode Inverters Used in Micro Grid. International Conference on Power Electronics and Energy Engineering (PEEE 2015).

Yu, K., Ai, Q., Wang, S., Ni, J., and Lv, T. (2016). Analysis and Optimization of Droop Controller for Microgrid System Based on Small-Signal Dynamic Model.

Downloads

Published

2019-11-16