1Instructor , Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2MSc, Department of Control, Imam Mohammad Bagher University, Sari, Iran
3MSc, Department of Electrical and Computer, College of Engineering, Khash branch, Islamic Azad University, Khash,
A multi objective Honey Bee Mating Optimization (HBMO) designed by online learning mechanism is proposed in this paper to optimize the double Fuzzy-Lead-Lag (FLL) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. The proposed double FLL stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the proposed multi objective optimization process. A multilayer adaptive network is employed to design the fuzzy logic controller with self-learning capability that does not require another controller to tune the fuzzy inference rules and membership functions. In the proposed online learning algorithm, two artificial neural networks are employed which this system makes the FLL stabilizer adaptive to changes in the operating conditions. Therefore, variation in the power system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter conventional controller. The effectiveness of the proposed stabilizer has been employed by simulation studies. The effectiveness of the proposed stabilizer is demonstrated on Two-Area Four-Machine (TAFM) power system under different loading conditions.
Abd-Elazim S.M., Ali E.S., (2013) “A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design,” International Journal Electric Power Energy Syst, vol. 46, pp. 334–341.
Baek S. M., Park J. W., Venayagamoorthy G. K., (2008) “Power System Control With an Embedded Neural Network in Hybrid System Modeling,” IEEE Trans. Ind. Applications, vol. 44, no. 5, pp. 1458- 1465.
Dounis I. A., Kofinas P., Alafodimos C., Tseles D., (2013) “Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system,” Renewable Energy, vol. 60, pp. 202-214.
Ghasemi A., (2013) “A Fuzzified Multi Objective Interactive Honey Bee Mating Optimization for Environmental/Economic Power Dispatch with valve point effect,” Int J Elec Power Energy Syst, vol. 49, pp. 308–321.
Ghasemi A., Abido M.A., (2012) “Optimal Design of Power System Stabilizers: A PSO-IIW Procedure,” 27th International Power System Conference, pp. 1-11.
Ghasemi A., Shayeghi H., Alkhatib H., (2013) “Robust Design of Multimachine Power System Stabilizers using Fuzzy Gravitational Search Algorithm,” Int J Elec Power Energy Syst, vol. 51, pp. 190-200.
Gonzalez M. R., Malik O. P., (2008) “Power System Stabilizer Design Using an Online Adaptive Neurofuzzy Controller With Adaptive Input Link Weights,” IEEE Trans. Energy Conversion, vol. 23, no. 3, pp. 914- 922.
Javidan J., Ghasemi A., (2012) “Environmental/Economic Power Dispatch Using Multi-Objective Honey Bee Mating Optimization,” Int Review Elec Engineering, vol. 7, no. 1, pp. 3667-3675.
Lu C. F., Hsu C. H., Juang C. F., (2013) “Coordinated Control of Flexible AC Transmission System Devices Using an Evolutionary Fuzzy Lead-Lag Controller With Advanced Continuous Ant Colony Optimization,” IEEE Trans. Power Systems, vol. 28, no. 1, pp. 385-392.
Mishra S., Tripathy M., Nanda J., (2007) “Multi-machine power system stabilizer design by rule based bacteria foraging,” Electric Power Systems Research, vol. 77, pp. 1595–1607.
Mostafa H. E., El-Sharkawy M. A., Emary A. A., Yassin K., (2012) “Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system,” International Journal Electric Power Energy Syst, vol. 34, pp. 57–65.
Noshyar M., Shayeghi H., Talebi A., Ghasemi A., Tabatabaei N.M., (2013) “Robust fuzzy-PID controller to enhance low frequency oscillation using improved particle swarm optimization,” International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Vol.5, No. 1, pp. 17-23.
Park J.-W., Venayagamoorthy G. K., Harley R. G., (2005) “MLP/RBF neural-networks-based online global model identification of synchronous generator,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 1685–1695.
Shabib G., (2012) “Implementation of a discrete fuzzy PID excitation controller for power system damping,” Ain Shams Engineering Journal, vol. 3, pp. 123-131.
Shayanfar H.A., Abedinia O., Mohammad. S. Naderi, Ghasemi A., (2011) “GSA to Tune Fuzzy Controller for Damping Power System Oscillation,” InProceedings of the international conference on artificial intelligence, Las Vegas, Nevada, pp: 713-719.
Shayeghi H., Ghasemi A., (2011) “Improved Time Variant PSO Based Design of Multiple Power System Stabilizer, “Int Review Elec Engineering, Vol. 6, No. 5, pp. 2490-2501, 2011.
Shayeghi H., Ghasemi A., (2011) “Multiple PSS Design Using an Improved Honey Bee Mating Optimization Algorithm to Enhance Low Frequency Oscillations, “Int Review Elec Engineering, vol. 6, no. 7, pp. 3122-3133.
Shayeghi H., Ghasemi A., (2012) “Optimal design of power system stabilizer using improved ABC algorithm,” Int J Technical Physical Prob Engineering, vol. 4, no. 3, pp. 24-31.
Shayeghi H., Ghasemi, A. (2014) “A multi objective vector evaluated improved honey bee mating optimization for optimal and robust design of power system stabilizers,” Electrical Power and Energy Systems, vol. 62, pp. 630–645.
Shayeghi H., Shayanfar H.A., Jalili A., Ghasemi A., (2010) “LFC design using HBMO technique in interconnected power system,” International Journal on “Int J Technical Physical Prob Engineering, vol. 2, no. 4, pp. 41-48.
Wang S. K., (2013) “A Novel Objective Function and Algorithm for Optimal PSS Parameter Design in a Multi-Machine Power System,” IEEE Trans. Power Systems, vol. 28, no. 1, pp. 522- 531.
Yassami H., Darabi A., Rafiei S.M.R., (2010) “Power system stabilizer design using Strength Pareto multi-objective optimization approach,” Electric Power Systems Research, vol. 80, pp. 838–846.