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Studding the Load Frequency Behavior in Power Systems via Using Expert Techniques

M. I. El-Sayed

Abstract


An expert system's knowledge is obtained from many sources and the code of them written in a form proper for the system to use in its inference or reasoning processes. And the goal of Load Frequency Control is to make changing in the power output of the electric generator in the area to change the system frequency. The LFC used to maintain the system frequency and tie-line power with the other areas within the arranged limits. This paper studies the using of modern tuning technique to control the load frequency in two areas power system using PID controller. By tuning the PID parameters this tuning technique depend on the expert systems. An expert system is a set of program that manipulates encoded knowledge to solve problem in a specialized domain that normally requires human expertise. Once a sufficient body of expert knowledge found, it must be programmed in some form, loaded into a data base, then tested, and refined repeatedly during the system. By using MATLAB/SIMULINK software. The Simulations are done for the two different areas with same parameters of PID. The used method in this paper is Adaptive Acceleration Coefficients based PSO (AACPSO). Then the comparison has been carried out for this method and many methods used in the old study. Therefore, this paper introduce some modern techniques for Load Frequency Control. The proposed techniques based on Artificial Intelligence / technique and expert system. It gives promising results.


Keywords


Expert Systems, Load Frequency Control Adaptive Acceleration Coefficients, Particle Swarm Optimization.

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References


Ahmed, S.,Tarek, B. and Djemai, N. (2013, May). Economic Dispatch Resolution using Adaptive Acceleration Coefficients based PSO considering Generator Constraints, International Conference on Control, Decision and Information Technologies (CoDIT’13).

Bevrani, H. (2009). Robust power system frequency control. Brisbane, Australia: Springer Science + Business Media, LLC.

Hamid, A., & Abdul Rahman, T. K. (2010, March). Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm. In Computer Modelling and Simulation (UKSim), 12th International Conference on (pp. 408-413). IEEE.

Ismail, A. (2006, April). Improving UAE power systems control performance by using combined LFC and AVR. In The Seventh UAE University Research Conference, ENG (pp. 50-60).

Bahgaat, N.K., El-Sayed, M.I., Moustafa Hassan, M.A. and Bendary, F.A.” Load Frequency Control in Power System via Improving PID Controller Based on Particle Swarm Optimization and ANFIS Techniques”, International Journal of System Dynamics Applications (IJSDA), IGI-Global, USA, Vol.3, No. 3, Pp. 1-24 (2014).

Ismail, M. M., & Hassan, M. A. (2012). Using Positive and Negative Sequence Components of Currents and Voltages for High Impedance Fault Analysis via ANFIS. International Journal of System Dynamics Applications (IJSDA), 1(4), 132-157.

RamaSudha, K., Vakula, V. S., &Shanthi, R. V. (2010). PSO Based Design of Robust Controller for Two Area Load Frequency Control with Nonlinearities. International Journal of Engineering Science, 2 (5), 1311-1324.

Rania H. Mansour (2012). Development of advanced controllers using adaptive weighted PSO algorithm with applications, M. Sc Thesis, Faculty of Engineering, Cairo University, Cairo, Egypt.

Salami, A., Jadid, S., &Ramezani, N. (2006, November). The Effect of load frequency controller on load pickup during restoration. In Power and Energy Conference, 2006. PECon'06. IEEE International (pp. 225-228). IEEE.

Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of process control, 13(4), 291-309.

Tammam, M. A., Aboelela, M. A. S., Moustafa, M. A., &Seif, A. E. A. (2012, June). Load Frequency Controller Design for Interconnected Electric Power System. 55th Annual Power Industry division Symposium POWID 2012, Austin, Texas, USA.

Tammam, M.A. (2011). Multi objective genetic algorithm controllers Tuning for load frequency control in Electric power systems. Cairo, Egypt: M. Sc. Thesis, Faculty of Engineering at Cairo University.

Wang, Y., Zhou, R., & Wen, C. (1993). Robust load-frequency controller design for power systems. In IEE Proceedings C (Generation, Transmission and Distribution) (Vol. 140, No. 1, pp. 11-16). IET Digital Library. Waterman, D. (1986). A guide to expert systems.

Hassanien AE, Tolba M, Azar AT (2014) Advanced Machine Learning Technologies and Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings, Communications in Computer and Information Science, Vol. 488, Springer-Verlag GmbH Berlin/Heidelberg. ISBN: 978-3-319-13460-4.

Zhu Q and Azar AT (2015) Complex system modeling and control through intelligent soft computations. Studies in Fuzziness and Soft Computing, Vol. 319, Springer-Verlag, Germany. ISBN: 978-3-319-12882-5.

Azar AT and Serrano FE (2015). Design and Modeling of Anti Wind Up PID Controllers. In: Q. Zhu, A.T Azar (eds.), Complex system modelling and control through intelligent soft computations, Studies in Fuzziness and Soft Computing, Vol. 319, pp 1-44, Springer-Verlag, Germany. DOI 10.1007/978-3-319-12883-2_1. 1.


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