A Hybrid Fuzzy AGC in a Competitive Electricity Environment
This paper presents a new Hybrid Fuzzy (HF) PID type controller based on Genetic Algorithms (GA-s) for solution of the Automatic generation Control (AGC) problem in a deregulated electricity environment. In order for a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membership functions, because it is a computationally expensive combinatorial optimization problem. On the other hand, GAs is a technique that emulates biological evolutionary theories to solve complex optimization problems by using directed random searches to derive a set of optimal solutions. For this reason, the membership functions are tuned automatically using a modified GA-s based on the hill climbing method. The motivation for using the modified GA-s is to reduce fuzzy system effort and take large parametric uncertainties into account. The global optimum value is guaranteed using the proposed method and the speed of the algorithm-s convergence is extremely improved, too. This newly developed control strategy combines the advantage of GA-s and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed GA based HF (GAHF) controller is tested on a threearea deregulated power system under different operating conditions and contract variations. The results of the proposed GAHF controller are compared with those of Multi Stage Fuzzy (MSF) controller, robust mixed H2/H∞ and classical PID controllers through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084844Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1441
 R. Raineri, S. Rios, D. Schiele, Technical and Economic Aspects of Ancillary Services Markets in the Electric Power Iindustry: an International Comparison, Energy Policy, Vol. 34, No. 13, 2006, pp. 1540 - 1555.
 R. D. Christie, A. Bose, Load frequency control issues in power system operations after deregulation, IEEE Trans. On Power Systems, Vol. 11, No. 3, 1996, pp. 1192 - 1200.
 J. Kumar, N. G. Hoe, G. Sheble, LFC Simulator for Price-based Operation Part I: Modeling, IEEE Trans. On Power Systems, Vol. 12, No. 2, 1997, pp. 527 - 532.
 V. Donde, A. Pai, I. A. Hiskens, Simulation and optimization in a LFC system after deregulation, IEEE Trans. On Power Systems, Vol. 16, No. 3, 2001, pp. 481 - 489.
 M. Kazemi M. Karrari, M. Menhajm, Decentralized Robust Adaptive-output Feedback Controller for Power System Load Frequency Control, Electrical Engineering, Vol. 84, No. 2, 2002, pp. 75 - 83.
 H. L. Zeynelgil, A. Demiroren and N. S. Sengor, The Application of ANN Technique to Automatic Generation Control for Multi-area Power System, Electrical Power Energy System, Vol. 24, 2002, pp. 545-354.
 H. Bevrani, Y. Mitani, and K. Tsuji, Robust Decen-tralized AGC in a Restructured Power System, Ener-gy Conversion and Management, Vol. 45, 2004, pp. 2297 - 2312.
 K.Y. Lim, Y. Wang, and R. Zhou, Robust Decentral-ized Load Frequency Control of Multi-area Power Systems, IEE Proc. On Gener. Transm. Distrib. , Vol. 143, No. 5, 1996, pp. 377 - 386.
 C. Chang, W. Fu, Area Load Frequency Control Using Fuzzy Gain Scheduling of PI Controllers, Electric Power System Research, Vol. 42, 1997, pp. 145-152.
 A. Feliachi, On Load Frequency Control in a Restructured Environment, Proc. of the IEEE International Conference on Control Application, 15-18 Sept. 1996, pp. 437-441.
 H. Shayeghi, H. A. Shayanfar, A. Jalili, M. Khazaraee, Area Load Frequency Control Using Fuzzy PID Type Controller in a Restructured Power System, Proc. of the Int. Conf. on Artificial Intelligence, Las Vegas Nevada, USA, June 27-30 2005, pp. 344-350.
 E. Yesil, M. Guzelkaya, I. Eksin, Self Tuning Fuzzy PID Type Load Frequency Controller, Energy Convers. and Manage., 45 (2004) 377-390.
 M. Petrov, I. Ganchev, A. Taneva, Fuzzy PID control of nonlinear plants, In Proceedings of the First International IEEE Symposium on Intelligence systems, Sep. 2002, pp. 30-35.
 H. Shayeghi, H. A. Shayanfar, A. Jalili, Multi stage fuzzy PID power system automatic generation controller in the deregulated environment, Energy Conversion and Management, Vol. 47, No. 18, 2006, pp. 2829-2845.
 M. James, Adams, Multi-Stage Fuzzy PID Controller with Fuzzy Switch, Proc. of the IEEE Int. Conference on Control Application, Vol, 4, 2001, pp. 323-327.
 M. James, Adams, Intelligent Control of a Direct-drive Robot Using Multi-Stage Fuzzy Logic, Midwest Symposium on Circuits and Systems, Vol. 2, 2001, pp. 543- 546.
 D. E Goldberg, Genetic Algorithms in Search and Machine Learning, Reading, MA: Addison-Wesley, 1989.
 F. Herrera, J. L. Verdegay, Genetic algorithms and Soft Computing, Physica- Verlag Heidelberg; 1996.
 J. McCall, Genetic Algorithms for Modeling and Optimization, Journal of Computational and Applied Mathematics, Vol. 84, No. 1, 2005, pp. 205-222.
 H. Shayeghi, H. A. Shayanfar, O. P. Malik, "Robust Decentralized Neural Networks Based LFC in a Deregulated Power System", Electric Power System Research, Vol. 77, No. 3, 2007, pp. 241-251.
 Y.P. Kuo, T. H. S. Li, "GA-based Fuzzy PI/PD Controller for Automotive Active Suspension System", IEEE Trans. On Industrial Electronics, Vol. 46, No. 6, 1999, pp. 1051-1056.
 Carvajd, G. Chen, H. Ogmen, Fuzzy PID Controller: Design, Performance Evaluation and Stability Analysis, Information Sciences, Vol. 123, 2000, pp. 249- 270.
 A.Visioli, Tuning of PID Controllers with Fuzzy Logic, Proc. of the IEEE Int. Conf. on Control Theory and Applications,, Vol. 4, No. 1, January 2001, pp. 1-8.
 T. Pal, N. R. Pal, SOGARG: A Self-Organized Genetic Algorithm-based Rule generation Scheme for Fuzzy Controllers, IEEE Trans. On Evolutionary Computation, Vol. 7, No. 4, 2003, pp. 397-415.
 H. Shayeghi, H. A. Shayanfar, Application of ANN Technique Based on ╬╝- Synthesis to Load Frequency Control of Interconnected Power System, Electrical Power and Energy Systems, Vol. 28, No. 7, 2006, pp. 503-511.