Total views : 223
AGC of an Interconnected Power System under Deregulated Environment using GA-tuned Fuzzy Logic Controller
Objectives: To design a Fuzzy Controller with knowledge of the system for AGC under Deregulated environment. Genetic Algorithm (GA) is proposed to be used to fine-tune the response of FLC by optimizing its gain through minimization of an ITAE based objective function. Methods/Statistical Analysis: Performance of the system is determined using distinct scenario of DISCO participation. The simulation is carried out on a two area model of AGC using MATLAB SIMULINK. Sensitivity analysis is carried out by varying various model parameters. Comparison between the optimised-FLC (GAFLC), hand-tuned FLC and PI controller is tabulated. Findings: It is revealed from the simulation results and the comparison table that GAFLC shows a paramount improvement in the transient response specifications in contrast to a PI and Fuzzy (hand-tuned) controller. Furthermore the GAFLC is able to confine the response within acceptable range even under the parametric changes, thus making the controller robust. The proposed work is novel in that; it takes into account the parametric variations in the system under deregulation. The controller thus designed is intelligent and reduces significant time and effort in tuning the fuzzy controller and at the same time able to arrest the parametric disturbances occurring in the system. Application/Improvements: The GA is effectively utilised as an optimisation tool to make the controller intelligent and thus can be implemented for the use in modern power system.
AGC, Deregulation, Disco-Participation, Fuzzy, GAFLC, Intelligent.
- Elgerd OI. Electric energy systems theory: An Introduction.1971. p. 564.
- Patel RN, Sinha SK, Prasad R. Design of a robust controller for AGC with combined intelligence techniques.International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering. 2008; 2(9):1951–7.
- Dash P, Saikia LC, Sinha N. Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system. International Journal Electronic. Power Energy Systerm. 2014 Feb; 55:429–36.
- Saikia LC, Nanda J, Mishra S. Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. International Journal Electronic Power Energy Systerm. 2011; 33(3):394–401.
- Panda G, Panda S, Ardil C. Automatic generation control of interconnected power system with generation rate constraints by hybrid neuro fuzzy approach. World Acad Sci Eng Technol. 2009; 52:543–8.
- Debbarma S, Saikia LC, Sinha N. Robust two-degree-offreedom controller for automatic generation control of multi-area system. Int J Electr Power Energy Syst. 2014; 63:878–86.
- Hou G, Lou R, Zhang J, Zhang Q, Fan C. T-S model based Fuzzy Logic Controller for AGC system after deregulation considering DPM. Chinese Control and Decision Conference, CCDC 2009; 2009. p. 2231–6.
- Karnavas YL. AGC tuning of an interconnected system after deregulation using Genetic Algorithms. Proceedings of the 5th WSEAS Int Conf on Power Systems and Electromagnetic Compatibility; 2005. p. 218–23.
- Kaur R, Kaur J. PID-controller-based-AGC-under-twoareaderegulated-power-system.doc. Int J Sci Eng Res.2015; 6(6):666–73.
- Kumar N, Tyagi B, Kumar V. Deregulated AGC scheme using dynamic programming controller. Eighteenth National Power Systems Conference (NPSC); 2014. p. 1–6.
- Roy R, Ghoshal SP, Bhatt P. Evolutionary computation based four-area automatic generation control in restructured environment. 2009 International Conference on Power Systems; 2009. p. 1–6.
- Chown GA, Hartman RC. Design and experience with a Fuzzy Logic Controller for Automatic Generation Control (AGC). Proc 20th Int Conf Power Ind Comput Appl; 1997.
- Bhongade S, Gupta HO, Tyagi B. Artificial neural network based automatic generation control scheme for deregulated electricity market. Conference Proceedings IPEC; 2010. p.1158–63.
- Ram P, Jha, Automatic generation control of interconnected hydro-thermal system in deregulated environment considering generation rate constraints. 2010 Int Conf Ind Electron Control Robot. IECR; 2010. p. 148–59.
- Saini JS, Jain V. A Genetic Algorithm optimised Fuzzy Logic Controller for automatic generation control for single area system. J Inst Eng Ser B; 2015.
- Zolfagharifar SA, Karamizadeh F. Developing a hybrid intelligent classifier by using evolutionary learning (Genetic Algorithm and Decision Tree). Indian Journal of Science and Technology. 2016 May; 9(20):1–8.
- Mahwish B, Shah TM, Tariq S. Text embedded into encrypted image based on Genetic Algorithm on piecewise linear chaotic map.Indian Journal of Science and Technology. 2016 Feb; 9(8):1–7.
- Noersasongko E, Julfia FT, Purwanto AS, Pramunendar RA, Supriyanto C. A tourism arrival forecasting using Genetic Algorithm based neural network. Indian Journal of Science and Technology. 2016 Jan; 9(4):1–5.
- Kaghed HN, Al–Shamery SN, Khazaal Al-Khuzaie FE.Multiple sequence alignment based on developed Genetic Algorithm. Indian Journal of Science and Technology. 2016 Jan; 9(2):1–7.
- Najafi S, Dalfard VM, Mohammadi G. Hybrid Genetic Algorithm for network locating problem by considering multi-purpose trip in stochastic state. Indian Journal of Science and Technology. 2011 Sep; 4(9):1–4.
- Bakken BH, Grande OS. Automatic generation control in a deregulated power system. IEEE Trans Power Syst. 1998; 13(4):1401–6.
- Christie RD, Bose A. Load frequency control issues in power system operations after deregulation. Proc Power Ind Comput Appl Conf. 1995; 11(3):1191–200.
- Donde V, Pai MA, Hiskens I. Simulation and optimization in a LFC system after deregulation. IEEE Trans Power Syst.2001; 16(3):481–9.
- Debbarma S, Saikia LC, Sinha N. AGC of a multi-area thermal system under deregulated environment using a non-integer controller. Electr Power Syst Res. 2013; 95:175–83.
- Pati S, Sahu BK, Panda S. Hybrid differential evolution Particle Swarm Optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system. IET Gener Transm Distrib. 2014; 8(11):1789–800.
- Sahu R, Panda S, Padhan S. A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. Int J Electr Power Energy Syst.2015; 64:9–23.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.