Load Frequency Control for Two-area Multi-Source Interconnected Power System using Intelligent Controllers
Main Article Content
Abstract
This paper presents the study of intelligent controllers for Two-area multi-source interconnected power system model. The controller gains are optimized using Conventional method, GA and BAT algorithms and investigation is carried out for the best optimization method on the basis of dynamic performance and stability of the power system model. The power system model under investigation two area each area consists of thermal, hydro and Double Fed Induction Generator (DFIG) based wind unit with different participation factor in the total generation for their respective area. It has been observed that an appreciable improvement in the system dynamic performance is achieved using Bat algorithms for load frequency controller for multisource power system model as compared with conventional method and GA algorithm.
Metrics
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE http://creativecommons.org/licenses/by/4.0/
Plaudit
References
Elgerd OI. Electrical energy systems theory: an introduction. 2nd edn., New Delhi, India: Tata McGraw Hill; 1971.
Elgerd OI, Fosha CE. Optimum megawatt-frequency control of multi-area electric energy systems. IEEE Transaction on Power Apparatus and System 1970; 89 (4): 556–563. DOI: https://doi.org/10.1109/TPAS.1970.292602
Ramakrishna KSS, Bhatti TS. Load frequency control of interconnected hydro-thermal power systems. International Conference on Energy and Environment 2006; 1 (8)
Yang XS. A new metaheuristic bat-inspired algorithm, in Gonzalez JR et al (Ed.), nature inspired cooperative strategies for optimization, Studies in Computational Intelligence, 65-74; 2010 DOI: https://doi.org/10.1007/978-3-642-12538-6_6
Nanda J, Mishra S, Saikia LC. Maiden application of bacterial foraging-based optimization technique in multi-area load frequency control. IEEE Transactions on Power Systems 2009; 24 (2): 602-609. DOI: https://doi.org/10.1109/TPWRS.2009.2016588
Lu ZS, Hou ZR. Particle swarm optimization with adaptive mutation. Acta Electronica Sinica 2004; 32 (2) 3: 416-420.
Kennedy, Eberhart R. Particle swarm optimization. IEEE International Conference Neural Networks 1995: p. 1942-1948.
Shi YH, Eberhart RC. A modified particle swarm optimizer. Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference 1998; Anchorage Alaska: p. 69-73.
Nickabadi A, Ebadzadeh M, Safabakhsh R. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 2011; 11: 3658-3670. DOI: https://doi.org/10.1016/j.asoc.2011.01.037
Al-Hamouz ZM, Al-Duwaish HN. A new load frequency variable structure controller using genetic algorithms. Electrical Power System. Research 2000; 55 (1):1-6. DOI: https://doi.org/10.1016/S0378-7796(99)00095-4
Aditya SK, Das D. Design of load frequency controllers using genetic algorithm for two area interconnected hydropower system. Electrical Power Component System 2003; 31 (1): 81-94. DOI: https://doi.org/10.1080/15325000390112071
Ghoshal SP. Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system. Electrical Power System Research 2004; 70 (2):115-127. DOI: https://doi.org/10.1016/j.epsr.2003.11.013
Bhatt P, Roy R, Ghoshal S. GA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two-area multi-units automatic generation control. International Journal of Electrical Power and Energy System 2010; 32 (4): 299 – 310. DOI: https://doi.org/10.1016/j.ijepes.2009.09.004
Yang XS. Harmony search as a metaheuristic algorithm", in music inspired harmony search algorithm: Theory and applications. Studies in Computational Intelligence 2009; 191: 1-14. DOI: https://doi.org/10.1007/978-3-642-00185-7_1
Yang XS. Nature-inspired metaheuristic algorithms. 2nd edn., Luniver Press; 2008.
Nakamura R, Pereira L, Costa K, Rodrigues D, Papa J, Yang XS. BBA: A binary bat algorithm for feature selection. 25th SIBGRAPI Conference on Graphics, Patterns and Images 2012 August 22-25; p. 291-297. DOI: https://doi.org/10.1109/SIBGRAPI.2012.47
Kundur P. Power system stability analysis. USA: Mc-Graw-Hill Inc;1994.
Saikia LC, Debbarma S, Sinha N, Dash P. AGC of a multi-area hydrothermal system using thyristor controlled series capacitor. Annual IEEE India Conference 2013; India. DOI: https://doi.org/10.1109/INDCON.2013.6725906
Abraham RJ, Das D, Patra A. AGC study of a hydrothermal system with SMES and TCPS. European Transactions on Electrical Power 2009; 19 (3): 487–498. DOI: https://doi.org/10.1002/etep.235
Rao CS, Nagaraju SS, Raju PS. Automatic generation control of TCPS based hydrothermal system under open market scenario: a fuzzy logic approach. International Journal of Electrical Power & Energy Systems 2009; 31 (7–8): 315–322. DOI: https://doi.org/10.1016/j.ijepes.2009.03.007
Bhatt P, Roy R, Ghoshal S. Comparative performance evaluation of SMES–SMES, TCPS–SMES and SSSC–SMES controllers in automatic generation control for a two-area hydro–hydro system. International Journal of Electrical Power & Energy Systems 2011; 33 (10): 1585-1597. DOI: https://doi.org/10.1016/j.ijepes.2010.12.015