Abstract
This study extensively represents a practical power system using various sources in each control area connected through AC/DC transmission link. Each area includes reheat thermal, hydro and nuclear power plant using proper generation rate constraints. A new population-based algorithm, i.e. fruit fly optimization algorithm (FOA), is applied for tuning purpose. Initially, the parameters of integral (I) controller and parameters of HVDC are optimized with FOA algorithm employing integral time absolute error (ITAE) as an objective function, and to show supremacy of FOA technique, the system performances are compared with GA and PSO algorithms. Further, performances of different conventional controllers like integral-derivative (ID), proportional-integral (PI), proportional-integral-derivative (PID), integral-double-derivative (IDD) and proportional-integral-double-derivative (PIDD) are compared with FOA algorithm for the concerned system. The investigation reveals that PIDD controller tuned with FOA algorithm outperforms than other controllers. Additionally, sensitivity analysis is executed with parameters of the system and operative load conditions variation. It is observed from simulation outcomes that the optimum gains of the controller are robust enough to sustain wide variation in loading condition and system parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Elgard, O.I.: Electric Energy Systems Theory. Mc Graw-Hill, New York (1982)
Saikia, L.C., Nanda, J., Mishra, S.: Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system. Electr. Power Energy Syst. 33, 394–401 (2011)
Singh, O.: Design of optimal state feedback controller for AGC using a hybrid stochastic search. In: 2008, IEEE Conference, Powercon, IIT Delhi (2008)
Demiroren, A., Yesil, E.: Automatic generation control with fuzzy logic controllers in the power system including SMES units. Electr. Power Energy Syst. 26, 291–305 (2004)
Mohanty, B.: TLBO optimized sliding mode controller for multi-area multi-source nonlinear interconnected AGC system. Int. J. Electr. Power Energy Syst. 73, 872–881 (2015)
Taher, S.A., Fini, M.H., Aliabadi, S.F.: Fractional order PID controller design for LFC in electric power systems using imperialist competitive algorithm. Int. J. Ain Shams Eng. 5, 121–135 (2014)
Ibraheem, Nizamuddin, Bhatti, T.S.: AGC of two area power system interconnected by AC/DC links with diverse sources in each area. Electr. Power Energy Syst. 55:297–304 (2014)
Saikia, L.C., Sinha, N., Nanda, J.: Maiden application of bacterial foraging based fuzzy IDD controller in AGC of a multi-area hydrothermal system. Electr. Power Energy Syst. 45, 98–106 (2013)
Ali, E.S., Abd-Elazim, S.M.: Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Electr. Power Energy Syst. 33, 633–638 (2011)
Bhatt, P., Roy, R., Ghoshal, S.P.: GA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two-area multi-units automatic generation control. Electr. Power Energy Syst. 32, 299–310 (2010)
Gozde, H., Taplamacioglu, M.C., Kocaarslan, I.: Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system. Electr. Power Energy Syst. 42, 167–178 (2012)
Rout, U.K., Sahu, R.K., Panda, S.: Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Eng. J. 3, 409–421 (2012)
Guha, D., Roy, P.K., Banerjee, S.: Quasi-oppositional symbiotic organism search algorithm applied to load frequency control. Int. J Swarm Evol. Comput. 33, 46–67 (2017)
Nandi, M., Shiva, C.K., Mukherjee, V.: TCSC based automatic generation control of deregulated power system using quasi-oppositional harmony search algorithm. Int. J. Eng. Sci. Technol. 20, 1380–1395 (2017)
TPan, W.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26, 69–74 (2012)
Li, H.Z., Guo, S., Zhao, H.R., Su, C.B., Wang, B.: Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Energies 5, 4430–4445 (2012)
Ramachandran, B., Bellarmine, G.T.: Improving observability using optimal placement of phasor measurement units. Electr. Power Energy Syst. 56, 55–63 (2014)
Zhao, J., Yuan, X.: Multi-objective optimization of stand-alone hybrid PV-wind-diesel-battery system using improved fruit fly optimization algorithm. Soft Comput. 20, 2841–2853 (2016)
Li, C., Xu, S., Li, W., Hu, L.: A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller. J. Converg. Inf. Technol. 7, 69–77 (2012)
Zheng, X.L., Wang, L., Wang, S.Y.: A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl.-Based Syst. 57, 95–103 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Acharyulu, B.V.S., Swamy, S.K., Mohanty, B., Hota, P.K. (2019). Performance Comparison of FOA Optimized Several Classical Controllers for Multi-area Multi-sources Power System. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_42
Download citation
DOI: https://doi.org/10.1007/978-981-13-0514-6_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0513-9
Online ISBN: 978-981-13-0514-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)