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Load Frequency Control of a Two Area Multi-source Power System with Electric Vehicle

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Abstract

The purpose of automatic generation control (AGC) is to maintain a balance between the total output of the generation system against the losses at the load side in order to maintain the desired frequency and the power exchange with the neighboring areas. If there is a mismatch between generated power and the demand, the grid frequency deviates from the rated value. This high frequency offset may results in complete failure of the power system. Both load frequency control (LFC) loop and automatic voltage regulation loop (AVR) connected together in electrical systems regulates the energy flows and frequencies by AGC. The LFC system provides load control of the generator through zero frequency Steady state error and optimal transient behavior which are the goals of the LFC in an interconnected multi-zone electrical system. This paper presents a hybrid ANFIS-ANN based load frequency controller oriented by Particle Swarm Optimization (PSO) algorithm. This technique can be implemented in a two area interconnected system in order to maintain the system frequency within the nominal value and to prevents accidental exchange of power between the two areas during load changes. The proposed method is tested in the MATLAB software to estimate its performance and the results are compared with conventional PID controller, Artificial Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN) based controller to prove its supremacy.

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References

  • Aravindan, P., & Sanavullah, M. (2009). Fuzzy logic based automatic load frequency control of two area power system with grc. International Journal of Computational Intelligence Research, 5(1), 37–45.

    Google Scholar 

  • Chan, F., Tiwari, M. (2007). Swarm Intelligence: Focus on ant and particle swarm optimization. BoD–Books on Demand.

  • Das, N. K., Saikia, P. M., Buragohain, D. M., Saikia, N. (2022). An adaptive controller design using duelist optimization algorithm for an interconnected power system. https://doi.org/10.35940/ijeat.d3410.0411422

  • Deng, W., Zhao, H., Yang, X., Xiong, J., Sun, M., & Li, B. (2017). Study on an improved adaptive pso algorithm for solving multi-objective gate assignment. Applied Soft Computing, 59, 288–302.

    Article  Google Scholar 

  • Gaur, P., Bhowmik, D., & Soren, N. (2019). Utilisation of plug-in electric vehicles for frequency regulation of multi-area thermal interconnected power system. IET Energy Systems Integration, 1(2), 88–96.

    Article  Google Scholar 

  • Guha, D., Roy, P. K., & Banerjee, S. (2016). Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Engineering Science and Technology, an International Journal, 19(4), 1693–1713.

    Article  Google Scholar 

  • Jagatheesan, K., Anand, B., Samanta, S., Dey, N., Santhi, V., Ashour, A. S., & Balas, V. E. (2017). Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity. Neural Computing and Applications, 28(1), 475–488.

    Article  Google Scholar 

  • Khosraviani, M., Jahanshahi, M., Farahani, M., & Bidaki, A. R. Z. (2018). Load-frequency control using multi-objective genetic algorithm and hybrid sliding mode control-based smes. International Journal of Fuzzy Systems, 20(1), 280–294.

    Article  MathSciNet  Google Scholar 

  • Kumar, P., Kothari, D. P., et al. (2005). Recent philosophies of automatic generation control strategies in power systems. IEEE Transactions on Power Systems, 20(1), 346–357.

    Article  Google Scholar 

  • Li, H., Wang, X., & Xiao, J. (2019). Adaptive event-triggered load frequency control for interconnected microgrids by observer-based sliding mode control. IEEE Access, 7, 68271–68280.

    Article  Google Scholar 

  • Nanda, J., Mangla, A. (2004). Automatic generation control of an interconnected hydro-thermal system using conventional integral and fuzzy logic controller. In 2004 IEEE international conference on electric utility deregulation, restructuring and power technologies. Proceedings, IEEE, vol 1, pp. 372–377.

  • Nanda, J., Sakkaram, J. (2003). Automatic generation control with fuzzy logic controller considering generation rate constraint. IET Journal.

  • Panda, G., Panda, S., & Ardil, C. (2009). Automatic generation control of interconnected power system with generation rate constraints by hybrid neuro fuzzy approach. International Journal of Electrical Power and Energy Systems Engineering, 2(1), 13–18.

    Google Scholar 

  • Pezzato, C., Ferrari, R., & Corbato, C. H. (2020). A novel adaptive controller for robot manipulators based on active inference. IEEE Robotics and Automation Letters, 5(2), 2973–2980.

    Article  Google Scholar 

  • Pradhan, C., & Bhende, C. N. (2019). Online load frequency control in wind integrated power systems using modified jaya optimization. Engineering Applications of Artificial Intelligence, 77, 212–228.

    Article  Google Scholar 

  • Prakash, S., & Sinha, S. (2014). Simulation based neuro-fuzzy hybrid intelligent pi control approach in four-area load frequency control of interconnected power system. Applied Soft Computing, 23, 152–164.

    Article  Google Scholar 

  • Prakash, S., Sinha, S. K., Pandey, A. S., & Singh, B. (2009). Impact of slider gain on load frequency control using fuzzy logic controller. ARPN Journal of Engineering and Applied Sciences, 4(7), 20–7.

    Google Scholar 

  • Qais, M. H., Hasanien, H. M., & Alghuwainem, S. (2020). Whale optimization algorithm-based sugeno fuzzy logic controller for fault ride-through improvement of grid-connected variable speed wind generators. Engineering Applications of Artificial Intelligence, 87, 103328.

    Article  Google Scholar 

  • Ram, P., & Jha, A. (2010). Automatic generation control of interconnected hydro-thermal system in deregulated environment considering generation rate constraints. 2010 International Conference on Industrial Electronics (pp. 148–159). IEEE: Control and Robotics.

  • Ramachandran, R., Madasamy, B., Veerasamy, V., & Saravanan, L. (2018). Load frequency control of a dynamic interconnected power system using generalised hopfield neural network based self-adaptive pid controller. IET Generation, Transmission & Distribution, 12(21), 5713–5722.

    Article  Google Scholar 

  • Shankar, G., Lakshmi, S. (2015). Frequency control of hybrid renewable energy system with pso optimized controller. In 2015 international conference on recent developments in control, automation and power engineering (RDCAPE), IEEE, pp. 220–225.

  • Shankar, G., & Mukherjee, V. (2016). Quasi oppositional harmony search algorithm based controller tuning for load frequency control of multi-source multi-area power system. International Journal of Electrical Power & Energy Systems, 75, 289–302.

    Article  Google Scholar 

  • Shayeghi, H., Shayanfar, H., & Jalili, A. (2009). Load frequency control strategies: A state-of-the-art survey for the researcher. Energy Conversion and Management, 50(2), 344–353.

    Article  MATH  Google Scholar 

  • Talaq, J., & Al-Basri, F. (1999). Adaptive fuzzy gain scheduling for load frequency control. IEEE Transactions on Power Systems, 14(1), 145–150.

    Article  Google Scholar 

  • Zhang, Y., Sen, P., & Hearn, G. E. (1995). An on-line trained adaptive neural controller. IEEE Control Systems Magazine, 15(5), 67–75.

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Author notes

  1. Nipan Kumar Das contributed to this work.

Authors and Affiliations

  1. Electrical Engineering, Jorhat Engineering college, Jorhat, Assam, 785007, India

    Nikhil Saikia & Nipan Kumar Das

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  1. Nikhil Saikia
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  2. Nipan Kumar Das
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Correspondence to Nikhil Saikia.

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Saikia, N., Das, N.K. Load Frequency Control of a Two Area Multi-source Power System with Electric Vehicle. J Control Autom Electr Syst 34, 394–406 (2023). https://doi.org/10.1007/s40313-022-00974-3

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