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Intelligent Load Frequency Control in Presence of Wind Power Generation

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Modeling, Identification and Control Methods in Renewable Energy Systems

Part of the book series: Green Energy and Technology ((GREEN))

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Abstract

With the advent of large-scale interconnected power systems, many new problems have emerged, which include frequency fluctuations problem. In many parts of the world, installed capacity and energy production levels for electric generation from non-conventional renewable resources such as wind power generation are growing rapidly. However, the fluctuations of these generators affect the system frequency. The purpose of this work is to design an intelligent load frequency control (LFC) strategy based on Fuzzy Logic-PID controller to suppress all the fluctuations of the total power output of the wind generation and maintain the constancy of the system frequency. To show the effectiveness of the proposed control strategy, a two-area multi-sources power system was investigated for the simulation. The observed simulation results of the proposed Fuzzy Logic-PID controller are compared with the results obtained by the classical Ziegler-Nichols method and the meta-heuristic Particle Swarm Optimization (PSO) technique. The transient responses showing the integration impact of the wind farm are depicted and the results are tabulated as a comparative performance in view of peak overshoot and settling time. The results are compared and the ability of the proposed approach to evaluate load frequency control over large wind farm integration is confirmed.

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References

  • Arita, M., Yokoyama, A., & Tada, Y. (2006). Evaluation of battery system for frequency control in interconnected power system with a large penetration of wind power generation. In IEEE International Conference on Power System Technology, Chongqing (pp. 1–7).

    Google Scholar 

  • Attya, A., & Hartkopf, T. (2012). Penetration impact of wind farms equipped with frequency variations ride through algorithm on power system frequency response. Electrical Power and Energy Systems, 40, 94–103.

    Article  Google Scholar 

  • Bevrani, H., & Daneshmand, P. (2012). Fuzzy logic-based load-frequency control concerning high penetration of wind turbines. IEEE Systems Journal, 6, 173–180.

    Article  Google Scholar 

  • Bihui, L., Hong, S., Yong, T., Hongyun, Z., Feng, S., & DongFu, L. (2011). Study on the frequency control method and AGC model of wind power integration based on the full dynamic process simulation program. In International Conference on Advanced Power System Automation and Protection, Beijing (pp. 246–251).

    Google Scholar 

  • Chung, I.-Y., Liu, W., Cartes, D., & Moon, S.-I. (2011). Control parameter optimization for multiple distributed generators in a microgrid using particle swarm optimization. European Transactions on Electrical Power, 21, 1200–1216.

    Article  Google Scholar 

  • Demirören, A., Kent, S., & Günel, T. (2002). A genetic approach to the optimization of automatic generation control parameters for power systems. European Transactions on Electrical Power, 12, 275–281.

    Article  Google Scholar 

  • Eduardo, V.-N., Andreas, S., Oriol, G.-B., Adriã, J.-F., & Marcela, M.-R. (2011). Pitch control system design to improve frequency response capability of fixed-speed wind turbine systems. European Transactions on Electrical Power, 21, 1984–2006.

    Article  Google Scholar 

  • Ge, B., Wang, W., Bi, D., Rogers, C. B., Peng, F. Z., de Almeida, A. T., & Abu-Rub, H. (2013). Energy storage system-based power control for grid-connected wind power farm. Electrical Power and Energy Systems, 44, 115–122.

    Article  Google Scholar 

  • Hang, J., Zhang, J., & Cheng, M. (2016). Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine. Fuzzy Sets and Systems, 297, 128–140.

    Article  MathSciNet  Google Scholar 

  • Hooshmand, R., Ataei, M., & Zargari, A. (2012). A new fuzzy sliding mode controller for load frequency control of large hydropower plant using particle swarm optimization algorithm and kalman estimator. European Transaction on Electrical Power, 22, 812–830.

    Article  Google Scholar 

  • Jafarian, M., & Ranjbar, A. (2013). The impact of wind farms with doubly fed induction generators on power system electromechanical oscillations. Renewable Energy, 50, 780–785.

    Article  Google Scholar 

  • Kassem, A. M., Hasaneen, K. M., & Yousef, A. M. (2013). Dynamic modeling and robust power control of DFIG driven by wind turbine at infinite grid. Electrical Power and Energy Systems, 44, 375–382.

    Article  Google Scholar 

  • Kiaee, M., Cruden, A., Infield, D., & Chladek, P. (2013). Improvement of power system frequency stability using alkaline electrolysis plants’. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 227, 115–123.

    Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., Boussahoua, B., & Boudour, M. (2014a). Optimal load frequency control based on hybrid bacterial foraging and particle swarm optimization. In IEEE, 11th International Multi-conference on Systems, Signals and Devices (SSD-PES), Barcelona (pp. 1–6).

    Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., Boussahoua, B., & Boudour, M. (2014b). Optimal load frequency control in interconnected power system using PID controller based on particle swarm optimization. In IEEE, International Conference on Electrical Sciences and Technology in Maghreb (CISTEM), Tunis (pp. 1–8).

    Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., & Boudour, M. (2014c). Application of artificial neural networks to load frequency control in multi-area power system. In Proceedings of the 3rd International Conference on Information Processing and Electrical Engineering (ICIPEE), Tebessa (pp. 357–362).

    Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., & Boudour, M. (2016a). Design of intelligent load frequency control strategy using optimal fuzzy-PID controller’. International Journal of Process Systems Engineering, 4, 41–64.

    Article  Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., & Boudour, M. (2016b). Frequency stability enhancement in two-area deregulated power system based competitive electricity markets with redox flow batteries and power flow controllers. In IEEE 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers (pp. 1029–1036).

    Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., & Boudour, M. (2016c). LFC enhancement concerning large wind power integration using new optimised PID controller and RFBS. IET Generation, Transmission and Distribution, 10, 4065–4077.

    Article  Google Scholar 

  • Kouba, N. E. Y., Menaa, M., Hasni, M., & Boudour, M. (2016d). A novel optimal frequency control strategy for an isolated wind-diesel hybrid system with energy storage devices. SAGE, Wind Engineering, 40, 497–517.

    Article  Google Scholar 

  • Liu, P., Yang, W.-T., Yang, C.-E., & Hsu, C.-L. (2015). Sensorless wind energy conversion system maximum power point tracking using Takagi-Sugeno fuzzy cerebellar model articulation control. Applied Soft Computing, 29, 450–460.

    Article  Google Scholar 

  • Mahabuba, A., & Khan, M. (2009). Small signal stability enhancement of a multi-machine power system using robust and adaptive fuzzy neural network-based power system stabilizer. European Transactions on Electrical Power, 19, 978–1001.

    Article  Google Scholar 

  • Mandal, P., Zareipour, H., & Rosehart, W. D. (2014). Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNS. International Journal of Energy Research, 28, 1654–1666.

    Article  Google Scholar 

  • Michigami, T., & Oishi, T. (2001). Construction of dynamic fluctuation load model and simulation with AFC control of BTB interconnection. Electrical Engineering in Japan, 136, 15–25.

    Article  Google Scholar 

  • Nanda, J., Mishra, S., & Saikia, L. (2009). Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control. IEEE Transactions on Power Systems, 24, 602–609.

    Article  Google Scholar 

  • Pan, I., & Das, S. (2015). Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Applied Soft Computing, 29, 328–344.

    Article  Google Scholar 

  • Panda, S., & Yegireddy, N. (2013). Automatic generation control of multi-area power system using multi-objective non-dominated sorting genetic algorithm-II. Electrical Power and Energy Systems, 53, 54–63.

    Article  Google Scholar 

  • Pandey, S., Mohanty, S., & Kishor, N. (2013). A literature survey on load frequency control for conventional and distribution generation power systems. Renewable and Sustainable Energy Reviews, 25, 318–334.

    Article  Google Scholar 

  • Patnaik, R., & Dash, P. (2015). Impact of wind farms on disturbance detection and classification in distributed generation using modified adaline network and an adaptive Neuro-fuzzy information system’. Applied Soft Computing, 30, 549–566.

    Article  Google Scholar 

  • Pothiya, S., & Ngamroo, I. (2008). Optimal fuzzy logic-based PID controller for load-frequency control including superconducting magnetic energy storage units. Energy Conversion and Management, 49, 2833–2838.

    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 

  • Rahmani, M., & Sadati, N. (2013). Two-level optimal load-frequency control for multi-area power systems. Electrical Power and Energy Systems, 53, 540–547.

    Article  Google Scholar 

  • Ramakrishna, K. S. S., & Bhatti, T. S. (2008). Automatic generation control of single area power system with multi-source power generation’. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222, 1–11.

    Google Scholar 

  • RamaSudha, K., Vakula, V., & Shanthi, R. V. (2010). PSO based design of robust controller for two area load frequency control with nonlinearities. International Journal of Engineering Science and Technology, 2, 1311–1324.

    Google Scholar 

  • Sahu, R. K., Panda, S., & Padhan, S. (2013). A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system’. Applied Soft Computing, 13, 4718–4730.

    Article  Google Scholar 

  • Saikia, L., & Sahu, S. (2013). Automatic generation control of a combined cycle gas turbine plant with classical controllers using firefly algorithm. Electrical Power and Energy Systems, 53, 27–33.

    Article  Google Scholar 

  • Saikia, L., Mishra, S., Sinha, N., & Nanda, J. (2011). Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller. Electrical Power and Energy Systems, 33, 1101–1108.

    Article  Google Scholar 

  • Sarrias-Mena, W., Fernãndez-Ramírez, L., García-Vázquez, C., & Jurado, F. (2015). Dynamic evaluation of two configurations for a hybrid DFIG-based wind turbine integrating battery energy storage system. Wind Energy, 18, 1561–1577.

    Article  Google Scholar 

  • Schlechtingen, M., Santos, I. F., & Achiche, S. (2013). Wind turbine condition monitoring based on scada data using normal behavior models. Part 1: System description. Applied Soft Computing, 13, 259–270.

    Article  Google Scholar 

  • Shayeghi, H., Jalili, A., & Shayanfar, H. (2008). Multi-stage fuzzy load frequency control using PSO. Energy Conversion and Management, 49, 2570–2580.

    Article  Google Scholar 

  • Singh, V., Mohanty, S., Kishor, N., & Ray, P. (2013). Robust H-infinity load frequency control in hybrid distributed generation system. Electrical Power and Energy Systems, 46, 294–305.

    Article  Google Scholar 

  • Tofighi, M., Alizadeh, M., Ganjefar, S., & Alizadeh, M. (2015). Direct adaptive power system stabilizer design using fuzzy wavelet neural network with self-recurrent consequent part. Applied Soft Computing, 28, 514–526.

    Article  Google Scholar 

  • Vrakopoulou, M. (2013). Optimal decision making for secure and economic operation of power systems under uncertainty. Ph.D., thesis, Eidgenössische Technische Hochschule ETH, Zürich.

    Google Scholar 

  • Wang, C., & McCalley, J. (2013). Impact of wind power on control performance standards. Electrical Power and Energy Systems, 47, 225–234.

    Article  Google Scholar 

  • Zhang, W., Wang, J., Wang, J., Zhao, Z., & Tian, M. (2013). Short-term wind speed forecasting based on a hybrid model. Applied Soft Computing, 13, 3225–3233.

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Electrical and Industrial Systems, Faculty of Electrical Engineering and Computing, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algiers, Algeria

    Nour EL Yakine Kouba & Mohamed Boudour

Authors
  1. Nour EL Yakine Kouba
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  2. Mohamed Boudour
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Correspondence to Nour EL Yakine Kouba .

Editor information

Editors and Affiliations

  1. Sfax Engineering School, University of Sfax, Sfax, Tunisia

    Nabil Derbel

  2. University of the West of England, Bristol, UK

    Quanmin Zhu

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Kouba, N.E.Y., Boudour, M. (2019). Intelligent Load Frequency Control in Presence of Wind Power Generation. In: Derbel, N., Zhu, Q. (eds) Modeling, Identification and Control Methods in Renewable Energy Systems. Green Energy and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-1945-7_14

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  • DOI: https://doi.org/10.1007/978-981-13-1945-7_14

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  • Online ISBN: 978-981-13-1945-7

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