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Optimized FOPID controller for improving steady state and transient response of Microturbine Generation system

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  • Manoharan, S.
  • Gnanambal, K.

Abstract

The power system structure is complicated and the demand is increasing rapidly. In order to minimize the power losses and to reduce the power shortage, Distribution Generation (DG) is added in the power system. Microturbine Generation (MTG) is a new type of DG which has become popular source of electric power industries due to their fuel flexibility, reliability, power quality and its size. In this paper, the modelling and simulation analysis of the MTG system are carried out with Fractional Order Proportional Integral Derivative (FOPID) controller to improve the overall performance of MTG system. The performance of the MTG system is studied under various linear and non-linear load conditions. The Microturbine model and converter controller models are simulated using MATLAB Software. FOPID controller is used to maintain constant voltage for various load conditions by maintaining the torque of the Microturbine (MT) system. The power quality of MTG system is analyzed using Fast Fourier Transform (FFT) Analysis. To improve the power quality, LCL filter is added in this paper. The overall performance of MTG system is analyzed with and without FOPID controller. When the results are compared with conventional PI controller, the MTG system shows better performance in FOPID controller.

Suggested Citation

  • Manoharan, S. & Gnanambal, K., 2019. "Optimized FOPID controller for improving steady state and transient response of Microturbine Generation system," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s036054421931922x
    DOI: 10.1016/j.energy.2019.116227
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    References listed on IDEAS

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    1. Ismail, M.S. & Moghavvemi, M. & Mahlia, T.M.I., 2013. "Current utilization of microturbines as a part of a hybrid system in distributed generation technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 142-152.
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    3. Loganthurai, P. & Rajasekaran, V. & Gnanambal, K., 2016. "Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand," Energy, Elsevier, vol. 107(C), pages 419-430.
    4. Kalantar, M. & Mousavi G., S.M., 2010. "Dynamic behavior of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage," Applied Energy, Elsevier, vol. 87(10), pages 3051-3064, October.
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    Cited by:

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    2. Konečná, Eva & Teng, Sin Yong & Máša, Vítězslav, 2020. "New insights into the potential of the gas microturbine in microgrids and industrial applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).

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