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Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine

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Abstract

Microgrid is a low voltage electrical network with distributed generations, energy storage devices and controllable loads. This paper utilizes artificial neural network (ANN) to predict the optimum voltages in order to extract the maximum power and increment the efficiency of photovoltaic system. In this regard, the optimum voltages are achieved by the genetic algorithm (GA). Then these optimum values are used in ANN method. The results of ANN-GA is compared with the other methods that verified the proposed method with high accuracy which can track the maximum power point (MPP) under different insolation and temperature circumstances and also, meet the load demand with less fluctuation around the MPP.; also it can increase the convergence speed to achieve the MPP. As well as, the evaluation of fuzzy logic controller (FLC) in comparison with the PI controller in pitch angle of wind turbine (WT) is carried out. In order to control the output power of wind turbine, by implementing the wind speed and active power as inputs of FLC, it has faster responses, smoother power curves, less oscillation than aforementioned methods which lead to improve the dynamic responses of WT. The models are developed and applied in the Matlab/Simulink program.

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Authors and Affiliations

  1. Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran

    Alireza Rezvani, Maziar Izadbakhsh & Majid Gandomkar

Authors
  1. Alireza Rezvani
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  2. Maziar Izadbakhsh
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  3. Majid Gandomkar
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Correspondence to Alireza Rezvani.

Appendix: Description of the detailed model

Appendix: Description of the detailed model

Photovoltaic parameters: output power = 4.4 kW, Carrier frequency in V\(_\mathrm{MPPT}\) PWM generator: 4 kHz and in grid-Sid controller: 5.5 kHz, boost converter parameters: L = 3 mH, C = 600 \(\mu \)F, PI coefficients in grid-side controller: K\(_\mathrm{pVdc}\) = 2.5, K\(_\mathrm{iVdc}\) = 8, K\(_\mathrm{pId}\) = 9, K\(_\mathrm{iId}\) = 360, K\(_\mathrm{pIq}\) = 9, K\(_\mathrm{iIq}\) = 360.

PMSG parameters: output power: 76 kW, Stator resistance per phase = 2.8 \(\Omega \), inertia: 0.7 kg m\(^{2}\), torque constant 12 N-M/A, Pole pairs = 8, Nominal speed = 12 m/s, L\(_\mathrm{d}\) = L\(_\mathrm{a}\) = 6.3 mH. Grid parameters: X/R = 7, and other parameters, DC link capacitor = 4700 \(\mu \)F, dc link voltage = 950. PI coefficients in grid-side controller: K\(_\mathrm{pVdc}\) = 7.2, K\(_\mathrm{iVdc}\) = 360, K\(_\mathrm{pId}\) = 0.73, K\(_\mathrm{iId}\) = 6, K\(_\mathrm{pIq}\) = 0.73, K\(_\mathrm{iIq}\)6.

MTG parameters: MTG ratings: 25 kW, Rotor speed: 66,000 rpm, T\(_{1}\) = 0.4, T\(_{2}\) = 1, K = 25.

FESS parameters: output power = 25 kW, J = 0.07 kg m\(^{2}\), L = 8 mH.

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Rezvani, A., Izadbakhsh, M. & Gandomkar, M. Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine. Energy Syst 6, 551–584 (2015). https://doi.org/10.1007/s12667-015-0156-6

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