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A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm

Author

Listed:
  • Julie Viloria-Porto

    (Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470003, Colombia)

  • Carlos Robles-Algarín

    (Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470003, Colombia)

  • Diego Restrepo-Leal

    (Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470003, Colombia)

Abstract

The Real-Time Recurrent Learning Gradient (RTRL) algorithm is characterized by being an online learning method for training dynamic recurrent neural networks, which makes it ideal for working with non-linear control systems. For this reason, this paper presents the design of a novel Maximum Power Point Tracking (MPPT) controller with an artificial neural network type Adaptive Linear Neuron (ADALINE), with Finite Impulse Response (FIR) architecture, trained with the RTRL algorithm. With this same network architecture, the Least Mean Square (LMS) algorithm was developed to evaluate the results obtained with the RTRL controller and then make comparisons with the Perturb and Observe (P&O) algorithm. This control method receives as input signals the current and voltage of a photovoltaic module under sudden changes in operating conditions. Additionally, the efficiency of the controllers was appraised with a fuzzy controller and a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) controller, which were developed in previous investigations. It was concluded that the RTRL controller with adaptive training has better results, a faster response, and fewer bifurcations due to sudden changes in the input signals, being the ideal control method for systems that require a real-time response.

Suggested Citation

  • Julie Viloria-Porto & Carlos Robles-Algarín & Diego Restrepo-Leal, 2018. "A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm," Energies, MDPI, vol. 11(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3407-:d:188043
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    References listed on IDEAS

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    1. Amir, A. & Amir, A. & Selvaraj, J. & Rahim, N.A., 2016. "Study of the MPP tracking algorithms: Focusing the numerical method techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 350-371.
    2. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    3. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    4. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    5. Jordehi, A. Rezaee, 2016. "Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138.
    6. Vieira, R.G. & Guerra, F.K.O.M.V. & Vale, M.R.B.G. & Araújo, M.M., 2016. "Comparative performance analysis between static solar panels and single-axis tracking system on a hot climate region near to the equator," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 672-681.
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    1. Marwen Bjaoui & Brahim Khiari & Ridha Benadli & Mouad Memni & Anis Sellami, 2019. "Practical Implementation of the Backstepping Sliding Mode Controller MPPT for a PV-Storage Application," Energies, MDPI, vol. 12(18), pages 1-22, September.
    2. Nalini Karchi & Deepak Kulkarni & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Sujata N. Patil & Veena Desai, 2022. "Adaptive Least Mean Square Controller for Power Quality Enhancement in Solar Photovoltaic System," Energies, MDPI, vol. 15(23), pages 1-19, November.

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