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Open AccessArticle

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

Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470003, Colombia
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Energies 2018, 11(12), 3407; https://doi.org/10.3390/en11123407
Received: 9 November 2018 / Revised: 27 November 2018 / Accepted: 27 November 2018 / Published: 5 December 2018
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. View Full-Text
Keywords: RTRL algorithm; MPPT controller; PV module; dynamic neural network controller RTRL algorithm; MPPT controller; PV module; dynamic neural network controller
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Viloria-Porto, J.; Robles-Algarín, C.; Restrepo-Leal, D. A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm. Energies 2018, 11, 3407.

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