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.
Highlights
Neurocontroller the as Least Mean Square (LMS) conditions, algorithm good resultswas for both irradiance and constant temperature as and similar to and constant temperature as well as for variable conditions, and its performance was similar to that obtained with the obtained with the Real-Time Recurrent Learning Gradient (RTRL) Neurocontroller
Fuzzy controllers controllers for for variable variable signals signals of of irradiance irradiance and temperature. These results show that the RTRL controller has an excellent performance, since it minimizes. These results show that the RTRL controller has an excellent performance, since it minimizes power losses due to oscillations
From the development of a dynamic neural controller for the tracking of the maximum power point, it can be concluded that before designing the control system, the non-linear behavior of the PV module must be taken into account and that the output signals will be a function of the climatic conditions that are used as inputs of the module
Summary
Due to the negative impact caused by the use of conventional energy sources, environmental problems such as pollution, and poor quality in the provision of primary energy service, renewable energy is presented as an excellent alternative to reduce dependence on conventional electrical network.The notable increase in the use of renewable energies as an alternative means of generating and storing clean energy is considered worldwide as one of the technological solutions to mitigate climate change.Internationally it is recommended that in places with high solar density, the use of photovoltaic (PV) energy sources be chosen, since this type of energy provides an inexhaustible energy resource [1].In the global report presented in 2017 by the International Agency for Renewable Energy (IRENA), it is evident that solar energy is a leader in power generation capacity due to its competitive costs in many emerging markets of the world [2].In order to improve competitiveness, increase demand, and obtain the highest performance, the elements of a PV system must be optimized. Due to the negative impact caused by the use of conventional energy sources, environmental problems such as pollution, and poor quality in the provision of primary energy service, renewable energy is presented as an excellent alternative to reduce dependence on conventional electrical network. In the global report presented in 2017 by the International Agency for Renewable Energy (IRENA), it is evident that solar energy is a leader in power generation capacity due to its competitive costs in many emerging markets of the world [2]. With the MPPT controllers, better performance of the PV systems is obtained, since they take advantage of all the current generated by the solar panels independently of the voltage. These controllers are more expensive than Pulse-Width Modulation
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