Comparative performance analysis between static solar panels and single-axis tracking system on a hot climate region near to the equator

Vieira, Romênia Gurgel; Guerra, Fabiana K. O. M. V.; Vale, Marcelo Roberto Bastos Guerra; Araújo, Maria Madalena Teixeira de

doi:10.1016/j.rser.2016.06.089

Cited by 99 publications

(41 citation statements)

References 20 publications

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“…Regarding DoF, there are three main types of trackers [3]: fixed devices [4], single-axis trackers [5], and dual-axis trackers [6]. Different researchers have reported the potential system benefits of using a simple single-axis tracking solar system [7,8]. The data acquisition, control, and monitor of the mechanical movement of the photovoltaic module were implemented based on a programmable logic-controlling unit.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Closed-Loop Solar Tracking System Based on the Sun Position Algorithm

et al. 2019

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Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac’s (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency.

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Section: Introductionmentioning

confidence: 99%

A Low-Cost Closed-Loop Solar Tracking System Based on the Sun Position Algorithm

et al. 2019

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“…Solar trackers are one or two-axis devices that change their orientation throughout the day to track the sun's path and maximize energy capture. In one-axis trackers, the surface rotates on a horizontal or vertical axis [1,2]; while, in the two-axis trackers, the surface is always perpendicular to the sun rays [3][4][5]. The energy obtained depend on the type of tracker used.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

Robles-Algarín¹,

Hernández²,

Leal³

2018

Electronics

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This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV) module. A nonlinear autoregressive network with exogenous inputs (NARX) was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA), and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

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“…In order to improve competitiveness, increase demand, and obtain the highest performance, the elements of a PV system must be optimized. There are several techniques to increase the performance of PV systems, among which solar trackers [3][4][5][6][7], hybrid systems [8,9], and controllers for the maximum power point tracking (MPPT) stand out. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Viloria-Porto¹,

Robles-Algarín²,

Restrepo-Leal³

2018

Energies

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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.

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Comparative performance analysis between static solar panels and single-axis tracking system on a hot climate region near to the equator

Cited by 99 publications

References 20 publications

A Low-Cost Closed-Loop Solar Tracking System Based on the Sun Position Algorithm

A Low-Cost Closed-Loop Solar Tracking System Based on the Sun Position Algorithm

A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

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

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