Maximum Power Point Tracking of PV System Based on Machine Learning

Takruri, Maen; Farhat, Maissa; Barambones, Óscar; Ramos-Hernanz, José Antonio; Turkieh, Mohammed Jawdat; Badawi, Mohammed; AlZoubi, Hanin; Sakur, Maswood Abdus

doi:10.3390/en13030692

Cited by 22 publications

(17 citation statements)

References 31 publications

(38 reference statements)

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“…Variations in cloud cover, fog and heat affect the PV system's conversion efficiency. Dust and other particles floating in the air or covering the panel can drastically decrease the efficiency of the power conversion process as well [76].…”

Section: The Latest Research On Mppt In Pvmentioning

confidence: 99%

“…In some papers, other statistical errors have been used to compare the reached power with that one at MPP: the Mean Error (ME in Equation ( 2)) [96], the Mean Square Error (MSE in Equation ( 3)) [96], the Standard Deviation error (σ in Equation ( 4)) [96], the Root Mean Square Error (RMSE in Equation ( 5)) [76,97], means absolute error (MAE in Equation ( 6)) [97], the overall power tracking efficiency (η in Equation ( 7)) [98] and a quality indicator that provides information about the ability of the ANN to predict the MPP (QI1 in Equation ( 8)) [99]:…”

Section: [90]mentioning

confidence: 99%

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A State-of-Art-Review on Machine-Learning Based Methods for PV

et al. 2021

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In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with applications in several applicative fields effectively changing our daily life. In this scenario, machine learning (ML), a subset of AI techniques, provides machines with the ability to programmatically learn from data to model a system while adapting to new situations as they learn more by data they are ingesting (on-line training). During the last several years, many papers have been published concerning ML applications in the field of solar systems. This paper presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. The paper gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.

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Section: The Latest Research On Mppt In Pvmentioning

confidence: 99%

Section: [90]mentioning

confidence: 99%

A State-of-Art-Review on Machine-Learning Based Methods for PV

et al. 2021

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“…The most important parameters of the PV panels (or cells) are the current-voltage and powervoltage nonlinear characteristics (Figure 2a) [21,22]. On the basis of the curves, further parameters can be determined, such as the short-circuit current (I SC ) that occurs when the voltage across the solar cell is zero (V PV = 0) as well as the open-circuit voltage (V OC ) measured at open terminals of the cell (I PV = 0).…”

Section: Princeples Of Photovoltaic Panelsmentioning

confidence: 99%

Designing Antennas for RFID Sensors in Monitoring Parameters of Photovoltaic Panels

et al. 2020

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The importance of the radio-frequency identification (RFID) technology and photovoltaic (PV) systems has been growing systematically in the modern world full of intelligent products connected to the Internet. Monitoring parameters of green energy plants is a crucial issue for efficient conversion of solar radiation, and cheap RFID transponders/sensors can be involved in this process to provide better performance of module supervision in scattered installations. Since many components of PV panels disturb the radio-wave propagation, research in the antenna scope has to be carried out to reach the proposed fusion. The problem with RFID transponders being detuned in close proximity to glass or metal surfaces can be solved on the basis of solutions known from the scientific literature. The authors went further, revealing a new antenna construction that can be fabricated straight on a cover glass of the PV panels. To achieve the established task, they incorporated advantages from the latest advancements in materials technology and low-power electronics and from the progress in understanding radio-wave propagation phenomena. The numerical model of the antenna was elaborated in the Hyper Lynx 3D EM software environment, and test samples were fabricated on the technology line of ML System Company. The convergence of calculated and measured antenna parameters confirms the design correctness. Thus, the studied antenna can be used to elaborate the cheap semipassive RFID transponders/sensors in the PV panel production lines.

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“…Artificial neural networks (ANN) are considered as one of the most commonly used methods for estimating the electrical efficiencies and predicting the power generation of PV modules [18]. The majority of these studies, however, merely investigated PV power outputs [19], optimum tracking behaviors [20], or fault detection [7,21].…”

Section: Introductionmentioning

confidence: 99%

Investigation of a Real-Time Dynamic Model for a PV Cooling System

et al. 2022

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The cooling of PV models is an important process that enhances the generated electricity from these models, especially in hot areas. In this work, a new, active cooling algorithm is proposed based on active fan cooling and an artificial neural network, which is named the artificial dynamic neural network Fan cooling algorithm (DNNFC). The proposed system attaches five fans to the back of a PV model. Subsequently, only two fans work at any given time to circulate the air under the PV model in order to cool it down. Five different patterns of working fans have been experimented with in this work. To select the optimal pattern for any given time, a back propagation neural network model was trained. The algorithm is a dynamic algorithm since it re-trains the model with new recorded surface temperatures over time. In this way, the model automatically adapts to any weather and environmental conditions. The model was trained with an indoor dataset and tested with an outdoor dataset. An accuracy of more than 97% has been recorded, with a mean square error of approximately 0.02.

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Maximum Power Point Tracking of PV System Based on Machine Learning

Cited by 22 publications

References 31 publications

A State-of-Art-Review on Machine-Learning Based Methods for PV

A State-of-Art-Review on Machine-Learning Based Methods for PV

Designing Antennas for RFID Sensors in Monitoring Parameters of Photovoltaic Panels

Investigation of a Real-Time Dynamic Model for a PV Cooling System

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