Modeling of a Photovoltaic Array with Maximum Power Point Tracking Using Neural Networks

Bouzidi, Mohammed; Harrouz, Abdelkader; Mansouri, Smail; Dumbrava, Virgil

doi:10.4028/p-ndl3bi

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The mathematical model for the single-diode equivalent circuit of a photovoltaic cell can be expressed by the following equation (Mohd Amiruddin et al, 2020b;Rahmoune et al, 2023), (Bouzidi et al, 2021a(Bouzidi et al, , 2022a:…”

Section: Modeling Of a Pv Panelmentioning

confidence: 99%

Intelligent fault detection of photovoltaic panel using neural networks

Bouzidi,

Rahmoune,

Nasri

2024

SEES

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This research primarily aims to leverage artificial neural network technology for diagnosing power output issues in photovoltaic (PV) panels stemming from fluctuations in solar irradiance and temperature. The proposed diagnostic approach relies on constructing a reference model that captures the expected normal operating behavior of the PV panel under fault-free conditions. This reference model is then compared against the actual power output, and the difference, known as the residual, is analyzed to detect potential faults. The neural network is trained using real-world data inputs like solar irradiance and temperature measurements, with the sole output being the power produced by the PV panel. Through training, the neural network learns to map the complex non-linear relationships between environmental inputs and expected power output, effectively modeling the PV system's intrinsic behavior under healthy conditions. Results demonstrate the neural network-based approach's remarkable ability to diagnose faults with high accuracy while avoiding potential non-linear complications. This intelligent monitoring system provides a reliable protocol for early fault detection through training on actual measurements, eliminating the need for complex mathematical models. Consequently, it streamlines the maintenance process by negating intricate procedures to identify PV panel issues. The neural network effectively learns the mapping between environmental conditions and expected power output through exposure to real-world data during training. By analyzing deviations from this learned mapping, represented by the residual signal, the approach can reliably detect anomalies indicative of faults or performance degradation in the PV system. This data-driven nature allows the system to adapt to site-specific characteristics and capture non-linear effects without explicit modeling.

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Section: Modeling Of a Pv Panelmentioning

confidence: 99%

Intelligent fault detection of photovoltaic panel using neural networks

Bouzidi,

Rahmoune,

Nasri

2024

SEES

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“…The cell temperature and the prevailing irradiance are often used as input parameters for the ANN. In addition, the voltage inverse can be also used as an input to improve the MPPT performance by black-boxing the ANN internally [70][71][72][73][74][75][76][77]. (4) Solar radiation prediction.…”

Section: Application Of Anns In Pv Systemsmentioning

confidence: 99%

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

Yuan

Xiong

2022

Energies

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Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.

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