Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

Guejia-Burbano, Rudy Alexis; Petrone, G.; Manganiello, Patrizio

doi:10.3390/app11198943

Cited by 7 publications

(3 citation statements)

References 33 publications

(50 reference statements)

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“…As the modules age, their energy conversion capacity weakens, leading to reduced output power. Aging faults may also result in increased internal resistance, higher leakage currents, and changes in transient and temperature characteristics [15].…”

Section: Aging Faultsmentioning

confidence: 99%

Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models

Lin,

Chang,

Huang

et al. 2024

Applied Sciences

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Currently, fault identification in most photovoltaic systems primarily relies on experienced engineers conducting on-site tests or interpreting data. However, due to limited human resources, it is challenging to meet the vast demands of the solar photovoltaic market. Therefore, we propose to identify fault types through the current–voltage curves of solar arrays, obtaining curves for various conditions (normal, aging faults, shading faults, degradation faults due to potential differences, short-circuit faults, hot-spot faults, and crack faults) as training data for the model. We employ a multi-input model architecture that combines convolutional neural networks with deep neural networks, allowing both the imagery and feature values of the current–voltage curves to be used as input data for fault identification. This study demonstrates that by inputting the current–voltage curves, irradiance, and module specifications of solar string arrays into the trained model, faults can be identified quickly using actual field data.

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Section: Aging Faultsmentioning

confidence: 99%

Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models

Lin,

Chang,

Huang

et al. 2024

Applied Sciences

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“…An automatic detection system for deteriorated PV modules that uses a drone with a thermal camera is proposed in [36]. In [37], one ANN stage is used to predict the single-diode-model parameters under the hypothesis of healthy operation and another one is used for the degraded condition. The variation in each parameter, of the outputs of the two ANN stages, will give a direct identification of the type of degradation in the PV panel.…”

Section: Introductionmentioning

confidence: 99%

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network

Lo,

Chung,

Hsung

et al. 2024

Sensors

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Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I–V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I–V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles’ population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I–V dataset and an I–V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.

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“…Comparison between the two stages is used for the identification of the degradation type. The method was tested using experimental data of I-V curves from the NREL database [21]. Hopwood et al propose an approach that utilizes physics-based simulations of string-level IV curves.…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

Ρουμπακιάς

Stamatelos

2022

Applied Sciences

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The operation and maintenance of a photovoltaic system is a challenging task that requires scientific soundness, and has significant economic impact. Faults in photovoltaic systems are a common phenomenon that demands fast diagnosis and repair. The effective and accurate diagnosis and categorization of faults is based on information received from the photovoltaic plant monitoring and energy management system. This paper presents the application of machine learning techniques in the processing of monitoring datasets of grid connected systems in order to diagnose faults. In particular, monitoring data from four photovoltaic parks located in Central Greece are analyzed. The existing data are divided for training and validation procedures. Different scenarios are examined first, in order to observe and quantify the behavior of artificial neural networks in already known faults. In this process, the faults are divided in three main categories. The system’s performance deviation against the prediction of the trained artificial neural network in each fault category is processed by health monitoring methodology in order to specify it quantitatively.

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Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

Cited by 7 publications

References 33 publications

Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models

Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network

Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

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