Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Hopwood, Michael; Stein, Joshua S.; Braid, Jennifer L.; Seigneur, Hubert

doi:10.3390/en15145085

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…However, after a rigorous filtering process adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines [6], 31 papers were retained for detailed examination. We found that many studies used I-V curves alongside ML for fault identification in PV arrays [7]. Techniques like Principal Component Analysis (PCA) [8] were commonly used for feature extraction, while complex PV systems adopted advanced methods, such as Recurrent Neural Networks (RNNs) [9] with satellite data and Convolutional Neural Networks (CNNs) [10], for analyzing voltage and current.…”

Section: Data From Satellitementioning

confidence: 99%

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Islam,

Rashel,

Ahmed

et al. 2023

Energies

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Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

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Section: Data From Satellitementioning

confidence: 99%

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Islam,

Rashel,

Ahmed

et al. 2023

Energies

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“…The package documentation for pvOps provides thorough examples exploring the various capabilities of each module. Additional details about the iv module capabilities are captured in (Hopwood et al, 2020;Hopwood, Stein, et al, 2022) while more information about the design and development of the text, timeseries, and text2time modules are captured in (Mendoza et al, 2021). Key package dependencies of pvOps include pandas (The pandas development team, 2020), sklearn (Pedregosa et al, 2011), nltk (Bird et al, 2009), and keras (Chollet & others, 2015) for analysis and matplotlib (Hunter, 2007), seaborn (Waskom, 2021), and plotly (Plotly Technologies Inc., 2015) for visualization.…”

Section: Package Overviewmentioning

confidence: 99%

pvOps: a Python package for empirical analysis of photovoltaic field data

Bonney,

Gunda,

Hopwood

et al. 2023

JOSS

Self Cite

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The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

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“…By their definition, faults or anomalous behaviour occurs much less frequently than normal operating conditions. 1 Because of this, data re-balancing efforts are usually required. However with faulty conditions there is also the issue that to collect data about faulty conditions, they need to occur, and this is far from ideal as faulty conditions usually lead to machine damage, or faulty components being manufactured.…”

Section: Introductionmentioning

confidence: 99%

Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools

Sicard,

Butler,

et al. 2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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Machine tools (MT) are critical to modern manufacturing. They allow precision manufacturing of complex components at high volumes. MTs are large capital investments that require maintenance and monitoring to ensure they remain in good working condition. To best achieve reliability and high performance it is necessary to implement condition monitoring, fault detection and predictive maintenance. One solution for implementing these is by utilizing data-driven methods such as neural networks. One issue with any data-driven method is that they require large quantities of labeled data. This is especially difficult for fault detection applications as faults tend to be rare, and as a result, the datasets tend to be very imbalanced. One emerging technology that can be implemented to solve this issue is the digital twin (DT). DTs provide a solution for data collection, modeling, simulation, and smart services. One way that DTs can be used is to generate synthetic data which can be used for various data-driven methods. This data can be validated on a test bench to ensure its accuracy before implementation in production. Synthetic data generated from the DT model can be used to create a dataset for various condition monitoring DT services. This study involved the use of simulation software to generate synthetic data which was used to implement a fault detection algorithm for preload loss monitoring. This method has been demonstrated to be effective at identifying the current operating conditions of the system. This method shows promise to improve reliability and performance in MTs, and could be adapted to condition monitoring in other systems such as vehicles, buildings, and power generation.

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Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Cited by 9 publications

References 45 publications

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

pvOps: a Python package for empirical analysis of photovoltaic field data

Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools

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