2 datasets found
  1. Fault Detection Dataset in Photovoltaic Farms

    • kaggle.com
    zip
    Updated Apr 17, 2022
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    Amr Ezz El-Din Rashed (2022). Fault Detection Dataset in Photovoltaic Farms [Dataset]. https://www.kaggle.com/datasets/amrezzeldinrashed/fault-detection-dataset-in-photovoltaic-farms/discussion
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    zip(52069 bytes)Available download formats
    Dataset updated
    Apr 17, 2022
    Authors
    Amr Ezz El-Din Rashed
    Description

    A simulated 250-kW PV power plant was utilized to create training and testing datasets of PV fault cases. The PV farm and its simulation are further discussed in Appendix A. Three fault types and normal operation (free-of-fault state) are defined. The default sets, as shown in Appendix A, are as follows. From the figure presented in the Appendix section, the fault cases F1, F2, and F3 describe a string fault (tested on string 1), string-to-ground fault (tested on string 1), and string-to-string fault (tested between strings 1 and 2), respectively. Training and testing datasets were built. The training dataset included 600 instances, each with 30 features and one column for classes or categories. Tab. 1 shows that the dataset included 100 (16.67%) free-of-fault cases, 153 cases (25.5%) of string faults, 149 cases (24.83%) of string-to-ground faults, and 198 cases (33%) of string-to-string faults. The total simulation time was 0.4 s, and a fault was assumed to occur at 0.2 s. In the training dataset, all measurements were taken after the fault occurred in the period from 0.2 s to 0.4 s. The testing dataset contained 50 instances. Measurements were taken in the period from 0.1 s to 0.3 s, with transient time from 0.1 s to 0.2 s, and faults occurring from 0.2 s to 0.3 s. for more information about this dataset see the following link: https://www.techscience.com/iasc/v30n2/44023 Please Cite This Article S. S. M. Ghoneim, A. E. Rashed and N. I. Elkalashy, "Fault detection algorithms for achieving service continuity in photovoltaic farms," Intelligent Automation & Soft Computing, vol. 30, no.2, pp. 467–479, 2021. video tutorial (see project 1) https://youtu.be/wpgQY5f2hOo presentation (see project 1) https://www.slideshare.net/AmrRashed3/machine-learning-workshop-using-orange-datamining-framework Matlab codes: https://github.com/amrrashed/Fault-Detection-Dataset-in-Photovoltaic-Farms

  2. Data from: FRIM - Fruit Integrative Modelling

    • data.europa.eu
    • zenodo.org
    unknown
    Updated Jul 3, 2025
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    Zenodo (2025). FRIM - Fruit Integrative Modelling [Dataset]. https://data.europa.eu/data/datasets/oai-zenodo-org-154041?locale=da
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    unknown(89197)Available download formats
    Dataset updated
    Jul 3, 2025
    Dataset authored and provided by
    Zenodohttp://zenodo.org/
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    FRIM - Fruit Integrative Modelling Implementation of the FRIM Dataset for ODAM (Open Data for Access and Mining), a EDTMS (Experimental Data Table Management System). See https://www.slideshare.net/danieljacob771282/odam-open-data-access-and-minin for further information Simply unzip the ZIP file under the data repository of your local instance of the ODAM system. (C) INRA UMR 1332 BFP - Metabolism Team - Yves Gibon - 2014

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Amr Ezz El-Din Rashed (2022). Fault Detection Dataset in Photovoltaic Farms [Dataset]. https://www.kaggle.com/datasets/amrezzeldinrashed/fault-detection-dataset-in-photovoltaic-farms/discussion
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Fault Detection Dataset in Photovoltaic Farms

Fault Detection Dataset in Photovoltaic Farms

Explore at:
zip(52069 bytes)Available download formats
Dataset updated
Apr 17, 2022
Authors
Amr Ezz El-Din Rashed
Description

A simulated 250-kW PV power plant was utilized to create training and testing datasets of PV fault cases. The PV farm and its simulation are further discussed in Appendix A. Three fault types and normal operation (free-of-fault state) are defined. The default sets, as shown in Appendix A, are as follows. From the figure presented in the Appendix section, the fault cases F1, F2, and F3 describe a string fault (tested on string 1), string-to-ground fault (tested on string 1), and string-to-string fault (tested between strings 1 and 2), respectively. Training and testing datasets were built. The training dataset included 600 instances, each with 30 features and one column for classes or categories. Tab. 1 shows that the dataset included 100 (16.67%) free-of-fault cases, 153 cases (25.5%) of string faults, 149 cases (24.83%) of string-to-ground faults, and 198 cases (33%) of string-to-string faults. The total simulation time was 0.4 s, and a fault was assumed to occur at 0.2 s. In the training dataset, all measurements were taken after the fault occurred in the period from 0.2 s to 0.4 s. The testing dataset contained 50 instances. Measurements were taken in the period from 0.1 s to 0.3 s, with transient time from 0.1 s to 0.2 s, and faults occurring from 0.2 s to 0.3 s. for more information about this dataset see the following link: https://www.techscience.com/iasc/v30n2/44023 Please Cite This Article S. S. M. Ghoneim, A. E. Rashed and N. I. Elkalashy, "Fault detection algorithms for achieving service continuity in photovoltaic farms," Intelligent Automation & Soft Computing, vol. 30, no.2, pp. 467–479, 2021. video tutorial (see project 1) https://youtu.be/wpgQY5f2hOo presentation (see project 1) https://www.slideshare.net/AmrRashed3/machine-learning-workshop-using-orange-datamining-framework Matlab codes: https://github.com/amrrashed/Fault-Detection-Dataset-in-Photovoltaic-Farms

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