Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data
Partial shading is one type of fault where photovoltaic panels cast shadows between each other, reducing their production and hastening their ageing. In this paper, we document and describe two distinct Machine Learning models that aim to identify and assess the impact of partial shading in a real...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidade do Porto
2025-03-01
|
| Series: | U.Porto Journal of Engineering |
| Subjects: | |
| Online Access: | https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2742 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850117197212418048 |
|---|---|
| author | Armando Luís Sousa Araujo Tiago Francisco Pires |
| author_facet | Armando Luís Sousa Araujo Tiago Francisco Pires |
| author_sort | Armando Luís Sousa Araujo |
| collection | DOAJ |
| description |
Partial shading is one type of fault where photovoltaic panels cast shadows between each other, reducing their production and hastening their ageing. In this paper, we document and describe two distinct Machine Learning models that aim to identify and assess the impact of partial shading in a real case study. These algorithms recognise similarities and patterns using expected and measured power data. The predicted power is calculated using the measured panel irradiance, current, and voltage using a photovoltaic panel electric circuit model. The first Machine Learning model employs K-means clustering to analyse the differences between expected and measured power, grouping data based on these deviations. The second Machine Learning model leverages the outputs of the K-means model as labels for a Long Short-Term Memory neural network, which classifies periods of partial shading. Experimental data from both models are presented, with the K-means model achieving a closer approximation to the reference values. However, the Long Short-Term Memory model demonstrated flexibility and scalability without requiring prior dataset knowledge from the end user.
|
| format | Article |
| id | doaj-art-d07c3d0f90314aaa9c5a08cf74860125 |
| institution | OA Journals |
| issn | 2183-6493 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Universidade do Porto |
| record_format | Article |
| series | U.Porto Journal of Engineering |
| spelling | doaj-art-d07c3d0f90314aaa9c5a08cf748601252025-08-20T02:36:08ZengUniversidade do PortoU.Porto Journal of Engineering2183-64932025-03-0111110.24840/2183-6493_0011-001_002742Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter DataArmando Luís Sousa Araujo0https://orcid.org/0000-0003-0317-7111Tiago Francisco Pires1https://orcid.org/0009-0001-8802-7552Universidade do Porto, Faculdade de EngenhariaUniversidade do Porto, Faculdade de Engenharia Partial shading is one type of fault where photovoltaic panels cast shadows between each other, reducing their production and hastening their ageing. In this paper, we document and describe two distinct Machine Learning models that aim to identify and assess the impact of partial shading in a real case study. These algorithms recognise similarities and patterns using expected and measured power data. The predicted power is calculated using the measured panel irradiance, current, and voltage using a photovoltaic panel electric circuit model. The first Machine Learning model employs K-means clustering to analyse the differences between expected and measured power, grouping data based on these deviations. The second Machine Learning model leverages the outputs of the K-means model as labels for a Long Short-Term Memory neural network, which classifies periods of partial shading. Experimental data from both models are presented, with the K-means model achieving a closer approximation to the reference values. However, the Long Short-Term Memory model demonstrated flexibility and scalability without requiring prior dataset knowledge from the end user. https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2742Partial ShadingSolar PanelsPhotovoltaic SystemsMachine LearningK-Means ClusteringLSTM Neural Networks |
| spellingShingle | Armando Luís Sousa Araujo Tiago Francisco Pires Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data U.Porto Journal of Engineering Partial Shading Solar Panels Photovoltaic Systems Machine Learning K-Means Clustering LSTM Neural Networks |
| title | Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data |
| title_full | Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data |
| title_fullStr | Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data |
| title_full_unstemmed | Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data |
| title_short | Machine Learning Algorithm for Assessing Photovoltaic Panels Partial Shading Losses based on Inverter Data |
| title_sort | machine learning algorithm for assessing photovoltaic panels partial shading losses based on inverter data |
| topic | Partial Shading Solar Panels Photovoltaic Systems Machine Learning K-Means Clustering LSTM Neural Networks |
| url | https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2742 |
| work_keys_str_mv | AT armandoluissousaaraujo machinelearningalgorithmforassessingphotovoltaicpanelspartialshadinglossesbasedoninverterdata AT tiagofranciscopires machinelearningalgorithmforassessingphotovoltaicpanelspartialshadinglossesbasedoninverterdata |