Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform
ABSTRACT Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13016 |
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| author | S Ramana Kumar Joga Chidurala SaiPrakash Srikanth Velpula Alivarani Mohapatra Theophilus A. T. Kambo Jr. |
| author_facet | S Ramana Kumar Joga Chidurala SaiPrakash Srikanth Velpula Alivarani Mohapatra Theophilus A. T. Kambo Jr. |
| author_sort | S Ramana Kumar Joga |
| collection | DOAJ |
| description | ABSTRACT Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development. |
| format | Article |
| id | doaj-art-d6462cc2ceca48d6bb345bc9b889ecbd |
| institution | OA Journals |
| issn | 2577-8196 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-d6462cc2ceca48d6bb345bc9b889ecbd2025-08-20T01:56:45ZengWileyEngineering Reports2577-81962024-12-01612n/an/a10.1002/eng2.13016Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet TransformS Ramana Kumar Joga0Chidurala SaiPrakash1Srikanth Velpula2Alivarani Mohapatra3Theophilus A. T. Kambo Jr.4Department of EEE Dadi Instiuite of Engineering and Technology Anakapalle IndiaElectrical and Electronics Engineering SR University Warangal IndiaElectrical and Electronics Engineering SR University Warangal IndiaSchool of Electrical Engineering KIIT Deemed to be University Bhubaneswar IndiaRural and Renewable Energy Agency Monrovia LiberiaABSTRACT Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.https://doi.org/10.1002/eng2.13016Chirplet transformfault classificationfault detectionfault diagnosissignal processingsolar PV |
| spellingShingle | S Ramana Kumar Joga Chidurala SaiPrakash Srikanth Velpula Alivarani Mohapatra Theophilus A. T. Kambo Jr. Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform Engineering Reports Chirplet transform fault classification fault detection fault diagnosis signal processing solar PV |
| title | Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform |
| title_full | Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform |
| title_fullStr | Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform |
| title_full_unstemmed | Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform |
| title_short | Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform |
| title_sort | time frequency analysis based fault detection in pv array using scaling basis chirplet transform |
| topic | Chirplet transform fault classification fault detection fault diagnosis signal processing solar PV |
| url | https://doi.org/10.1002/eng2.13016 |
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