Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 324, P. 119311 - 119311
Published: Nov. 29, 2024
Language: Английский
Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 324, P. 119311 - 119311
Published: Nov. 29, 2024
Language: Английский
Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 316, P. 118845 - 118845
Published: July 27, 2024
Language: Английский
Citations
17Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02561 - e02561
Published: Jan. 1, 2025
Language: Английский
Citations
1Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135219 - 135219
Published: Feb. 1, 2025
Language: Английский
Citations
1Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 314, P. 118665 - 118665
Published: June 21, 2024
Language: Английский
Citations
6Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104268 - 104268
Published: Feb. 1, 2025
Language: Английский
Citations
0Energy Reports, Journal Year: 2024, Volume and Issue: 13, P. 345 - 352
Published: Dec. 13, 2024
Language: Английский
Citations
2Applied Energy, Journal Year: 2024, Volume and Issue: 373, P. 123805 - 123805
Published: July 10, 2024
Language: Английский
Citations
1Engineering Reports, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 9, 2024
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 is crucial optimal performance and long‐term durability. The early detection faults vital prevent further damage, improve maintenance strategies, ensure uninterrupted production. In this study, we propose a novel fault method based on Time Frequency Analysis (TFA) using Scaling Basis Chirplet Transform (SBCT). proposed method, array signal decomposed into set chirplets SBCT. represent localized time‐frequency components that can capture dynamic behavior signal. To evaluate effectiveness extensive simulations experiments are conducted real‐world data. SBCT with combination various machine learning algorithms detect array. Support Vector Machine, Decision Tree, Random Forest, ANN classifiers able 99%, 98.5%, 99.2%, 99.5% accuracies no shading condition 88%, 85%, 89%, 89.5% severe condition. achieves high accuracy robustness detecting types arrays, even presence noise uncertainties. TFA offers promising solution efficient arrays. It enables detection, facilitating timely minimizing losses. approach contribute enhancing overall performance, reliability, lifespan thereby advancing adoption renewable sources promoting development.
Language: Английский
Citations
1Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133533 - 133533
Published: Oct. 1, 2024
Language: Английский
Citations
1Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 1, 2024
Language: Английский
Citations
1