Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO)
Critical line-to-line faults (LLFs) and line-to-ground faults (LGFs) in photovoltaic (PV) systems are the most difficult faults to detect not only by conventional protection devices, but also modern fault detection schemes. The difficulty occurs due to critical mismatch levels and/or high fault impe...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002121 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321728277217280 |
|---|---|
| author | Sherko Salehpour Aref Eskandari Amir Nedaei Mohammad Gholami Mohammadreza Aghaei |
| author_facet | Sherko Salehpour Aref Eskandari Amir Nedaei Mohammad Gholami Mohammadreza Aghaei |
| author_sort | Sherko Salehpour |
| collection | DOAJ |
| description | Critical line-to-line faults (LLFs) and line-to-ground faults (LGFs) in photovoltaic (PV) systems are the most difficult faults to detect not only by conventional protection devices, but also modern fault detection schemes. The difficulty occurs due to critical mismatch levels and/or high fault impedance values which result in LLFs and LGFs remain undetected thus damaging the PV components, affecting system stability, reliability, and efficiency, and even leading to catastrophic fire hazards. However, challenges persist even in recent studies, including the need for a massive training dataset, disregard of fault severity assessment, and insufficient model accuracy. To address these challenges, the present paper proposes a deep reinforcement learning (DRL)-based model to detect, classify, and assess the severity of all and specifically critical LLFs and LGFs in PV arrays using the proximal policy optimization (PPO) algorithm. Additionally, to carry out the dataset dimensionality reduction, thus simplifying the training process, a two-stage feature engineering process has been implemented, including a feature importance finding stage using the permutation technique and a feature selection stage. To implement the proposed model and verify its capability in real-life condition, a laboratory PV system has been carefully designed. The results of the real-world experiment shows that the proposed model is able to detect LLFs and LGFs, under various environmental (temperature and irradiance), and electrical (mismatch and impedance) conditions with outstanding 100% of accuracy in the test process, using only a small training dataset. |
| format | Article |
| id | doaj-art-84730f9e0e8a48f4a3b50663ebacdae3 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-84730f9e0e8a48f4a3b50663ebacdae32025-08-20T03:49:41ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-07-0116811066110.1016/j.ijepes.2025.110661Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO)Sherko Salehpour0Aref Eskandari1Amir Nedaei2Mohammad Gholami3Mohammadreza Aghaei4Department of Electrical Engineering, Amirkabir University of Technology (AUT), Tehran, IranDepartment of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran; Corresponding author at: Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Norway (M. Aghaei); Department of Electrical Engineering, Iran University of Science and Technology (IUST), Iran (A. Eskandari).Department of Electrical Engineering, Amirkabir University of Technology (AUT), Tehran, IranDepartment of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, IranDepartment of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Norway; Department of Sustainable Systems Engineering (INATECH), University of Freiburg, Freiburg, Germany; Corresponding author at: Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Norway (M. Aghaei); Department of Electrical Engineering, Iran University of Science and Technology (IUST), Iran (A. Eskandari).Critical line-to-line faults (LLFs) and line-to-ground faults (LGFs) in photovoltaic (PV) systems are the most difficult faults to detect not only by conventional protection devices, but also modern fault detection schemes. The difficulty occurs due to critical mismatch levels and/or high fault impedance values which result in LLFs and LGFs remain undetected thus damaging the PV components, affecting system stability, reliability, and efficiency, and even leading to catastrophic fire hazards. However, challenges persist even in recent studies, including the need for a massive training dataset, disregard of fault severity assessment, and insufficient model accuracy. To address these challenges, the present paper proposes a deep reinforcement learning (DRL)-based model to detect, classify, and assess the severity of all and specifically critical LLFs and LGFs in PV arrays using the proximal policy optimization (PPO) algorithm. Additionally, to carry out the dataset dimensionality reduction, thus simplifying the training process, a two-stage feature engineering process has been implemented, including a feature importance finding stage using the permutation technique and a feature selection stage. To implement the proposed model and verify its capability in real-life condition, a laboratory PV system has been carefully designed. The results of the real-world experiment shows that the proposed model is able to detect LLFs and LGFs, under various environmental (temperature and irradiance), and electrical (mismatch and impedance) conditions with outstanding 100% of accuracy in the test process, using only a small training dataset.http://www.sciencedirect.com/science/article/pii/S0142061525002121Photovoltaic (PV) systemFault detection and diagnosis (FDD)Deep reinforcement learning (DRL)Proximal policy optimization (PPO)Feature engineeringPermutation feature importance technique |
| spellingShingle | Sherko Salehpour Aref Eskandari Amir Nedaei Mohammad Gholami Mohammadreza Aghaei Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) International Journal of Electrical Power & Energy Systems Photovoltaic (PV) system Fault detection and diagnosis (FDD) Deep reinforcement learning (DRL) Proximal policy optimization (PPO) Feature engineering Permutation feature importance technique |
| title | Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) |
| title_full | Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) |
| title_fullStr | Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) |
| title_full_unstemmed | Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) |
| title_short | Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) |
| title_sort | accurate detection of critical llfs and lgfs in pv arrays based on deep reinforcement learning using proximal policy optimization ppo |
| topic | Photovoltaic (PV) system Fault detection and diagnosis (FDD) Deep reinforcement learning (DRL) Proximal policy optimization (PPO) Feature engineering Permutation feature importance technique |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002121 |
| work_keys_str_mv | AT sherkosalehpour accuratedetectionofcriticalllfsandlgfsinpvarraysbasedondeepreinforcementlearningusingproximalpolicyoptimizationppo AT arefeskandari accuratedetectionofcriticalllfsandlgfsinpvarraysbasedondeepreinforcementlearningusingproximalpolicyoptimizationppo AT amirnedaei accuratedetectionofcriticalllfsandlgfsinpvarraysbasedondeepreinforcementlearningusingproximalpolicyoptimizationppo AT mohammadgholami accuratedetectionofcriticalllfsandlgfsinpvarraysbasedondeepreinforcementlearningusingproximalpolicyoptimizationppo AT mohammadrezaaghaei accuratedetectionofcriticalllfsandlgfsinpvarraysbasedondeepreinforcementlearningusingproximalpolicyoptimizationppo |