Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO)

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...

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Bibliographic Details
Main Authors: Sherko Salehpour, Aref Eskandari, Amir Nedaei, Mohammad Gholami, Mohammadreza Aghaei
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:International Journal of Electrical Power & Energy Systems
Subjects:
Photovoltaic (PV) system
Fault detection and diagnosis (FDD)
Deep reinforcement learning (DRL)
Proximal policy optimization (PPO)
Feature engineering
Permutation feature importance technique
Online Access:http://www.sciencedirect.com/science/article/pii/S0142061525002121
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