Application of machine learning methods in fault detection and classification of power transmission lines: a survey

FM Shakiba, SM Azizi, M Zhou, A Abusorrah - Artificial Intelligence Review, 2023 - Springer
The rising development of power systems and smart grids calls for advanced fault diagnosis
techniques to prevent undesired interruptions and expenses. One of the most important part …

Real-time sensing and fault diagnosis for transmission lines

FM Shakiba, M Shojaee, SM Azizi, M Zhou - International Journal of …, 2022 - sciltp.com
Protection of high voltage transmission lines is one of the crucial problems in the power
system engineering. Accurate and timely detection and identification of transmission line …

A robust mean-field actor-critic reinforcement learning against adversarial perturbations on agent states

Z Zhou, G Liu, M Zhou - IEEE Transactions on Neural Networks …, 2023 - ieeexplore.ieee.org
Multiagent deep reinforcement learning (DRL) makes optimal decisions dependent on
system states observed by agents, but any uncertainty on the observations may mislead …

[HTML][HTML] Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions

A Serikbay, M Bagheri, A Zollanvari, BT Phung - Energies, 2024 - mdpi.com
Overhead transmission line insulators are non-conductive materials that separate
conductors from grounded transmission towers. Once in operation, they frequently …

Insulator Defect Recognition Based on Vision Big‐Model Transfer Learning and Stochastic Configuration Network

S Liu, Y Ma, Z Zheng, X Pang, B Li - IET Signal Processing, 2024 - Wiley Online Library
Insulator faults are an important factor in causing outages and accidents in power
transmission lines. In response to problems related to inefficient insulator positioning, limited …

Performing effective generative learning from a single image only

Q Xu, J Chen, J Tang, Q Kang… - 2023 32nd Wireless and …, 2023 - ieeexplore.ieee.org
Generative adversarial networks (GANs) can be well used for image generation. Yet their
training typically requires large amounts of data, which may not be available. This paper …

Insulator Condition Classification, Defect Detection, and Segmentation using Yolov8 Deep-Learning Model

S Panigrahy, R Sahoo… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
This study proposes a single-stage deep learning model capable of condition classification,
defect detection, and segmentation for high-voltage insulator inspection. Leveraging the …

An LSTM-based Anomaly Classification Framework for Power Electronics Dominated Grids

M Baker, MB Shadmand - 2023 IEEE Power and Energy …, 2023 - ieeexplore.ieee.org
The power electronics dominated grid (PEDG) is a paradigm shift which will see an increase
of distributed energy resources (DERs) for power generation. As the PEDG becomes …

An AI-Based Real-time Intrusion Detection System for Power Electronics-Dominated Grid: Attack on Inverters PQ Set-Points

A Zadehgol-Mohammadi, M Baker… - 2023 IEEE Energy …, 2023 - ieeexplore.ieee.org
This work presents a long short-term memory (LSTM) neural network based method for real-
time detection of intruded power setpoints of grid-following inverters (GFLIs) in power …

Surface Defect Detection of Outdoor Insulators in Low-Light Environments

S Panigrahy, S Karmakar - 2024 IEEE Region 10 Symposium …, 2024 - ieeexplore.ieee.org
Outdoor insulators are crucial for maintaining reliable power transmission and distribution.
However, inspecting these vital components under low-light conditions is essential to ensure …