Review on deep learning applications in frequency analysis and control of modern power system

Y Zhang, X Shi, H Zhang, Y Cao, V Terzija - International Journal of …, 2022 - Elsevier
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …

Situation awareness in ai-based technologies and multimodal systems: Architectures, challenges and applications

J Chen, KP Seng, J Smith, LM Ang - IEEE Access, 2024 - ieeexplore.ieee.org
Situation Awareness (SA) is a process of sensing, understanding and predicting the
environment and is an important component in complex systems. The reception of …

[HTML][HTML] Analysis of renewable-friendly smart grid technologies for the distributed energy investment projects using a hybrid picture fuzzy rough decision-making …

H Dinçer, S Yüksel, A Mikhaylov, G Pinter, ZA Shaikh - Energy Reports, 2022 - Elsevier
Smart grid systems help increase RWJ projects (RWJ) so that environmentally friendly
energy production can be generated. However, efficient technologies should be …

A real-time hierarchical framework for fault detection, classification, and location in power systems using PMUs data and deep learning

MR Shadi, MT Ameli, S Azad - International Journal of Electrical Power & …, 2022 - Elsevier
Abstract Frequency Disturbance Events (FDEs) occur due to various events such as
Generator Trip (GT), Line Outage (LO), and Load Disconnection (LD), which affect the …

Optimal energy storage system-based virtual inertia placement: A frequency stability point of view

H Golpîra, A Atarodi, S Amini… - … on Power Systems, 2020 - ieeexplore.ieee.org
In this paper, the problem of optimal placement of virtual inertia is considered as a techno-
economic problem from a frequency stability point of view. First, a data driven-based …

[HTML][HTML] Intelligent fault detection and classification schemes for smart grids based on deep neural networks

AS Alhanaf, HH Balik, M Farsadi - Energies, 2023 - mdpi.com
Effective fault detection, classification, and localization are vital for smart grid self-healing
and fault mitigation. Deep learning has the capability to autonomously extract fault …

Fault detection and classification in ring power system with DG penetration using hybrid CNN-LSTM

AS Alhanaf, M Farsadi, HH Balik - IEEE Access, 2024 - ieeexplore.ieee.org
A modern electric power system integrated with advanced technologies such as sensors
and smart meters is referred to as a “smart grids”, aimed at enhancing electrical power …

A data-driven under frequency load shedding scheme in power systems

H Golpîra, H Bevrani, AR Messina… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This paper presents a measurement-based under-frequency load shedding scheme that
considers time delays, measurement uncertainties, and communication network faults …

数据驱动的有源配电网运行态势智能感知方法

于淼, 闫旻睿, 万克厅, 齐冬莲 - 电力建设, 2024 - epjournal.csee.org.cn
态势感知是保障有源配电网安全, 可靠, 经济运行的重要方法. 随着**年来电力数据采集技术与大
数据技术的发展, 数据驱动的有源配电网态势感知得到了广泛的研究与应用 …

Power system event identification based on deep neural network with information loading

J Shi, B Foggo, N Yu - IEEE Transactions on Power Systems, 2021 - ieeexplore.ieee.org
Online power system event identification and classification are crucial to enhancing the
reliability of transmission systems. In this paper, we develop a deep neural network (DNN) …