Review
Multidisciplinary Sciences
Shen Yuong Wong, Clifford Wei Chang Choe, Hui Hwang Goh, Yik Wen Low, Dennis Yang Shen Cheah, Chiia Pang
Summary: The paper provides an in-depth analysis of the latest artificial intelligence techniques for fault detection and classification on power transmission lines, discussing the differences in implementing intelligent methods in protection schemes and drones, and highlighting the lack of research on the application of intelligent methods in UAVs in this field.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Saizhao Yang, Wang Xiang, Jinyu Wen
Summary: The MMC based DC grids are effective for integrating bulk wind power. This study proposes an improved DC fault detection scheme using zone partition and traveling-wave polarities, ensuring the safe operation of multi-terminal DC wind power integration systems during fault isolation.
IEEE TRANSACTIONS ON POWER DELIVERY
(2022)
Article
Computer Science, Information Systems
Na An, Hongchun Shu, Bo Yang, Pulin Cao, Jian Song, Yu Guo
Summary: By analyzing the fault voltage characteristics of a modular multilevel converter high voltage direct current (MMC-HVDC) transmission system, it is found that there are distinct differences in the positive and negative voltage relationships of direct current transmission lines with single pole-to-ground faults compared to other faults. The Pearson correlation coefficient is used to quantify these differences. A novel fault identification scheme is proposed, which combines the sum of voltage variation characteristics to effectively identify fault poles.
Article
Energy & Fuels
Houjie Tong, Robert C. Qiu, Dongxia Zhang, Haosen Yang, Qi Ding, Xin Shi
Summary: The proposed method utilizes graph convolutional neural network for transient fault detection and classification, with strong feature extraction ability and rapid output of classification results, demonstrated to have strong generalization ability. It can efficiently detect and classify transient faults, with potential application in practical online transmission line protection.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Prateek Mundra, Anoop Arya, Suresh K. Gawre, Sandeep Biswal, Felipe Lopes, Om P. Malik
Summary: In the presence of nonlinear response created by power electronics-based compensators, reliable fault detection and classification by distance protection relays is a major concern. This study proposes a Taylor Series-based strategy using an adaptive thresholding technique to achieve accurate and efficient fault detection in the presence of the dynamic characteristics of static synchronous compensators (STATCOM). The proposed strategy has been analyzed and compared to other techniques, demonstrating its accuracy and feasibility.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Energy & Fuels
Patrick S. Pouabe Eboule, Jan Harm C. Pretorius, Nhlanhla Mbuli
Summary: This paper compares the results of two powerful machine learning techniques applied to predict fault classification and location on a nine-phase transmission line system, showing the potential of these artificial intelligence techniques in increasing line yield.
Article
Engineering, Electrical & Electronic
Sunil Kumar Maurya, Rakesh Kumar Panda, Abheejeet Mohapatra, Ankush Sharma
Summary: This paper proposes a communication-less fault detection and classification scheme in a Low-Voltage DC microgrid using local parameters. The scheme identifies forward and backward faults and categorizes Pole-to-Ground and Pole-to-Pole faults by analyzing the simultaneous threshold violations of different parameters. The proposed scheme can quickly detect and classify the worst possible faults within 0.72 ms.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Article
Engineering, Electrical & Electronic
Jing Ma, Ruifeng Wang, Chen Liu, Jiaming Zhang, A. G. Phadke
Summary: The paper proposes an adaptive DC-line fault recovery strategy based on voltage gradient, which analyzes the changes in the topology of the DC system and the characteristics of the inverter station's output voltage to construct criteria for identifying transient and permanent faults. This method successfully achieves adaptive fault recovery by accurately distinguishing transient and permanent faults on the DC line.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zhongxue Chang, Zhihao Zeng, Minna Dou, Zhihua Zhang, Guobing Song
Summary: This paper analyzes the voltage characteristics of single phase line-broken (SPLB) fault in different scenarios, and proposes a highly sensitive SPLB fault detection method based on differential voltage. It also presents SPLB fault location methods based on centralized intelligent mode and intelligent distributed mode. Simulations demonstrate the feasibility and effectiveness of the proposed methods.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2023)
Article
Engineering, Electrical & Electronic
Bhargav Y. Vyas, R. P. Maheshwari, Biswarup Das
Summary: This article presents a new and fast approach for detection and classification of faults on transmission lines, which has been verified to be accurate and fast under various system parameter variations.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Acoustics
Syrine Derbel, Florentina Nicolau, Nabih Feki, Jean-Pierre Barbot, Mohamed Slim Abbes, Mohamed Haddar
Summary: This article presents a diagnosis method for nonlinear dynamical systems, called sparse recovery diagnosis, which estimates a sparse fault vector from few system measurements. The method is applied to gear power transmission in industrial systems and can detect different sensor and mechanical faults.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Computer Science, Information Systems
Bhuvnesh Rathore, Om Prakash Mahela, Baseem Khan, Sanjeevikumar Padmanaban
Summary: The WAN technique developed for fault analysis of transmission networks utilizes wavelet-based approximate coefficients computed from current signals for fault detection and classification, while an Artificial Neural Network is employed for precise fault location estimation based on voltage and current signals. Case studies on varying fault scenarios demonstrate the robustness of the algorithm in handling different system parameters and noise effects.
Article
Engineering, Electrical & Electronic
Chao Dong, Ke Zhang, Zhiyuan Xie, Chaojun Shi
Summary: Overhead transmission line detection based on aerial images taken by UAVs has been extensively studied. However, it faces challenges such as inappropriate evaluation criteria and significant scaling of components in the images. To address these challenges, a relative mean Average Precision evaluation index is proposed to accurately measure the model's detection performance for smaller objects. A data enhancement strategy with multi-scale transformation is adopted to mitigate the scaling issue. The proposed method incorporates Swin-v2 and a balanced feature pyramid to enhance the feature characterization capabilities of the existing Cascade RCNN target detection technology, and utilizes side-aware boundary localization for improved positioning accuracy of the model. Experimental results demonstrate the superiority of the proposed method over state-of-the-art methods, achieving higher detection accuracy for different mAP metrics. The paper also discusses the impact of the adopted data enhancement on the model's robustness.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2023)
Article
Engineering, Electrical & Electronic
Maanvi Bhatnagar, Anamika Yadav, Aleena Swetapadma
Summary: The study aims to reduce relaying time for fault detection and classification, as well as accurate fault location estimation using the TKEO and XGBoost algorithm. The analysis was conducted on the IEEE 14 Bus transmission network, developing modules for fault detection, classification, and location estimation. The proposed methodology successfully determines, locates, and identifies faulty phases, with a focus on high impedance faults.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Subodh Kumar Mohanty, Aleena Swetapadma, Paresh Kumar Nayak, Om P. Malik
Summary: Fault detection in a TCSC-compensated transmission line is more difficult and complicated, especially during power swings. This paper presents a novel approach for fault detection during power swings using a decision tree-based classifier with only local end current measurements as input. The proposed method is validated on different test cases and shows superior sensitivity compared to existing methods, proving its effectiveness.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)