Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network-Reference-Cited by-同舟云学术

Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network

Published:2018-05-28 Issue:9 Volume:46 Page:987-998
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Tong Zhang¹,Lanxiang Sun²³⁴,Jianchang Liu¹,Haibin Yu²³⁴,Xiaoming Zhou⁵,Lin Gao⁶,Yingwei Zhang¹

Affiliation:

1. Institute of Automation, College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning Province, P. R. China

2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

3. Key Laboratory of Networked Control System, CAS, Shenyang, China

4. University of Chinese Academy of Sciences, Beijing, China

5. Liaoning Electric Power Compony Limited of State Grid, Shenyang, China

6. Yingkou Electric Power Supply Company, State Grid Liaoning Electric Power Supply Company Ltd, Liaoning, China

Funder

National Natural Science Foundation of China

IAPI Fundamental Research Funds

National Key R&D Program

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Energy Engineering and Power Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2018.1460884

Reference36 articles.

1. Deterministic approach for active distribution networks planning with high penetration of wind and solar power

2. Fault location in active distribution networks using non-synchronized measurements

3. Fault Detection and Faulted Line Identification in Active Distribution Networks Using Synchrophasors-Based Real-Time State Estimation

4. Online fault detection and the economic analysis of grid-connected photovoltaic systems

5. Automatic Faulted Feeder Section Location and Isolation Method for Power Distribution Systems Considering the Change of Topology

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fault classification and location of a PMU-equipped active distribution network using deep convolution neural network (CNN);Electric Power Systems Research;2024-04

2. A novel fault location method for the active distribution network based on dynamic quantum genetic algorithm;Electrical Engineering;2024-02-08

3. A Novel Fault Location Method of Distribution Network Based on Dynamic Quantum Genetic Algorithm;Lecture Notes in Electrical Engineering;2024

4. Design of Active Fault Diagnosis and Repair System for Active Distribution Networks Based on Decision Tree Algorithm;Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering;2023-11-03

5. Data-Driven Fault Detection of Power Distribution Network Based on Temporal Convolutional Network and Long Short-Term Memory;2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD);2023-11-02