您当前的位置:
首页 >
文章列表页 >
Optimal Location and Sizing of Conglomerate DG-FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations
Original Research | 更新时间:2026-09-30
    • Optimal Location and Sizing of Conglomerate DG-FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations

    • Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations

    • 现代电力系统保护与控制(英文)   2022年7卷第1期 页码:1-25
    • DOI:10.1186/s41601-022-00230-5    

      中图分类号:
    • 纸质出版:2022-01-01

    移动端阅览

  • Anwar Shahzad Siddiqui, Prashant. Optimal Location and Sizing of Conglomerate DG-FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations[J]. 现代电力系统保护与控制(英文), 2022,7(1):1-25. DOI: 10.1186/s41601-022-00230-5.

    Anwar Shahzad Siddiqui, Prashant. Optimal Location and Sizing of Conglomerate DG- FACTS using an Artificial Neural Network and Heuristic Probability Distribution Methodology for Modern Power System Operations[J]. Protection and Control of Modern Power Systems, 2022, 7(1): 1-25. DOI: 10.1186/s41601-022-00230-5.

  •  
  •  
icon
试读结束,您可以激活您的VIP账号继续阅读。
去激活 >
icon
试读结束,您可以通过登录账户,到个人中心,购买VIP会员阅读全文。
已是VIP会员?
去登录 >

0

浏览量

1

下载量

0

CSCD

文章被引用时,请邮件提醒。
提交
工具集
下载
参考文献导出
分享
收藏
添加至我的专辑

相关文章

A Robust Estimation Method for the Coupling Matrix in Non-Contact Measurement of Switching Transient Voltages in 500 kV Transmission Lines
Research on Reactive Power Control Strategy for Small Extinction Angle Operation of Hybrid Commutation Converters
ANN based directional relaying scheme forprotection of Korba-Bhilai transmission lineof Chhattisgarh state
Localizing Detection of False Data Injection Attack in CPPS Based on CFSSA Improved Multi-Layer Extreme Learning Machine
Distribution Network Optimization Based on Topology Security-Constrained Integrated Reinforcement Learning

相关作者

Shen Chen
Yang Xu
Hengxin He
Siyuan Xie
Zitian Wang
Xupeng Mu
Zhaoyin Shi
Anqi Pang

相关机构

State Grid Corporation of China
State Grid Jiangsu Electric Power Co., Ltd. Research Institute
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science of Technology
Beijing Huairou Laboratory
The Department of Electrical Engineering, Tsinghua University
0