Home > View > Focus

Transmission line fault-cause identification method for large-scale new energy grid connection scenarios

2022-09-05

国网经济技术研究院梁涵卿等：适用于高比例新能源并网的输电网故障原因辨识方法

原创英文期刊编辑部全球能源互联网期刊 2022-09-05 08:00

摘要

对架空输电线路开展高精度的故障原因识别，有助于运维人员制定针对性的维护策略，进而缩短故障线路检查时间。在“碳达峰、碳中和”目标下，清洁能源快速发展，大规模新能源接入电网，电力系统运行特性将发生显着变化，会对传统的故障辨识方法造成一定影响。本文基于故障波形的时频特性、新能源特征参数以及深度学习模型，提出一种适用于高比例新能源并网的故障辨识方法。首先。选取与输电线路故障和新能源接入场景相关的10个参数作为模型特征参量；其次，构建了基于自适应深度信念网络（ADBN）的故障识别模型；最后，采用现场数据验证了模型的辨识效果。

Transmission line fault-cause identification method for large-scale new energy grid connection scenarios

适用于高比例新能源并网的输电网故障原因辨识方法

Hanqing Liang¹, Xiaonan Han¹, Haoyang Yu¹, Fan Li¹, Zhongjian Liu¹, Kexin Zhang¹

(1.State Power Economic and Technological Research Institute Co., Ltd., Beijing 102206, P. R. China )

收听作者1分钟语音介绍

Transmission Line Fault-cause Identification Method

Abstract

The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines. With the goal of achieving “carbon peak and carbon neutrality”, the schemes for clean energy generation have rapidly developed. Moreover, new energy-consuming equipment has been widely connected to the power grid, and the operating characteristics of the power system have significantly changed. Consequently, these have impacted traditional fault identification methods. Based on the time-frequency characteristics of the fault waveform, new energy-related parameters, and deep learning model, this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid. Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters. Further, a fault identification model based on adaptive deep belief networks was constructed, and its effect was verified by field data.

Keywords

Fault-cause identification, Transmission lines, Fault waveform, Large-scale new energy, Fault cause.

Fig.1 Structure of ADBN model

Fig.2 Typical waveforms for different fault types

Fig.3 Training process of fault identification model

Fig.4 Overall framework of proposed method

Fig.5 Relationships between the number of the network layers, the number of training cycles, and the identification accuracy rate

Fig.6 Relationships between the self-defined time tk, the wavelet decomposition level n, and the identification accuracy rate

Fig.7 Confusion matrix of recognition results

本文引文信息

Liang HQ, Han XN, Yu HY, et al. (2022) Transmission line fault-cause identification method suitable for large-scale new energy grid-connected scenarios. Global Energy Interconnection, 5(4): 362-374

梁涵卿，韩晓男，于昊洋等. (2022) 适用于高比例新能源并网的输电网故障原因辨识方法. 全球能源互联网（英文）, 5(4): 362-374

Biographies

Hanqing Liang

Hanqing Liang received Ph.D. degree at Shanghai Jiao Tong University in 2020. He is working in State Power Economic and Technological Research Institute Co., Ltd., Beijing, China. His research interests include fault diagnosis, renewable energy power generations, and power grid planning.

Xiaonan Han

Xiaonan Han received M.S. degree at North China Electric Power University in 2012. She is working in State Power Economic and Technological Research Institute Co., Ltd., Beijing, China. Her research interests include power grid planning and grid economics.

Haoyang Yu

Haoyang Yu received M.S. degree at London’s Global University in 2017. He is working in State Power Economic and Technological Research Institute Co., Ltd., Beijing, China. His research interests include renewable energy power generation and power grid planning.

Fan Li

Fan Li received Ph.D. degree at Tsinghua University in 2019. He is working in State Power Economic and Technological Research Institute Co., Ltd., Beijing, China. His research interests include power system reliability assessment, stability analysis and power grid planning.

Zhongjian Liu

Zhongjian Liu received Ph.D. degree at University of Bath in 2018. He is working in State Power Economic and Technological Research Institute Co., Ltd., Beijing, China. His research interests include power system stability analysis and power grid planning.

Kexin Zhang

Kexin Zhang received M.S. degree at New York University in 2019. She is working in State Power Economic and Technological Research Institute Co., Ltd., Beijing, China. Her research interests include grid economics and renewable energy power generation.

编辑：王彦博

审核：王伟

郑重声明

根据国家版权局相关规定，纸媒、网站、微博、微信公众号转载、摘编本网站作品，需包含本网站名称、二维码等关键信息，并在文首注明《全球能源互联网》原创。个人请按本网站原文转发、分享。

Transmission line fault-cause identification method for large-scale new energy grid connection scenarios

国网经济技术研究院 梁涵卿等：适用于高比例新能源并网的输电网故障原因辨识方法

郑重声明

国网经济技术研究院梁涵卿等：适用于高比例新能源并网的输电网故障原因辨识方法