A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements

Chao Ren; Yan Xu; Junhua Zhao; Rui Zhang; Tong Wan

doi:10.17775/CSEEJPES.2020.05930

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements

Regular Papers | 更新时间：2026-10-15

- A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements
- A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第1期页码：76-85
- 作者机构：
  
  1. Interdisciplinary Graduate School, Nanyang Technological University,Singapore,Singapore
  2. School of Science and Engineering, Chinese University of Hong Kong,Shenzhen,China
  3. Changsha University of Science and Technology,Changsha,China
  4. School of Electrical and Information Engineering, University of Sydney,NSW,Sydney,Australia
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.05930
  中图分类号：
- 纸质出版：2022-01-01
- 稿件说明：
移动端阅览
Chao Ren, Yan Xu, Junhua Zhao, 等. A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(1):76-85.

Chao Ren, Yan Xu, Junhua Zhao, et al. A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements[J]. CSEE Journal of Power and Energy Systems, 2022, 8(1): 76-85.
Chao Ren, Yan Xu, Junhua Zhao, 等. A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(1):76-85. DOI： 10.17775/CSEEJPES.2020.05930.

Chao Ren, Yan Xu, Junhua Zhao, et al. A super-resolution perception-based incremental learning approach for power system voltage stability assessment with incomplete PMU measurements[J]. CSEE Journal of Power and Energy Systems, 2022, 8(1): 76-85. DOI： 10.17775/CSEEJPES.2020.05930.

摘要

Abstract

This paper develops a fully data-driven

missing- data tolerant method for post-fault short-term voltage stability (STVS) assessment of power systems against the incomplete PMU measurements. The super-resolution perception (SRP)

based on a deep residual learning convolutional neural network

is employed to cope with the missing PMU measurements. The incremental broad learning (BL) is used to rapidly update the model to maintain and enhance the online application performance. Being different from the state-of-the-art methods

the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario. Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Data-driven Transient Stability Assessment Using Sparse PMU Sampling and Online Self-check Function

Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach

FDIA Localization Detection Based on Intuitionistic Fuzzy Set-Multiple Hidden Layer Random Vector Functional Link Network

Data-Driven Convex Optimal Power Flow Approximation for Uncertainty-Aware Static Voltage Stability Margin Calculation

Imbalance Correction Based on the Ratio of Loss Function Values for Transient Stability Assessment