Forecasting-aided State Estimation for Distribution Networks Based on Event-triggering Encryption

被引：0

作者：

Chen, Zhong ^{[1
]}

Pan, Jundi ^{[1
]}

Cai, Rong ^{[2
]}

Ni, Chunyi ^{[1
]}

Tian, Jiang ^{[2
]}

Wang, Yi ^{[3
]}

机构：

[1] School of Electrical and Engineering, Southeast University, Nanjing

[2] Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Suzhou

[3] NARI Group Corporation, State Grid Electric Power Research Institute, Nanjing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 23期

关键词：

distribution network; encryption transmission; event-triggering mechanism; maximum correntropy criterion; state estimation;

D O I：

10.7500/AEPS20240102010

中图分类号：

学科分类号：

摘要：

In the context of power cyber-physical systems, the communication links in distribution networks are more vulnerable to cyber-attacks compared to transmission networks. Among the existing state estimation methods considering cyber-attacks, the detection-correction method overly relies on attack detection, while the robust estimation method roughly regards cyber-attacks as quantitative outliers. The performance of state estimation under cyber-attacks needs further improvement. Therefore, a forecasting-aided state estimation method for distribution networks based on event-triggering encryption is proposed to enhance the active defense capability of distribution network state estimation against cyber-attacks and ensure the performance of state estimation. Firstly, an event-triggering encryption transmission framework is constructed to enhance the active defense capability of distribution network state estimation against cyber-attacks. Secondly, addressing the uncertainty in the measurement error distribution introduced by the event-triggering encryption transmission framework, an enhanced Cauchy-kernel-based maximum correntropy cubature Kalman filter algorithm is designed to utilize the state forecast values to assist robust filtering for accurate state estimation under unknown measurement noise distribution. Finally, simulation analysis is conducted in modified IEEE 33-bus and IEEE 118-bus distribution network systems to validate the effectiveness of the proposed algorithm. © 2024 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：145 / 155

页数：10

共 30 条

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