Power system anomaly detection and classification based on state estimation and deep learning

Abstract

Abstract: Aiming at a series of difficult to detect network security events in power system caused by Web attacks and DDoS attacks, a smart power grid anomaly detection method that combines the weighted least squares method, extended Kalman filter, and a two-stage deep learning anomaly detection model is proposed. This method first adopts an adaptive composite sampling algorithm to address the issue of imbalanced distribution of network security data in the power system; Then, by integrating the weighted least squares method and the extended Kalman filter to utilize the dynamic nonlinear characteristics in the power system, the accuracy of intelligent power grid anomaly detection is improved through accurate state estimation, and safety risks in the power system are detected using the chi-square test and anomaly detection indicators; On this basis, the improved deep neural network model and multiple BiLSTM network models are combined to achieve the classification and recognition of network security risk events. Finally, the proposed method was numerical simulation verified on the CICIDS2017 dataset. The numerical simulation results show that this method can effectively detect various attacks, it has better anomaly detection capability and improve the accuracy of network security risk event classification by 3.85%.

Key words: smart grid, state estimation, anomaly detection, adaptive synthetic sampling

CLC Number:

O235

ZHENG Tie-jun, ZHANG Hong-jie, WANG Jing, WANG Yang. Power system anomaly detection and classification based on state estimation and deep learning[J]. Journal of Lanzhou University of Technology, 2025, 51(2): 143-151.

References

[1] 岳芳,王雪珍,姜山.智能电网的网络安全风险及应对策略 [J].科技导报,2024,42(9):6-16.
[2] BALUEV D,ALI M,GRYAZINA E.State of the art approach for comprehensive power system security assessment—real case study [J].International Journal of Electrical Power & Energy Systems,2024,155:109594.
[3] 王以良,周鹏,叶卫,等.基于金字塔网络的非侵入式负荷辨识及其隐私保护方案 [J].计算机工程,2024,50(5):182-189.
[4] 杜奇伟,徐红泉,郑翔,等.面向恶意网络攻击的电力监控系统防御策略设计 [J].控制工程,2024,31(1):185-192.
[5] 金志刚,刘凯,武晓栋.智能电网AMI领域IDS研究综述 [J].信息网络安全,2023,23(1):1-8.
[6] 李坤,孟建,罗广惠,等.面向智能电网的融合通信MEC卸载策略 [J].电力信息与通信技术,2024,22(6):10-17.
[7] AURANGZEB M,WANG Y,IQBAL S,et al.Enhancing cybersecurity in smart grids:deep black box adversarial attacks and quantum voting ensemble models for blockchain privacy-preserving storage [J].Energy Reports,2024,11:2493-2515.
[8] 王素红,唐煜星,郭文豪,等.考虑优先级的智能电网业务调度与资源分配方案 [J].南方电网技术,2024,18(4):59-70+79.
[9] GUTIERREZ S A,BOTERO J F,GOMEZ N G,et al.Next-generation power substation communication networks:IEC 61850 meets programmable networks [J].IEEE Power and Energy Magazine,2023,21(5):58-67.
[10] 石乐义,朱红强,刘袆豪,等.基于相关信息熵和 CNN-BiLSTM的工业控制系统人侵检测 [J].计算机研究与发展,2019,56(11):2330-2338.
[11] YANG Y,SONG R,ZHOU Z.Network intrusion detection method based on GAN-PSO-ELM [J].Computer Engineering and Applications,2020,56(12):66-72.
[12] 丁龙斌,伍仲冬,苏佳丽.基于集成深度森林的入侵检测方法 [J].计算机工程,2020,46(3):144-150.
[13] FU R,WANG J,WANG J,et al.Very fast transient overvoltage calculation and evaluation for 500 kV gas insulated substation power substation with double circuit and long gas insulated substation busbar[J].IET Generation,Transmission & Distribution,2023,17(1):252-262.
[14] TIAN Z,ZHAO N,HILLMANSEN S,et al.Traction power substation load analysis with various train operating styles and substation fault modes [J].Energies,2020,13(11):2788.
[15] ZHAO R,YIN J,XUE Z,et al.An efficient intrusion detection method based on dynamic autoencoder [J].lEEE Wireless Communication Letters,2021,10(8):1707-1711.
[16] HAGHIGHAT M H,LI J.Intrusion detection system using voting-based neural network [J].Tsinghua Science & Technology,2021,26(4):484-495.
[17] SAFALDIN M,OTAIR M,ABUALIGAH L.Improved binary gray wolf optimizer and SVMM for intrusion detection system in wireless sensor networks [J].Journal of Ambient Intelligence and Humanized Computing,2020,12(2):1559-1576.
[18] WANG Q,ZHAO W,REN J.Intrusion detection algorithm based on image enhanced convolutional neural network [J].Journal of Intelligent and Fuzzy Systems,2021,41(1):2183-2194.
[19] ZHANG H,HUANG L,WU C Q,et al.An effective convolutional neural network based on SMOTE and gaussian mixture model for intrusion detection in imbalanced dataset [J].Computer Networks,2020,177(18):107315.
[20] LEE J H,PARK K H.GAN-based imbalanced data intrusion detection system [J].Personal and Ubiquitous Computing,2021,25(1):121-128.
[21] JIANG Y,YAO A,MA H,et al.Digital-twin-based substation operation safety monitoring system [C]//2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).Okinawa,Japan:IEEE,2023:16-22.
[22] XU G,ZHANG P.Safe distance monitoring method of substation based on machine vision [C]//2021 International Conference on Neural Networks,Information and Communication Engineering.Bellingham,Washington:SPIE,2021:607-611.
[23] XIE J,LIU C C,SFORNA M,et al.On-line physical security monitoring of power substations [J].International Transactions on Electrical Energy Systems,2016,26(6):1148-1170.
[24] HE H,YANG B,GARCIA E A,et al.ADASYN:adaptive synthetic sampling approach for imbalanced learning [C]//Procedings of the 2008 International Joint Conference on Neural Networks(IJCNN).Hong Kong:IEEE,2008:1322-1328.
[25] MENG Q X,CATCHPOOLE D,SKILLICOM D,et al.Relational autoencoder for feature extraction[C]// Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN).Anchorage,AK,USA:IEEE,2017:364-371.
[26] SHARAFALDIN I,LASHKARI A H,GHORBANI A A.Toward generating a new intrusion detection dataset and intrusion traffic characterization [C]//Proceedings of the 4th International Conference on Information Systems Security and Privacy.Funchal,Madeira,Portugal:SCITEPRESS,2018:108-116.
[27] BINBUSAYYIS A,VAIYAPURI T.Identifying and benchmarking key features for cyber intrusion detection:an ensemble approach [J].IEEE Access,2019,7(10):106495-106513.
[28] VINAYAKUMAR R,ALAZAB M,SOMAN K P,et al.Deep learning approach for intelligent intrusion detection system[J].IEEE Access,2019,7(8):41525-41550.
[29] CHIBA Z,ABGHOUR N,MOUSSAID K,et al.Intelligent approach to build a deep neural Network based IDS for cloud environment using combination of machine learning algorithms [J].Computers & Security,2019,86(10):291-317.
[30] 郑灿伟,李世明,王禹贺,等.面向ICS的CGAN-DEEPFOREST入侵检测 [J].小型微型计算机系统,2023,44(4):868-874.