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Localization of Dummy data Injection Attacks in power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

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arXiv 2024年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong Zang, Jiye Xu, Qi School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China School of Computer Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

关键词： Convolution

Localization of Dummy data Injection Attacks in power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

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SSRN

SSRN 2023年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China

The emergence of new dummy data attacks seriously endangers power systems' safe and stable operation. Due to the injected data meeting the spatial distance concealment, it is difficult to accurately locate the attack location by existing methods, so a dummy data injection attack (DDIA) localization method based on a spatio-temporal graph wavelet convolutional neural network is proposed in this paper. First, the principle of the new DDIA for power systems is analyzed. Based on the injected data satisfying the constraints of consistency, virtuality, destructiveness, and finiteness, the mathematical model of DDIA under consideration of incomplete topological information and AC state estimation is designed from the attacker's perspective to compensate for the limitations of the DC flow-based attack data generation method that can be easily captured. Then, combining the a priori knowledge of grid topology, taking into account the temporal correlation of the measurement data and the topology-dependent features of the power grid, the temporal and spatial attention matrices are introduced to capture the spatio-temporal correlation of the attack adaptively, and the spatiotemporal features of DDIA are extracted jointly based on gated stacked causal convolution and graph wavelet sparse convolution, which improves the dynamic mining ability and computational efficiency of the spatiotemporal correlation in the neighborhood. Finally, based on the spatio-temporal graph neural network, a DDIA localization method is proposed to adapt to the topological structure changes of the power system. The correctness of the DDIA model is proved through the analysis of arithmetic cases. It is verified that the proposed localization method can detect and localize DDIA quickly and effectively and has superior accuracy, robustness and generalization ability. © 2023, The Authors. All rights reserved.

关键词： Convolution

Extraction of Typical Operating Scenarios of New power System Based on Deep Time Series Aggregation

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arXiv 2024年

作者： Qu, Zhaoyang Zhang, Zhenming Qu, Nan Zhou, Yuguang Li, Yang Jiang, Tao Li, Min Long, Chao School of Electrical Engineering Northeast Electric Power University Jilin132012 China Jilin Province Technology Research Center of Power Big Data Intelligent Processing Jilin132012 China State Grid Jiangsu Electric Power Co. Ltd. Nanjing Power Supply Branch Nanjing210008 China State Grid Jilin Electric Power Co. Ltd. Changchun130022 China Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyze the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A gramian angular summation field (GASF) based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enables dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy. Copyright © 2024, The Authors. All rights reserved.

关键词： Image coding

An Efficient Multi-Order Cascade Distillation Model for the Detection of Small Targets and Occluded Objects in Transmission Line Inspection

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SSRN

SSRN 2023年

作者： Qu, Zhaoyang Zang, Jiye Dong, Yunchang Qu, Nan Song, Siqi Jiang, Tao Li, Min Liang, Feng Li, Lingcong School of Computer Northeast Electric Power University Jilin132012 China Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center Jilin132012 China State Grid Jilin Electric Power Research Institute Changchun130012 China State Grid Jiangsu Electric Power Co. Ltd. Nanjing Power Supply Company 210000 China Northeast Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group Changchun130021 China

The proposed cascade distillation detection model improves the accuracy of small target defect detection and robustness in complex environments during UAV inspection of transmission lines. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. A high-order aggregated feature extraction network is introduced to enhance feature extraction of small and obscured targets. Lastly, a spatially deformable attention mechanism optimizes sampling point selection and attention weight calculation by aggregating nearby contextual information, improving bounding box adjustment. © 2023, The Authors. All rights reserved.

关键词： electric lines

Demand Response Strategy Model Based on User Satisfaction 16th

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Demand Response Strategy Model Based on User Satisfaction

16th International Conference on intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Guo, Xiaoli Sun, Yuhan Feng, Li Qu, Chaoyang Sun, Tieli School of Computer Science of Northeast Electric Power University Jilin City132012 China College of Humanities and Sciences of Northeast Normal University Changchun City130117 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin City132012 China State Grid Xin Yuan Fengman Training Center Jilin City132018 China

ISBN: (纸本)9789813364196

The demand response model of household microgrid based on satisfaction of customers is proposed after the users’ habit of using electricity which is found from the historical electricity data and aims at the problems of the load diversity of the household microgrid and the improvement of user satisfaction in response process, combined with the demand response theory of the users and the thought of data mining. The demand response strategy model based on users’ satisfaction is designed with the research of demand response behavior integrated with user’s side experience. The model takes the user's demand responsiveness as the load constraint, takes user’s comprehensive satisfaction maximum as the demand response objective, and takes the bacterial colony chemotaxis hybrid algorithm to solve the model. Finally, the optimal load power consumption plan is obtained as the user’s demand response strategy. The experimental simulation results verify the energy-saving effect of the model and the effectiveness of the algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conservation

research on the Improved YOLOv5 Algorithm Fall Monitoring System for Home Care for the Elderly 7

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Research on the Improved YOLOv5 Algorithm Fall Monitoring Sy...

7th International Conference on Computer Information Science and Application technology, CISAT 2024

作者： Xu, Wanchao Zhang, Junchao Zhang, Xinyu Li, Fengxia Tao, Jinyi Zhao, Rongli Taiyuan University of Technology College of Electrical and Power Engineering Shanxi Taiyuan030024 China Shanxi Provincial Engineering Research Center of Electric Drive and Internet of Things Shanxi Taiyuan030024 China Nanjing University College of Software Jiangsu Nanjing210093 China National Intelligent Lighting and Smart Wearable Technology Innovation Center Shanxi Province Taiyuan030024 China Eng. Technol. Res. Ctr. of Smart City Lighting Data Sharing and Pub. Serv. Platform Based on Big Data Shanxi Province Taiyuan030024 China Research Institute of New Materials and Chemical Engineering Shanxi Zhejiang University Shanxi Taiyuan030032 China

ISBN: (纸本)9798350375107

In view of the risk of fall for the elderly in the home care environment, a surveillance video display fall detection method of the improved YOLOv5 algorithm is improved, and the Transformer module that increases the human attention mechanism is combined with the CSPDarknet structure of the backbone part of the YOLOv5 algorithm. The system collects video data through the camera, trains and verifies the video data, and then judges the action and posture of the target person. It connects the WIFI module of Orange School to send the action and posture information to the guardian's mobile phone to remind the guardian to timely treat the person who fell down, so as to improve the rescue efficiency. On the basis of improving the algorithm structure, the loss function was improved and conducted repeated training. The experimental results showed that the improved algorithm mAP improved by 2.8%, the F1 score by 2.2%, the number of processing frames per second by 10% during inference, and achieved the fall detection accuracy of 98.6%. © 2024 IEEE.

关键词： Fall detection

Active and passive hybrid detection method of power CPS false data injection attacks based on improved AKF and GRU-CNN

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arXiv 2022年

作者： Qu, Zhaoyang Bo, Xiaoyong Yu, Tong Liu, Yaowei Dong, Yunchang Kan, Zhongfeng Wang, Lei Li, Yang School of Electrical Engineering Northeast Electric Power University Jilin132012 China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin132101 China Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing Jilin132012 China Guangxi Power Grid Co. Ltd. Electric Power Research Institute Nanning530013 China State Grid Jilin Electric Power Co. Ltd. Changchun130012 China State Grid Jilin Electric Power Co. Ltd. Jilin Power Supply Company Jilin132011 China

With the deep penetration of the new generation of information technology in the power system, the power system has gradually evolved into a highly coupled cyber-physical systems (CPS), and false data injection attacks (FDIAs) is the most threatening attack among many power CPS network attacks. Aiming at the problem that the existing knowledge-driven detection process of FDIAs has been in a passive detection state for a long time and ignores the advantages of data-driven active capture of features, an active and passive hybrid detection method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, it analyzes the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed. The state estimation algorithm based on non-negative positive definite adaptive Kalman filter (NDAKF) is improved, and FDIAs passive detection method with similarity Euclidean distance detection and residual detection as the core is constructed. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature expression ability, an active detection method of FDIAs based on GRU-CNN hybrid neural network is designed. Finally, the results of joint knowledge-driven and data-driven parallel detection define a mixed fixed calculation formula, and the active and passive hybrid detection method of FDIAs is established considering the characteristic constraints of the parallel mode. The simulation system example of power CPS FDIAs verifies the effectiveness and accuracy of the method proposed in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Adaptive filters

Coordinated cyber-attack detection model of cyber-physical power system based on the operating state data link

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arXiv 2021年

作者： Wang, Lei Xu, Pengcheng Qu, Zhaoyang Bo, Xiaoyong Dong, Yunchang Zhang, Zhenming Li, Yang School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Siping Power Supply Company of State Grid Jilin Electric Power Company Limited Siping China

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber-Physical power System (CPPS). Two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms. © 2021, CC BY-NC-ND.

关键词： Decision trees

Lightweight helmet detection algorithm based on improved YOLOv5 2

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Lightweight helmet detection algorithm based on improved YOL...

2nd International Conference on Advanced Algorithms and Signal Image processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： Signal detection