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arXiv 2021年

作者： Bo, Xiaoyong Chen, Xiaoyu Li, Huashun Dong, Yunchang Qu, Zhaoyang 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 Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China State Grid Jilin Electric Power Co. Ltd. Baishan Power Supply Company Baishan134300 China State Grid Jilin Electric Power Co. Ltd. Jilin Power Supply Company Jilin132011 China

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios. © 2021, CC BY-NC-ND.

关键词： Multi agent systems

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Method for extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation

arXiv

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arXiv 2020年

作者： Wang, Lei Qu, Zhaoyang Li, Yang Hu, Kewei Sun, Jian Xue, Kai Cui, Mingshi School of Electric Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China State Grid Jilin Province Electric Power Supply Company Changchun130000 China EastInner Mongolia Electric Power Company Hohhot010000 China

In the analysis of coordinated network attacks on electric power cyber-physical system (CPS), it is difficult to restore the complete attack path, and the intent of the attack cannot be identified automatically. A method is therefore proposed for the extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. First, the attack events are aggregated according to the alarm log of the cyber space, and a temporal-causal Bayesian network-based cyber attack recognition algorithm is proposed to parse out the cyber attack sequences of the same attacker. Then, according to the characteristic curves of different attack measurement data in physical space, a combination of physical attack event criteria algorithm is designed to distinguish the types of physical attack events. Finally, physical attack events and cyber attack sequences are matched via temporal-topological correlation, frequent patterns of attack sequences are extracted, and hidden multi-step attack patterns are found from scattered grid measurement data and information from alarm logs. The effectiveness and efficiency of the proposed method are verified by the testbed at Mississippi State University. Copyright © 2020, The Authors. All rights reserved.

关键词： Embedded systems

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power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

arXiv

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arXiv 2020年

作者： Qu, Zhaoyang Xie, Qianhui Liu, Yuqing Li, Yang Wang, Lei Xu, Pengcheng Zhou, Yuguang Sun, Jian Xue, Kai Cui, Mingshi College of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Tesearch Center of Intelligent Electric Power Big Data Processing Jilin132012 China Department of Electrical and Electronic Engineering University of Bath Bath United Kingdom School of Electrical Engineering Northeast Electric Power University Jilin132012 China State Grid Jilin Electric Power Supply Company Changchun13000 China State Grid Neimenggu Eastern Electric Power Supply Company Huhehaote010000 China

Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Cyber Physical System

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The recommendation method of power selling packages based on the optimal feature subset of power trading users

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Journal of Computers (Taiwan) 2019年第2期30卷 14-26页

作者： Qu, Zhao-Yang Feng, Rong-Qiang Qu, Nan Liu, Yao-Wei Yan, Jia Qu, Chong School of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China Maintenaue Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China

By aiming at huge user groups in the power market and the difficult selection of power selling packages in the trading process, a recommendation method of power selling packages based on the optimal feature subset of power trading users was proposed. First of all, the Optimal Feature Subset of power Trading Users (OFSPTU) was defined, and the optimal subset discovery algorithm based on coverage rate with weighing increase was designed, the optimal user feature subset could be screened out reasonably from mass trading users. Next, the similar computing method of power selling packages based on attribute correlation was proposed, in the form of clustering and confirming package attribute weighing, the package similarity was calculated, obtaining the nearest neighbor of package projects. At last, the accurate recommendation of power selling packages based on the OFSPTU and package attribute was realized. The experiment shows that the effectiveness of OFSPTU and accuracy of the recommended algorithm. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Sales

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electricity Sales Prediction Model of electricity Market of the Non-linear Regression Combination with the Optimal Weight

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Journal of Computers (Taiwan) 2019年第5期30卷 75-87S页

作者： Qu, Zhao-Yang Xie, Shu-Ya Feng, Rong-qiang Qu, Nan Lv, Hong-bo Chi, Zuo-Wei Qu, Chong Department of computer science and technology of the Institute of Information Engineering Northeast Electric Power University Jilin Jilin132012 China Intelligent Electric Power Big Data Processing Jilin Engineering Technology Research Center Jilin Jilin132012 China Maintenance Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China Guizhou Power Grid Co. Ltd. Guiyang Baiyun Power Supply Bureau GuiYang550014 China

The model to estimate electric sales in electric market of the optimal weighting nonlinear regression combination is brought forth to improve the accuracy of electricity sales forecast. Firstly, the least square method is used to estimate the nonlinear regression equation in order to obtain the result sets of the prediction sequence. Secondly, the design method of minimizing the calculation of fitting variance and weighting com-bination to calculate the residual sequence and weighting of the Radial Basis Function (RBF) neural network and the least squares vector machine, finally the result sets of residual sequence concluded. At the end, a nonlinear decreasing strategy of inertia weight is introduced on the basis of cuckoo algorithm to optimize the extended search space of model parameters and the model solution. The simulation results show that the model can accurately and comprehensively reflect the law of electric sales and improve the accuracy of sales prediction. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Sales

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Method for quantitative estimation of the risk propagation threshold in electric power CPS based on seepage probability

arXiv

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arXiv 2018年

作者： Zhaoyang, Q.U. Zhang, Yu Nan, Q.U. Wang, Lei Yang, L.I. Dong, Yunchang College of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China Maintenaue Company of Jiangsu Power Company Nanjing210000 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China Energy Systems Division Argonne National Laboratory IL60439 United States

Because of the non-uniformity of the electric power CPS network and the dynamic nature of the risk propagation process, it is difficult to quantify the critical point of a cyber risk explosion. From the perspective of the dependency network, this paper proposes a method for quantitative evaluation of the risk propagation threshold of power CPS networks based on the percolation theory. First, the power CPS network is abstracted as a dual-layered network-directed unweighted graph according to topology correlation and coupling logic, and the asymmetrical balls-into-bins allocation method is used to establish a "one-to-many" and "partially coupled" non-uniform power CPS characterization model. Then, considering the directionality between the cyber layer and the physical layer link, the probability of percolation flow is introduced to establish the propagation dynamic equations for the internal coupling relationship of each layer. Finally, the risk propagation threshold is numerically quantified by defining the survival function of power CPS network nodes, and the validity of the proposed method is verified by the IEEE 30-bus system and 150-node Barabsi-Albert model. Copyright © 2018, The Authors. All rights reserved.

关键词： Solvents

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