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Editorial:The development of eco-friendly insulating gases and SF6-free equipment for‘net-zero’

High Voltage 2024年第1期9卷 1-1页

作者： Song Xiao Yi Li Ju Tang Xiaoxing Zhang State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and AutomationWuhan UniversityWuhanHubeiChina Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment School of Electrical and Electronic EngineeringHubei University of TechnologyWuhanHubeiChina

Developing low-carbon gas insulated transmission and distribution equipment is essential to achieve the‘carbon peak,carbon neutralisation’or‘net-zero’in the power *** have carried out various research on the eco-friendly insulating gas(such as C4F7N,C5F10O)and next-generation SF6-free equipment to gradually reduce the utilisation of strong greenhouse gas SF6.

关键词： SF6 carbon free

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Research and Application of Reconfigurable Computing Approaches in power Network Security Processing

Research and Application of Reconfigurable Computing Approac...

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New Energy and power Engineering (ICNEPE), International Conference on

作者： Xiaojuan Guan Chen Wang Cheng Zhou Sheng Zhu State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd Nanjing China

ISBN: (数字)9798331516086

ISBN: (纸本)9798331516093

Given the issue that certain key components of the dedicated security protection equipment for the power system rely heavily on large-scale imported FPGA chips and specialized integrated circuits, and mature domestic alternatives and solutions have not yet been formed, this paper proposes the application concept of reconfigurable computing technology in the process of high-performance network security processing. The reconfigurable CGRA chip is led by China, with independently controllable technology, combining energy efficiency and flexibility, enabling efficient processing of computation-intensive tasks and completing the substitution for foreign FPGA chips.

关键词： Computers power engineering Process control Switches Network security Energy efficiency power systems Computational efficiency protection Field programmable gate arrays

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Research on Heterogeneous Computing technology Based on Domestic CPUs and Reconfigurable Chips

Research on Heterogeneous Computing Technology Based on Dome...

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Intelligent power and Systems (ICIPS), International Conference on

作者： Xiaojuan Guan Chen Wang Cheng Zhou Sheng Zhu State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd Nanjing China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

In view of the performance and security problems existing after the replacement of specialized security protection equipment for power grids with domestic software and hardware, and in response to the security protection requirements of different application scenarios, the research on heterogeneous fusion computing technology based on domestic CPUs and reconfigurable chips is proposed. The optimal mapping of national secret algorithms, isolation exchange, protocol processing, and data analysis on reconfigurable chips is realized, supporting the parallel computing of special processors for scene acceleration and domestic CPUs, and meeting the requirements of different network security analysis and computing scenarios in the power system.

关键词： Protocols Program processors Data analysis Software algorithms Heterogeneous networks power grids Hardware Encryption Security protection

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An Intelligent Multi-Source Data Clustering and Analysis Method for AC/DC Hybrid power grid Planning with Increased Complexity 6

An Intelligent Multi-Source Data Clustering and Analysis Met...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Lan, Tu Zhou, Aihua Diao, Ruisheng Sun, Fangyuan Zeng, Ruiyuan Shen, Xiaofeng Huang, Chenhong Zheng, Zhen ZJU-UIUC Institute Zhejiang University Zhejiang Haining314400 China State Grid Smart Grid Research Institute Co. LTD. State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Jiangsu Nanjing210003 China ZJU-UIUC Institute Zhejiang University State Key Laboratory of Clean Energy Utilization Zhejiang Haining314400 China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai201799 China

ISBN: (纸本)9798350346671

In order to better analyze the response characteristics of an AC/DC hybrid power grid with increased complexity following various types of disturbances, this paper proposes an intelligent method for clustering dynamic responses of multi-source data obtained from actual measurements or high-fidelity simulators. Base cases for AC/DC hybrid power grid planning students are created first, and a list of high-impact contingencies is searched using a graph algorithm. Then, the electromechanical-electromagnetic hybrid simulation method is adopted to create multi-source simulation data in terms of dynamic voltage, frequency, etc. An unsupervised learning algorithm and cluster analysis are then conducted to extract key features of dynamic response characteristics of the AC/DC hybrid power grid and create specific cluster labels. Advanced analysis, e.g., fast evaluation of risks from multi-source steady-state data, can then be enabled for further analysis. The proposed approach's effectiveness is verified on the actual AC/DC power grid model in East China. © 2023 IEEE.

关键词： Dynamic response

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Multi-source System Log Behavior Pattern Mining Method Based on FP-Growth 23

Multi-source System Log Behavior Pattern Mining Method Based...

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Proceedings of the 2023 International Conference on Communication Network and Machine Learning

作者： Daojuan Zhang Tianqi Wu Xiaoming Zhou Bo Hu Wenjie Zhang State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute co. Ltd China State Grid Liaoning Electric Power Supply Co. Ltd. China

ISBN: (纸本)9798400716683

By analyzing and mining system logs, it is possible to effectively discover behavioral characteristics and anomalies of network users or systems. Through association analysis, patterns and correlations between different log sources can be identified, thereby enabling the detection of various intrusion behaviors in computer networks. To address the challenge of handling massive and complex log data, this paper proposes a multi-source system log behavior pattern mining method based on FP-Growth. This method efficiently extracts frequent patterns and abnormal behaviors from large-scale system logs. Firstly, to ensure data quality and facilitate subsequent data mining processes, we perform necessary structuring and cleansing of raw logs and transform them into a format suitable for data analysis. Subsequently, given the variations in structure and content across different types of logs, we conduct distinct feature extraction for each type of log to retain essential information and generate transaction items. The data is then organized into datasets based on temporal partitions, forming a transaction database as input for the association rule mining algorithm. Finally, utilizing the FP-Growth association rule mining algorithm, this paper explores the relationships between entries from various types of single and multi-source logs. Based on metrics such as support, appropriate association rules are selected for behavior pattern mining. Experimental results demonstrate that the proposed method effectively uncovers typical behavior patterns.

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Automated Security Penetration Testing Based on Machine Learning

Automated Security Penetration Testing Based on Machine Lear...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Peng Zhang Wenhui Wang Daojuan Zhang Hongbin Wu Longxi Han Qi Zhao State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd. Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Penetration testing is a crucial measure for preventing cyberspace attacks. Typically, penetration testing is conducted manually, which presents challenges such as reliance on experience, high costs, and low efficiency. This study proposes an automated penetration testing method that integrates machine learning algorithms with Metasploit to achieve low-code generation and execution of penetration testing cases. This approach enhances testing efficiency, reduces costs, and mitigates dependence on experience. The study introduces a decision-making algorithm to facilitate the selection of exploit files during the generation of penetration testing cases and leverages Metasploit to implement basic penetration testing functionalities. Experimental results show that the algorithm achieves an accuracy of 73.16%, and the method successfully meets the objective of automated penetration testing.

关键词： Machine learning algorithms Costs Accuracy Decision making Cyberspace Machine learning Security Penetration testing Testing

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Attack Path Extraction based on VGAE and DBSCAN

Attack Path Extraction based on VGAE and DBSCAN

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Cyber Science and technology Congress (CyberSciTech), IEEE

作者： Ziang Lu Lu Chen Yong Li Guangxin Guo Tengyan Wang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd Nanjing China State Grid Beijing Electric Power Research Institute Beijing China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Mining and analyzing potential attack paths in networks based on alarm information can provide references for accurate and effective network defense measures. However, current research lacks thorough extraction and integration of deep features from alarm information. In this work, we propose APE-VD, an attack path extraction model based on VGAE and DBSCAN. It involves constructing network attack graphs based on host connectivity information and alarm types. Additionally, it creates temporal attack graphs by combining alarm data from different time slices with flow relationships. Using variational autoencoders, the temporal attack graphs are reduced in dimensionality to obtain alarm flow features. DBSCAN clustering is utilized to mine outlier nodes. To enhance clustering accuracy and robustness, second-order temporal risk features are extracted based on outlier nodes as prior information. Further, variational autoencoders and DBSCAN clustering are applied again. After filtering based on risk thresholds, forward and backward retrieval of nodes is conducted to derive attack paths. Simulation results on widely used log datasets demonstrate that APE-VD can efficiently uncover attack paths within limited computational complexity, aiding in reconstructing attack scenarios.

关键词： Accuracy Filtering Simulation Autoencoders Feature extraction Robustness Real-time systems Encoding Data mining Streams

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A Semi-Supervised Clustering-Based Approach for Sensing and Classifying Unknown Attacks in power Systems

A Semi-Supervised Clustering-Based Approach for Sensing and ...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Yong Li Zhipeng Shao Tao Zhang Yuanyuan Ma State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Enstitute Co. Ltd Nanjing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

To solve the problem of perceiving and classifying unknown attacks in power systems, the study proposes a method for perceiving and classifying unknown attacks in power systems based on semi-supervised clustering. The method first uses a hybrid algorithm of improved genetic algorithm (GA) and improved multi-grain cascade forest (GC-forest) power network system unknown attack for perception and detection, then increases the number of training data by marking extension algorithm, provides specific information of detected attack or event using the idea of semi-supervised clustering, and finally filters the clustered unknown attack or event using similarity metric method . The analysis of the performance results testing showed that the perceptual detection of unknown attacks by the improved GC-Forest and the improved Genetic Algorithm (GA) had a check-all rate, precision and accuracy of 97.1% and 96.9%, 98.9% and 98.6%, 99.0% and 98.7%, respectively, and the average computing time was 3.12s and 3.68, respectively. for the classification of unknown attack types, the semi-supervised clustering-based (SSL) classification method has 100%, 95% and 95% classification accuracy for normal, non-power network attacks and power network attacks, respectively, while the adaptive classification method has 100%, 92%-98% and 90%-95%, respectively. Taken together, these data illustrate that the research can efficiently and accurately sense and classify unknown attacks on power systems and provide some reference value for fault sensing and classification in other areas.

关键词： Measurement Radio frequency Clustering algorithms Training data Forestry Classification algorithms power systems

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A 5G Slice Traffic Anomaly Detection Method Based on Convolution Neural Network

A 5G Slice Traffic Anomaly Detection Method Based on Convolu...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Chen Lu Zhang Tao Ma Yuanyuan State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd Nanjing China

As the network security threats caused by the Internet of Things (IoT) continue to increase, the attack surface continues to expand and the network heterogeneity increases, and the use of virtualization technology and distributed architecture increases. Protecting the future network will become a challenging field. This paper proposes the approach of converting 5G slice traffic into images for analysis by a CNN (Convolutional Neural Network) model, and uses NAS (Neural Architecture Search) to detect anomalous network traffic. In order to improve the accuracy and efficiency of the intrusion detection system, this paper uses SDS(Software Defined Security) as a means to design a scalable, high-efficiency 5G traffic defense system. In the multiclass classification, the accuracy of CNN model is 94.9\%. The results from this method are promising as the model has performed in multiclass classification with an accuracy of 96.4\%. Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

关键词：

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Evolution and Challenges of the New-Type power System: Transformation under the Carbon Neutrality Goal

Evolution and Challenges of the New-Type Power System: Trans...

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Advanced Control Systems and Automation Technologies (ACSAT), International Conference on

作者： Tengyan Wang Nige Li Lu Chen Mu Chen Nanjing Branch of China Electric Power Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China

ISBN: (数字)9798331541668

ISBN: (纸本)9798331541675

Amid the severe backdrop of global climate change, achieving carbon peaking and neutrality has emerged as a universal consensus and the cornerstone of energy policies worldwide. As the largest energy consumer and carbon emitter, China’s profound transformation of its power system holds paramount significance for global climate governance. This paper delves into the backdrop, characteristics, current development status, multifaceted challenges, and future trends of China’s newtype power system. It aims to provide theoretical underpinnings and practical guidance for accelerating the realization of the “dual carbon” (carbon peaking and neutrality) goals. Additionally, this research explores the application of advanced control and automation technologies in the power system transformation. For example, the intelligent grid technologies, such as the Internet of Things and big data analytics, play a crucial role in enhancing the grid’s sensing and control capabilities, which are essential aspects of advanced control systems. Moreover, the research on energy storage technology and its integration into the power system involves innovative control strategies and optimization algorithms, directly contributing to the field of automation technologies in the power sector.

关键词： Climate change Automation Carbon neutral Control systems Market research power systems Sensors Carbon Sustainable development Meteorology

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