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Information and Communication technology (ICTech), International Conference of

作者： Junfeng Qiao Zhujian Ou Lin Peng Fuyun Zhu Aihua Zhou Pei Yang State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co.ltd. Nanjing Jiangsu China Nantong Power Supply Company State Grid Jiangsu Electric Power Co. LTD. Nanjing Jiangsu China

In the electric distribution grid, the access of distributed electric power generation also brings new technical challenges. From the perspective of operation, after the distributed generation is related to the electric distribution grid, due to the uncertainty of wind energy, solar energy and other new energy, there will be higher harmonics in the distribution network that affect the power quality and power supply reliability. On the other hand, it is necessary to consider the economic cost and reasonably distribute the output status of different units in the distribution network to achieve the unity of economic and ecological benefits. While, it has occasion to research the dispatching method of electric distribution grid with distributed generation. This paper uses digital twinning technology to build virtual equipment modeling scheme, which integrates equipment basic data, system single line diagram and grid geographic coordinate data. With rapid processing governance as the core and electric grid topology computing function, it realizes the whole process management of electric distribution grid dispatching and management, and provides production management personnel with distribution network digital simulation platform and auxiliary decision-making function.

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Research on the Integration and Fusion technology of Distribution Network Data Resources Considering New Energy 23

Research on the Integration and Fusion Technology of Distrib...

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Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering

作者： Junfeng Qiao Aihua Zhou Lin Peng Yiqing Wang Xiaofeng Shen Sen Pan Pei Yang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD China Xuzhou Power Supply Company of State Grid Jiangsu Electric Power Co. LTD China Qingpu Power Supply Company of State Grid Shanghai Municipal Electric Power Company China

ISBN: (纸本)9798400708299

The essence of the construction and development of new power systems is to improve the functionality and performance of existing power systems, and the distribution network is the key to the functionality of the power system. Therefore, the upgrading and optimization of the distribution network is the key to the success of the modern energy revolution, and the development of the distribution network is also faced with management and technical challenges brought by new equipment and resources. This article proposes a distribution network data resource integration and fusion method that takes into account new energy, integrating the constantly emerging new power equipment and facilities as well as their corresponding data resources into existing power information management systems. Using the integrated distribution network data platform as the data base to support the deepening and development of distribution network business applications. Firstly, the data description characteristics of the new power equipment and facilities are sorted out, and the description rule system is extracted using the methods of power equivalence processing and equipment profiling. Due to the lack of standardized description rules for new energy equipment, it is necessary to reorganize and construct a corresponding description rule system. Then map and correspond its description rules with existing power information description rules, find its mapping method, and build a mapping rule library. Finally, based on the mapping rule library, the fusion of new energy data and existing distribution network data resources is achieved, and the accuracy and applicability of data resource fusion are verified by selecting real data from business application scenarios.

关键词： Application validation Data mapping rules Data resource integration Distribution network New energy

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Multi-Task Terminal Radio Frequency Fingerprint Feature Extraction Framework Based on Shuffle Network

Multi-Task Terminal Radio Frequency Fingerprint Feature Extr...

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International Conference on Computer and Communications (ICCC)

作者： Jingqi Hu Tao Zhang Chong Tan Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai Academy of Sciences University of Chinese Beijing State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd Nanjing

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the process of extracting and identifying wireless radio frequency signal fingerprints, the hardware differences between devices of the same type are too small to extract sufficiently distinct and effective features. To address this issue, this paper proposes a multi-task terminal radio frequency finger-print feature extraction framework based on a shuffle network, called RFFRe (Radio Frequency Fingerprint Re-identification). This framework can effectively capture the set of features corresponding to the subtle hardware differences from radio frequency signals. The framework employs a ResNet network model, removing the traditional fully connected layer and using the deep network model solely as a feature extractor for radio frequency signals. Additionally, it incorporates a shuffle network to apply additional perturbations to the extracted features, enhancing feature variability. Further, it performs multi-task learning on the perturbed feature space, using metric learning and representation learning to extract the distribution characteristics of feature vectors in the feature space. This compactly clusters intra-class features while improving inter-class discriminability. This model can complete the device registration process with a single training session when new devices are added and achieve quick identification through simple feature similarity comparison during device access. Experimental results show that RFFRe achieves a recognition Rank-1 accuracy of 99.4% on devices of the same type with minimal hardware differences, outperforming similar solutions.

关键词： Representation learning Performance evaluation Accuracy Perturbation methods Computational modeling Fingerprint recognition Feature extraction Multitasking Hardware Object recognition

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Classification and Grading for power Sensitive Data Based on Hybrid Data Distribution Learning

Classification and Grading for Power Sensitive Data Based on...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Xiuli Huang Yujia Zhai Congcong Shi Yiwen Jiang Shijun Zhang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China Big Data Center of State Grid Corporation of China Beijing China State Grid Jibei Electric Power Co. LTD. Beijing China

power industry data contain various sensitive information, and the leakage of such data will cause critical damage to both the company and the country. However, the unbalanced and various data types make the traditional data classification and grading method fail to achieve enough performance. To better classify and grade the power sensitive data, we propose a Hybrid Data Distribution Learning Network, which simultaneously learns the distribution of multiple data types, including Character-based, Text-based, and Numerical data. Furthermore, we use an optimized nonlinear Support Vector Machine model to detect the grade of power data based on the learned data distribution. The experimental results show that our method outperforms other baselines and achieves a precision of 0.994, a recall of 0.985, and an F 1 of 0.989. The visualization results also show that our method effectively learns the distribution of multiple types of power data.

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Online Network traffic anomaly detection method combining OS-ELM and SADE

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IEEE Access 2023年 1-1页

作者： Dai, Shiyong Zhao, Yufei Huang, Jinhua Wang, Xuxian Zhao, Guifei Zhang, Lei Xie, Ming Guo, Ziyue China Southern Power Grid Key Laboratory of Power Grid Automation Laboratory Guangzhou Guangdong China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd Beijing China Beijing Institute of Electronic Information Technology Beijing China Power Research Institute of State Grid Gansu Electric Power Company Gansu China Guangxi Power Grid Co. Ltd Nanning Guangxi China Qingyuan Power Supply Bureau of Guangdong Power Grid Co. Ltd Qingyuan Guangdong China

Network traffic anomaly detection methods can detect traffic that is significantly different from normal traffic by analyzing network traffic, and are seen as an effective means to detect unknown new attacks because they do not rely on static feature codes. However, most of the current network traffic anomaly detection methods have low accuracy and high false alarm rate. In this paper, an OS-ELM (Online Sequential Extreme Learning Machine) network traffic anomaly detection method is proposed. First, we use SMOTE to balance the data samples to improve the classification detection performance. In order to reduce the high dimensionality between data features and eliminate the redundancy, the Stacked Denoising Autoencoder (SDAE) network is adopt to reduce the dimension of the feature vectors. Finally, we test the real network traffic and analyze the effect of model structure and external noise on the detection performance, and the experimental results verify the correctness of our scheme. Compared with other detection methods based on data reconstruction, the proposed method has higher detection accuracy and better detection performance. Author

关键词： Intrusion detection

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Research and Implementation of Operation monitoring and Resource Scheduling Methods Based on the Digital Twin Model of the power grid

Research and Implementation of Operation Monitoring and Reso...

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International Conference on Applied Machine Learning (ICAML)

作者： Junfeng Qiao Hai Yu Zhimin He Lianteng Shen Xiaodong Du State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China China Electric Power Research Institute Co. LTD Beijing China State Grid Hebei Electric Power Co. LTD Shijiazhuang China

The digital twin model of the power grid has been widely applied in fields such as power equipment status acquisition and power equipment fault analysis. The use of digital twin models to virtual equipment status parameters provides strong model support for the acquisition and display of power equipment status. The digital twin model describes the physical entity in the power grid as digital indicators, and superimposes effective business analysis logic, making it widely provide digital services for data using departments. Similarly, the virtualization and visualization of device status parameters can also provide technical support for monitoring power operation status and scheduling power resources. This article proposes an operation monitoring and resource scheduling method based on the digital twin model of the power grid, which abstracts the status data of power grid equipment into key indicators of the digital twin model. Then, high-performance data collection and transmission technology is used to push the status data of power grid equipment and power grid operation status information to the power management department in a way that is easy to observe and edit. By adopting this approach, power managers do not need to conduct on-site surveys to monitor the operating status of electricity, and then develop resource scheduling plans to schedule electricity resources and achieve secondary distribution, providing better power supply services for electricity customers.

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Deep Learning-Based Control Model for Electronic Evidence Access Rights

Deep Learning-Based Control Model for Electronic Evidence Ac...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Peng Chen Yongliang Li Xianzhou Gao State Grid Ningxia Electric Power Co.Ltd. Ningxia China State Grid Ningxia Electric Power Co.Ltd. Electric Power Research Institute Ningxia China State Grid Smart Grid Research Institute Co. Ltd. Nanjing China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China

With the rapid development of digitalization and intelligence in smart grids, how to identify untrusted users in the electric power system and implement user access control based on electronic evidence has become an important research topic. A credible assessment needs to be made on the subject users accessing electronic evidence. In this paper, we propose a deep learning-based user multi-dimensional attribute feature extraction access control model, which considers multi-dimensional attribute features such as user identity, behaviour, and device information, and determines whether the user is a legitimate access user by using CNN-Transformer deep learning framework to extract user features and determine their trust value. Through simulation experiments, it is proved that the access permission control model proposed in this paper has high accuracy and reliability and can meet the access requirements of different electronic evidence users.

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Research on Digital Twin Modeling technology for Distribution Networks Integrating Multiple Digital Resources

Research on Digital Twin Modeling Technology for Distributio...

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Intelligent Computing, Communication & Convergence (ICI3C), International Conference on

作者： Junfeng Qiao Aihua Zhou Lin Peng Hang Su Zhonghao Qian Min Xu Sen Pan Pei Yang State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China State Grid Jiangsu Electric Power Co. LTD Xuzhou Power Supply Company Xuzhou China State Grid Jiangsu Electric Power Co. LTD Nantong Power Supply Company Nantong China

ISBN: (数字)9798350343922

ISBN: (纸本)9798350343939

The sources of digital resources in the distribution network mainly include three aspects: equipment ontology, production operation, and management. The data is described in various forms and stored in various types of systems and data platforms. However, with the continuous development and upgrading of distribution network business, the fusion of diverse data resources to drive decision-making has become a current development trend. Therefore, how to integrate and apply diverse digital resources is the key to the current development of distribution networks. In response to this issue, the article proposes a digital twin modeling technique for distribution networks that integrates multiple digital resources. Firstly, build a cross platform data mapping system based on the production and operation data management rules of the distribution network, and then use data access and integration technology to integrate diverse data resources into a unified data platform. Utilize the unified data model of the power grid to align multiple data resources and map the aligned data resources to the digital twin model. Simultaneously defining data synchronization update interfaces to ensure real-time and accurate data in the digital twin model. Finally, a typical scenario application of the distribution network based on the digital twin model is carried out to verify the effectiveness and practicality of the model.

关键词： Location awareness Accuracy Voltage fluctuations power quality Distribution networks Production Data models Real-time systems Digital twins Synchronization

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Risk Assessment of Distributed PV Microgrids Under cyber Attacks based on CNN-AE

Risk Assessment of Distributed PV Microgrids Under Cyber Att...

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IEEE International Conference on Smart grid and Smart Cities (ICSGSC)

作者： Shenjian Qiu Shiang Guo Zhipeng Shao Jiaxuan Fei Quan Liu Kaikai Pan State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China Zhejiang University School of Electrical Engineering Hangzhou China State Grid Jiangsu Electric Power Co. Ltd. Yangzhou Power Supply Branch Yangzhou China

ISBN: (数字)9798350377408

ISBN: (纸本)9798350377415

The widespread application of smart grid technology and numerous Internet of Things (IoT) devices expose microgrid vulnerability to various cyber attacks. Moreover, the complex interactions and implicit risk propagation within the system pose challenges in accessing the risk from cyber attacks. To this end, we propose a risk assessment method for distributed PV microgrids under cyber attacks. This program employs an autoencoder (AE) architecture where convolutional neural networks (CNN) serve as the intermediate layer to extract the local features of data exchange from adjacent nodes. The AE conducts unsupervised learning using power data under normal operating conditions, thus performing risk assessment by evaluating the reconstruction errors. To validate the proposed method, we conducted training on the algorithm using historical data and the topology of a real scenario of a distributed photovoltaic microgrid and tested the risk assessment with various fake data injection attacks (FDIA) and Denial of Service (DoS) attacks.

关键词： Photovoltaic systems Distributed databases Microgrids Feature extraction Topology Smart grids Risk management Convolutional neural networks Internet of Things cyberattack

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cyber-Attack Detection Approach In Photovoltaic Microgrids Using LSTM-based Autoencoders

Cyber-Attack Detection Approach In Photovoltaic Microgrids U...

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power Engineering (ICPE), International Conference on

作者： Quan Liu Shiang Guo Shenjian Qiu Jiaxuan Fei Bo Zhang Xiang Yu Kaikai Pan Yangzhou Power Supply Branch State Grid Jiangsu Electric Power Co. Ltd. Yangzhou China Zhejiang University School of Electrical Engineering Hangzhou China State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China

Photovoltaic (PV) microgrids have been exposed to serious cyber threats for the integration of information and communication technologies (ICT) and renewable energy. In this article, a data-driven detection approach using an LSTM-based autoencoder has been developed against cyberattacks including fake data injection attacks (FDIA) and denial of service (DoS) attacks in PV microgrids. The proposed detection algorithm applies the unsupervised learning method that uses an LSTM neural network to capture the temporal feature and autoencoder to get reconstructed residuals of given input sequence data of measurements. By comparing residuals with the given threshold, the decision can be made on whether the system is attacked. The proposed algorithm has been evaluated for its performance in the PV microgrid model considering PV generation, energy storage system, and grid-tied function under various cyberattacks.

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