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9th Purple Mountain Form on Smart grid Protection and control, PMF 2024

作者： Li, Junhao Wu, Yuanbao Lu, Peng Zhang, Qingquan Wang, Yaqing Zhang, Ning Lin, Ye Ma, Qian College of Information and Electrical Engineering China Agricultural University Haidian District Beijing100083 China Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China Power Dispatching and Control Center of China Southern Power Grid Guangdong Province Guangzhou510000 China

ISBN: (纸本)9789819616701

Extreme weather poses a problem to the operation and scheduling of a large proportion of modern energy generation systems due to its low chance of occurrence and high destructiveness. To that aim, this paper provides a day-ahead optimal dispatch model for large-scale wind power (LSWP) integration in power systems that takes cold wave weather into account, directly linking system outages and unit up/down with cross-section current fluctuations. Firstly, Wasserstein- Generative Adversarial Network WGAN is constructed to generate the output of wind farms during cold wave weather to solve the sample shortage problem;secondly, a wind-thermal power joint optimal dispatch model based on the low output of wind power is constructed to minimize the expected total system cost to cope with all the uncertainties caused by cold weather and transform the model into a mixed integer programming model;finally, the model is solved using the nonlinear transformation technique of segmental linearization. The suggested method's effectiveness is validated using an enhanced 24 node standard test system, and the study demonstrates that it successfully handles the problem of LSWP grid-connected scheduling with substantial variations during cold-wave weather. © State grid Electric power 2025.

关键词： Wind power integration

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Risk Assessment of Coupled Gas-Electricity Systems Under Extreme Weather 19th

Risk Assessment of Coupled Gas-Electricity Systems Under Ext...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Guangzeng Huang, Yuteng Wang, Wen Zhang, Jiansong He, Dong Yu, Fengshuo State Grid Zhejiang Electric Power Company Ltd. Zhejiang China State Grid Zhejiang Electric Power Company Ltd. Information and Communication Branch Zhejiang China Department of Electrical Engineering Tsinghua University Beijing China

ISBN: (纸本)9789819608966

At present, the impact of extreme weather on power systems is becoming more and more significant, especially when the coupling degree of natural gas systems and power systems is strengthened. Conventional safety assessment processes for coupled gas-electricity systems fail to account for the possible impacts of extreme weather. Therefore, a risk assessment model for coupled gas-electricity systems under storm conditions is proposed in this paper. Firstly, the possible effects of weather, including wind speed and temperature factors, on the components of the system are studied. Secondly, the risk indicator on the system level is defined to measure the change of system security over time under different states. Finally, a case study of a city-level coupled gas-electricity system of a city-level system is given to illustrate the effectiveness of the proposed method. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Associated gas

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Enhancing Network Abnormal Detection With NMF-SECNN: Leveraging Deep Feature Learning for High-Precision Traffic Analysis

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2069-2080页

作者： Yuan, Yazhou Yu, Ning Zheng, Zhuolin Yang, Yong Ma, Kai Liu, Zhixin Chen, Cailian Zhang, Jianmin Yanshan University Institute of Electrical Engineering Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education of China Qinhuangdao066004 China State Grid Handan Electric Power Supply Company Hebei 056000 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Baosteel Research Institute Automation Department Shanghai200240 China

Detection of abnormalities in industrial network traffic plays a crucial role in maintaining network system security. However, current abnormal detection models suffer from low precision, and extracting deep-level feature information from industrial network traffic is difficult. This leads to the loss of partial feature information during the detection process, thereby affecting detection efficiency. To address this issue, this paper proposes an abnormal traffic detection framework for industrial networks. By employing a Non-negative Matrix Factorization (NMF)-based method for extracting abnormal traffic features and optimizing the NMF decomposition process through constructing label consistency constraints, we facilitate effective feature extraction. Additionally, the Squeeze-and-Excitation attention mechanism is introduced into a Convolutional Neural Network (CNN) to construct a classifier that enhances detection precision without increasing complexity, enabling efficient identification of complex network traffic patterns. This results in the NMF-Squeeze-and-Excitation-CNN (NMF-SECNN) model, which combines effective feature extraction capability with a lightweight structural design, achieving superior detection performance in industrial network environments. The proposed method achieves a detection accuracy of 99.4%, representing a 5.6% improvement over baseline methods, and a recall rate of 98.2%, showcasing the model's capability to identify abnormalities across diverse scenarios. Various classification metrics confirm the model's robustness and effectiveness, demonstrating its significant advantages over traditional methods. © 2013 IEEE.

关键词： Non-negative matrix factorization

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Physical Intrusion Attack Detection in Fieldbus Network With Passive Fail-Safe Biasing

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14008-14019页

作者： Wang, Xiangming Liu, Yang Yu, Nanpeng Liu, Pengfei Deng, Nanyi Li, Yanan Liu, Ting Xi’an Jiaotong University School of Cyber Science and Engineering MoE KLINNS Laboratory Xi’an710049 China University of California at Riverside Department of Electrical and Computer Engineering RiversideCA92521 United States Xi’an Thermal Power Research Institute Company Ltd. Power Infrastructure Cyber Security Research Center Xi’an710054 China Electric Power Dispatch and Control Center of Guangdong Power Grid Company Ltd. Guangzhou510699 China

Fieldbus is widely used for real-time distributed control in Industrial control Systems (ICSs) due to its simplicity and stability. The real-world fieldbus network contains hundreds of interconnected devices, presenting a widespread network layout. Attackers can attach external intrusion devices to these communication lines to launch various attacks. In this paper, we model the fieldbus network’s channel fingerprint based on the signal’s amplitude and propose a detection method to identify potential attackers (silent intrusion devices that are eavesdropping) via channel fingerprint differences. Leveraging the passive fail-safe biasing voltage in the fieldbus network such as RS485, we can still detect the intrusion device when the fieldbus is idle (i.e., no devices are transmitting commands), which can significantly reduce the detection delay with lower sampling costs. Moreover, our method can adapt to environmental changes with little computational overhead by generating dynamic thresholds. Using a monitoring unit with stored channel fingerprints, our method can be easily deployed in fieldbus networks without occupying communication resources. The effectiveness and robustness of the proposed method have been demonstrated via extensive experiments on two real-world scenarios and one simulation scenario, where we can achieve 100% accuracy and 0% false alarm rates against various intrusion devices. © 2004-2012 IEEE.

关键词： Network intrusion

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Research on Routing Optimization Algorithm for power OTN Based on DQN 24

Research on Routing Optimization Algorithm for Power OTN Bas...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Hu, Yang Yu, Yang Zhang, Qiang He, Yujun Information and Communication Company State Grid Jilin Electric Power Co. Ltd. Jilin Changchun China Department of Electronic and Communication Engineering North China Electric Power University Hebei Baoding China

ISBN: (纸本)9798400712883

Routing optimization is key to improve the performance of power OTN. To solve the unequally distributed traffic load in power OTN, this paper put forward a power OTN routing optimization algorithm based on DQN. The power OTN was logically divided into states according to the technology characteristics of DQN, with corresponding actions, rewards and DQN agent designed for its operation. It generated optimized routing strategies that produce network routing optimization after several rounds of device-level iteration. The experimental method verified good convergence and effectiveness of the algorithm through simulation experiments on a 14 nodes NSFNet network. © 2024 Copyright held by the owner/author(s).

关键词： Routing algorithms

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Concentric Circles Nested Array Design Method for Acoustic Imaging Based on Differential Co-Array Model

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IEEE Sensors Journal 2025年第10期25卷 18106-18114页

作者： Xie, Zhiyuan Yang, Yan Zhang, Junyan Zhu, Wenzhao Bai, Zonglong North China Electric Power University Department of Electrical and Communication Engineering Baoding071003 China Hebei Provincial Key Laboratory of Power Internet of Things Baoding071003 China Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Education Ministry of China Qinhuangdao066004 China

Acoustic imaging intuitively illustrates the locations of sound sources and has a wide range of applications, including fault detection. However, this technology requires a substantial number of microphones, which increases the complexity and cost of the data acquisition system. To reduce the number of microphones while maintaining the performance of acoustic imaging, this paper introduces a concentric circular nested array (CCNA) design method. By extending the array using a differential co-array model, a virtual uniform concentric circular array is achieved. This virtual array preserves the advantages of the circular array structure while minimizing the number of required microphones. The performance of the proposed array is validated through simulation experiments. The simulation results demonstrate that the CCNA offers higher resolution, a greater dynamic range (DR), and improved positioning accuracy in acoustic imaging compared to other arrays. Finally, this paper compares the acoustic imaging results of the CCNA with those of the uniform circular array (UCA) through experiments. The experimental results reveal that the positioning accuracy of the proposed CCNA surpasses that of the UCA, thereby confirming the superiority of the proposed design. © 2001-2012 IEEE.

关键词： Acoustic imaging

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Numerical Simulation of Streamer Discharge in Transformer Oil Under Various Ionization Energies 19th

Numerical Simulation of Streamer Discharge in Transformer Oi...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Bao, Yanyan Liang, Shaobin Wang, Feng Zhang, Guangdong Chen, Jianning Zhou, Yuanxiang State Grid Gansu Electric Power Research Institute Lanzhou730070 China State Key Laboratory of Power System Operation and Control Beijing100084 China Department of Electrical Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9789819613786

Discharge in transformer oil is one of the primary causes of transformer failures. The physical mechanisms of oil discharges are not yet fully understood. Scholars have simulated the discharge process with electrodynamics model, but there is limited research on how the ionization energy, which changes due to various factors such as components and temperature of transformer oil, affect the streamer discharge characteristic. To improve the mechanism of discharge in transformer oil, this paper adopts an electrohydrodynamic model to study the impact of different ionization energies on the streamer discharge characteristics of transformer oil, including initiation voltage, discharge voltage, development speed, and electric field strength. When the ionization energy is between 6.1 eV and 8.1 eV, the initiation voltage and discharge voltage increase linearly with the ionization energy. Within the range of 7.1 eV to 8.1 eV, the time in the streamer accumulation phase increases exponentially with the ionization energy, while the streamer development speed decreases. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Electrohydrodynamics

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An Asynchronous Robust Federated Learning Approach for Data Value in IoT 3rd

An Asynchronous Robust Federated Learning Approach for Data ...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Liang, Hua Jin, Min Yan, Hua Han, Shihai Li, Wei Yang, Zhigang Wang, Zhuotong State Grid Chongqing Electric Power Company Electric Power Research Institute Chongqing401123 China Digitization Department State Grid Chongqing Electric Power Company Chongqing400015 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

ISBN: (纸本)9789819624676

The heterogeneity of IoT devices, resource bottlenecks, and security risks present challenges to the robustness and privacy of federated learning. The difference in the value of IoT data further weakens the robustness of the model. Additionally, attack methods such as Byzantine attacks and privacy inference attacks escalate the risks and vulnerabilities of federated learning models. To address these issues, we propose an asynchronous robust federated learning method based on data value. Firstly, an asynchronous grouping robust aggregation (AGRA) algorithm is designed and combined with a cosine anomaly filter to eliminate abnormal gradients. Simultaneously, an inter-group delay aggregation strategy oriented towards data value is proposed. Secondly, an efficient and robust aggregation protocol supporting asynchronous grouping is constructed to significantly reduce communication overhead while safeguarding data privacy. Finally, through data grading and secure storage adjustment using differential privacy, high-value data can be maximized, thereby enhancing the efficiency and performance of federated learning while protecting privacy. Results show that the algorithm can significantly improve accuracy by 0.9% to 65.8% in the presence of stragglers, while also reducing communication overhead by 4.2% to 5.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Operation Improvement of Garbage power Plants - via control Technology 16th

Operation Improvement of Garbage Power Plants - via Contro...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Cao, Guizhou Xia, Dawei Meng, Kai Zhu, Zheran Yuan, Jie Wu, Zhenlong State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou450052 China State Grid Shangqiu Electric Power Company Shangqiu476002 China State Grid Henan Electric Power Company Zhengzhou450018 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China

ISBN: (纸本)9789819617760

This paper focuses on the operation improvement of garbage power plants, which are a key disposal technology for household waste, construction waste, etc. However, the low of the furnace temperature can cause a sharp increase in dioxins and result in consequences of severe environmental pollution. To enhance the control performance of the combustion system for garbage power plants, proportional-integral-derivative based on predictive (PID-predictive) and proportional-integral based on predictive (PI-predictive) controllers are designed for the combustion system, which is dominated by time delay. By equaling the desired dynamic characteristics and the closed-loop system combined with the combustion system and controller, the PID-predictive and PI-predictive can be designed. The comprehensive comparison of the tracking performance, disturbance rejection ability, measurement noise rejection performance and ability to handle system uncertainties is carried out with PI, active disturbance rejection control, delay active disturbance rejection control and Smith predictor active disturbance rejection control. Simulation results show that PID-predictive and PI-predictive obtain similar control performance, and have a faster tracking speed than other comparative controllers. The disturbance rejection ability of PID-predictive and PI-predictive is weaker than delay active disturbance rejection control and Smith predictor active disturbance rejection control, and is stronger than PI and active disturbance rejection control. When the controlled process has system uncertainties, PID-predictive and PI-predictive still obtain satisfactory control performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disturbance rejection

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Investigation of the Effect of Bonding Wires Degradation on Switching Stress Waves Released During power Cycling in Discrete IGBT

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IEEE Journal of Emerging and Selected Topics in power Electronics 2025年第2期13卷 2057-2069页

作者： Geng, Xuefeng He, Yunze Tang, Longhai Chang, Shan Zhang, Jie Yang, Wenxue Yuan, Man Wang, Guangxin Li, Qiying Ping, Yang Huang, Songling Hunan University College of Electrical and Information Engineering Changsha410082 China Tsinghua University State Key Laboratory of Power System Operation and Control Beijing100084 China Hunan University Shenzhen Research Institute Shenzhen518000 China Tsinghua University Department of Electrical Engineering Beijing100084 China

Effective condition monitoring (CM) technology ensures the safe operation of insulated gate bipolar transistors (IGBTs). Most CM methods for IGBTs focus on extracting electrical, thermal, and magnetic parameters. Recent studies indicate that at the switching moment, IGBTs can emit the switching stress wave (SSW) that is detectable by the acoustic emission (AE) sensors. However, using these SSWs to monitor the health status of IGBTs, even for discrete devices with relatively simple package structures, remains challenging. This article investigates how SSWs from discrete IGBTs during power cycling change as bonding wires degrade and whether these waves are promising for monitoring the condition of internal bonding wires. In this work, a power cycle test platform and an SSW test platform for discrete IGBTs are built, and SSWs from discrete IGBTs during power cycling are analyzed. Results show that SSWs vary with the degradation of the device’s bonding wires, with changes in the time domain indicator of signal energy, reflecting SSW strength, and the frequency domain indicator of the center of gravity (COG) frequency, reflecting frequency shifts. These findings suggest the possibility for developing AE-based detection technology (AEDT) in CM of the IGBT. © 2013 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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