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Power Engineering (ICPE), International Conference on

作者： Zhiyuan Sun Chao Luo Kun Zheng Yihua Zhu Jiawei Yu Guangxi Power Grid Electric Power Research Institute China Southern Power Grid Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guang zhou China National Energy Power Grid Technology R&D Centre Guang zhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guang zhou China

ISBN: (数字)9798331531140

ISBN: (纸本)9798331531157

The grid-forming battery energy storage system (BESS) plays important role in constructing new energy system. This paper presents a frequency domain modal-based stability analysis of virtual synchronous generator (VSG) controlled BESS. Both ac and dc side interactive dynamics of the grid-tied inverter system are considered, by establishing a hybrid ac/dc impedance network. Then, the frequency domain modal analysis (FMA) method for networked converter systems is applied to the BESS system, covering the eigen-sensitivity from the node level to parameter level. Using this method, key parameters that influence the BESS system’s weakly/negative damping modes most can be better analyzed and identified. The analysis results are validated via the time-domain simulations.

关键词： Battery energy storage system Sensitivity analysis Frequency-domain analysis Modal analysis Stability analysis Synchronous generators Impedance Time-domain analysis Tuning Grid forming

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research on relay setting attack defense in power systems based on a three-layer optimization model

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Frontiers in Energy research 2024年 12卷

作者： ren, Liu Binyuan, Yan Hengdao, Guo Junrong, Liu Yihua, Zhu Yun, Fu Liang, Tu Zeyuan, Zhou State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China Information Centre of Guizhou Power Grid Co. Guizhou China

With the intelligent development of power systems, the number of relays continues to increase. differences in manufacturers, systems, and protocols lead to growing security risks. Tampering with relay settings could potentially cause power outages or even system instability. Consequently, relay settings have gradually become a key target for cyberattacks, particularly in smart grids where traditional defense measures struggle to effectively address complex and diverse attack scenarios. To address this issue, this paper proposes a three-layer optimization defense model based on game theory, designed to adapt to various attack scenarios. The core methodology of this model includes a three-layer structure: The first layer optimizes the protection level of each relay by allocating limited defense budgets. The second layer analyzes the potential system damage based on the attacker’s strategy choices. The third layer comprehensively calculates system losses to evaluate the effectiveness of defense plans. Through layer-by-layer optimization of budget allocation, the model minimizes the unsupplied energy loss caused by relay setting attacks. Compared to existing methods, this model not only improves defense effectiveness under resource constraints but also addresses multiple complex attack scenarios. Experimental results demonstrate that this model significantly enhances the system’s defense capabilities and effectively reduces the impact of attacks on system security operations. Copyright © 2024 ren, Binyuan, Hengdao, Junrong, Yihua, Yun, Liang and Zeyuan.

关键词： Budget control

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Nonlinear Modeling of the VSC Grid-Connected system with controller Saturation 4

Nonlinear Modeling of the VSC Grid-Connected System with Con...

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4th International Conference on Electrical Engineering and control Technologies, CEECT 2022

作者： Yu, Jiawei Zhu, Yihua Li, Chengxiang Luo, Chao State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid China National Energy Power Grid Technology R&d Center Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power Sytem Simiulation ELectic Power Reasearch Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9781665498999

With the development of power electronics technology, the power system is now evolving into a new-generation power system dominated by renewable energy. due to the physical limitations of power electronics, controller saturation is widely adopted in wind turbines and photovoltaics. In this paper, a nonlinear system model considering output saturation is proposed in the view of general dynamic systems. By defining saturation-activation vector L, all the saturations of the system are uniquely represented. Then, the proposed modeling method is applied to a voltage source converter (VSC) grid-connected system. Based on rTdS simulation, this paper revealed that a class of oscillations at a specific frequency are caused by the activation of the phase-locked loop (PLL) saturation. The frequency of the oscillation depends on the top and bottom boundaries of PLL saturation. © 2022 IEEE.

关键词： Chemical activation

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Application of deep reinforcement learning in power system scheduling strategy simulation

Application of deep reinforcement learning in power system s...

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Binjiang Hu Libin Huang Yufeng Hu Jie Zhang Haiping Guo Yuanhong Lu Zeqi Hong Jingyue Zhang Hui Luo Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

With the increasing scale and complexity of power system, the current scheduling strategy methods often have the problems of limited adaptability and unsatisfactory results. Therefore, a power system scheduling method using dQN (deep Q Network) is introduced in this study. This method uses neural network model to learn and optimize scheduling strategy, and has strong adaptability and enhanced global optimization ability. Comparative evaluations with genetic algorithms and particle swarm algorithms on IEEE-118 and SG-126 datasets show that the proposed method has significant advantages in terms of cumulative reward and stable runtime.

关键词： Electrical engineering runtime Neural networks Power system stability deep reinforcement learning Stability analysis real-time systems Particle swarm optimization Optimization Genetic algorithms

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A real-time Framework of Multi-Camera Vehicle Tracking system*

A Real-time Framework of Multi-Camera Vehicle Tracking Syste...

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IEEE International Conference on real-time Computing and robotics (rCAr)

作者： Hanyang Zhuang Xingyuan Chen Jianglin Lan Ying Wang Zhaoxuan Lu Chunxiang Wang Ming Yang University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China James Watt School of Engineering University of Glasgow Glasgow United Kingdom School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Intelligent and Connected Vehicle R&D Center Co. LTD Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Multi-camera vehicle tracking (MCVT) system is a key technology to build an intelligent city and intelligent transportation system. The MCVT system utilizes roadside monitoring devices and computing platforms to achieve city-level road traffic perception at a finer scale by tracking each vehicle on the road to obtain the trajectory. The existing MCVT system mainly focuses on dealing with the algorithm modules separately, without studying the real-time performance and system architecture of the entire pipeline. However, in real-world applications, many of the tricks in improving evaluation metrics are not applicable since there is endless traffic flow running all the time. Therefore, this paper proposes a real-time framework of the MCVT system to streamline all the algorithm modules with proper data flow. Firstly, a multi-threaded multi-vehicle tracking (MVT) module is developed to detect vehicles in each camera. re-identification is implemented during MVT to provide the feature for tracking in the same camera and across different cameras. Then, after the vehicle leaves the camera field of view, the feature of vehicle trajectory is calculated by averaging the feature at each frame. Meanwhile, a background thread runs continuously to match the incoming feature of vehicle trajectory with past features by conducting a topology searching method. In the end, the vehicle trajectories with well-matched features are considered to be the same vehicle. The entire pipeline has been validated on two different datasets, the precision is 0.64 in the CityFlow dataset which uses 6 cameras simultaneously. In the customized dataset, the same method can still reach real-time when using 16 cameras at the same time with smaller network modules.

关键词： Visualization Target tracking roads Human-machine systems Pipelines Urban areas Cameras

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Technical research on Simulation and Predictive Analysis of Wind Farms in Power systems

Technical Research on Simulation and Predictive Analysis of ...

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Tianyu Guo Libin Huang Yanjun Zhao Haiping Guo Yuanhong Lu Jie Zhang Hui Luo Binjiang Hu National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

To address the issue of insufficient accuracy in wind power output forecasting, this paper proposes a Support Vector Machine (VSM) model based on similar day matching and kernel entropy component analysis for wind power prediction. The model effectively extracts similar day features through the similar day approach and optimizes feature representation by combining it with kernel entropy component analysis (KECA), ultimately employing the SVM model for wind farm power forecasting. Simulation results demonstrate that using this model for optimizing wind power generation predictions enhances the accuracy of wind power forecasts, surpassing the precision of single-method predictions. The Similar day-KECA-SVM method leverages historical data from similar days and features extracted through KECA, with SVM performing the final prediction. This approach adeptly handles complex patterns and nonlinear relationships in wind power forecasting, thereby achieving superior predictive performance.

关键词： Support vector machines Analytical models Accuracy Wind speed Wind power generation Predictive models Feature extraction Entropy Forecasting Kernel

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Neural Network Technology Applied in Fault detection and Simulation of Power systems

Neural Network Technology Applied in Fault Detection and Sim...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Zeqi Hong Libin Huang Yufeng Hu Haiping Guo Yuanhong Lu Binjiang Hu Jingyue Zhang Hui Luo State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the escalating demand for power inspection and equipment maintenance, developing effective algorithms for power system fault detection is crucial for ensuring the safety and stability of power systems. This paper leveragesthe Trellis network, enhanced with an attention module, to significantly improve the Trellis network’s capability in recognizing power fault images, thus better addressing fault identification tasks within power systems. Through testing on a self-constructed dataset and comparative experiments with models such as dCNN, rCNN, and denseNet, the proposed attention mechanismenhanced Trellis network algorithm has demonstrated its effectiveness and superiority in fault detection.

关键词： Fault diagnosis Accuracy Attention mechanisms Fault detection Neural networks Power system stability Predictive models Feature extraction Electrical fault detection Maintenance

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research on the Application of Big data in Transient Stability Simulation of Power systems

Research on the Application of Big Data in Transient Stabili...

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Jie Zhang Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. Advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability.

关键词： Accuracy Computational modeling Power system stability Big data Predictive models Stability analysis data models real-time systems Transient analysis Thermal stability

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design and Implementation of Power system Simulation Platform Based on Cloud Computing

Design and Implementation of Power System Simulation Platfor...

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Yuanhong Lu Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control.

关键词： Cloud computing data privacy Accuracy Federated learning data security Power system simulation Power system dynamics Power system stability Power grids Testing

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distributed Power system Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MAdrL

Distributed Power System Simulation and Monitoring Supported...

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Hui Luo Jie Zhang Yufeng Hu Jingyue Zhang Haiping Guo Libin Huang China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

This study explores the optimization of distributed Generation systems (dGS) within a Multi-Agent deep reinforcement Learning (MAdrL) framework. The focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. The results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. The cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. The scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources.

关键词： Photovoltaic systems HVAC Processor scheduling system performance Collaboration deep reinforcement learning Smart grids resource management Vehicle dynamics Energy storage

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