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检索条件"机构=State Key Laboratory of HVDC and CSG Key Laboratory for Power System Simulation"

共 314 条记录，以下是11-20 订阅

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A Multi-Objective Structural Optimization Method Based on Multi-Physics simulations for power Module

A Multi-Objective Structural Optimization Method Based on Mu...

引用

2024 International Exhibition and Conference for power Electronics, Intelligent Motion, Renewable Energy and Energy Management, PCIM Europe 2024

作者： Liu, Baihan Du, Mengyao Yan, Yiyang Lv, Jianwei Chen, Cai Kang, Yong Wu, Yue He, Zhipeng Huazhong University of Science and Technology China State Key Laboratory of HVDC Electric Power Research Institute CSG China

ISBN: (纸本)9783800762620

With the rise of wide-bandgap semiconductors SiC, power module packaging needs to be designed with all performance outstanding enough to give full play of advantages of SiC in harsh conditions. Due to the difficulty of taking into account all performance, packaging design of SiC power module faces great challenges. This study proposes a multi-objective structural optimization method based on Multiphysics simulation for power modules design, which combines Taguchi, TOPSIS, CRITIC, and entropy weight method to simplify the optimization process. Based on the proposed method, structural parameters of a SiC power module is optimized. According to the simulation results, a good effect of the compromise among the electrical, thermal, and mechanical performance is realized. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Structural optimization

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A Coordinated Optimization Method for Source-Load-Storage in Renewable power system Considering Overcharge/Overdischarge Cost of Stored Energy 6

A Coordinated Optimization Method for Source-Load-Storage in...

引用

6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Guo, Tianyu Huang, Libin Guo, Haiping 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: (纸本)9781665460033

Building a renewable power system with renewable energy as the main part can give full play to the regulatory capacity of sources, network, loads, storage and other types of controllable resources. It is the key to promoting clean energy transformation and achieving the goal of carbon peaking and carbon neutralization, and has become a research hotspot in the energy field. In this paper, a coordinated optimization method for source load storage in renewable power system considering overcharge/overdischarge cost of stored energy is proposed. Two state of Charge (SOC) thresholds for the energy storage are set, when the SOC exceeds the corresponding threshold, the cost of overcharge/overdischarge penalty is added respectively to effectively balance the security and regulation capability of energy storage;Then, set the wind/light abandonment cost and the demand response cost of adjustable load, to coordinate the output of source, load and energy storage, to improve the regulation capacity of the renewable power system. Finally, the correctness and rationality of the method proposed in this paper are effectively verified. © 2023 IEEE.

关键词： Energy storage

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Neural Network Technology Applied in Fault Detection and simulation of power systems 8

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

引用

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

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

ISBN: (纸本)9798331506230

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. © 2024 IEEE.

关键词： power distribution faults

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Nonlinear Optimal Control Method for DC/DC Converters in DC Microgrid Based on the Differential Geometry Theory 7

Nonlinear Optimal Control Method for DC/DC Converters in DC ...

引用

7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Guo, Tianyu Guo, Haiping Tu, Liang Huang, Libin Lu, Yuanhong 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 China Southern Power Grid Csg Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China

ISBN: (纸本)9798350334197

The DC/DC converter in the DC microgrid is the core device that supports the efficient grid connection of power electronic drive devices such as new energy generation units and energy storage devices. In view of this, this paper proposes a nonlinear optimal control method for DC/DC converters based on differential geometry theory, aiming at the nonlinear characteristics of the mathematical model of the converter. First, the topology structure of the DC microgrid is analyzed, and the mathematical model of the DC/DC converter is established;Secondly, the basic principle of power system nonlinear control based on differential geometry theory is discussed, and the nonlinear optimal controller for DC/DC converter is designed based on the accurate linearization method in differential geometry theory to quickly suppress power disturbance and realize the efficient grid connection application of power electronic drive equipment. Finally, the correctness and effectiveness of the proposed nonlinear optimal method were verified based on MATLAB/simulink. © 2023 IEEE.

关键词： DC-DC converters

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Research on Back-to-Back hvdc Additional Frequency Synchronous Control Strategy Applied to Asynchronous Interconnected systems

Research on Back-to-Back HVDC Additional Frequency Synchrono...

引用

2023 IEEE Sustainable power and Energy Conference, iSPEC 2023

作者： Chen, Qinlei Huang, Libin Li, Shuyong Lin, Xuehua Wu, Wencong Wu, Mingkang Tu, Liang Guo, Qi CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China State Key Laboratory of HVDC China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350322699

When regional power grid is asynchronized by high voltage direct current (hvdc) systems, the division of frequency regulation resources and reserve capacity weakens the system's ability to resist load fluctuation and fault disturbance. With the continuous promotion of electricity market construction in China, the demand of building a unified frequency regulation auxiliary market for the whole power grid is increasingly frequent. In this paper, with the objectives of improving the overall frequency regulation performance of asynchronous interconnected systems and refers to the power transmission characteristics of AC transmission lines. The additional frequency synchronous control (AFSC) strategy of back-to-back hvdc was studied, which enables back-to-back hvdc to automatically adjust the transmission power based on the frequency difference on both sides. Then, taking Luxi back-to-back hvdc of China Southern power Grid (csg) as an example, the specific implementation of AFSC strategy is studied, and the effectiveness of the proposed strategy is verified by real-time simulation. Finally, the on-site practical application of this strategy was analyzed. © 2023 IEEE.

关键词： additional frequency synchronous control (AFSC) Asynchronous interconnected systems back-to-back hvdc

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Application research of time-slot task synchronization method based on timestamp counter polling in electromagnetic transient real-time simulation 6

Application research of time-slot task synchronization metho...

引用

6th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2024

作者： Guo, Qi Lu, Yuanhong Guo, Tianyu Zhang, Jie Huang, Libin Tu, Liang Guo, Zhilong Xu, Dehuai Guo, Chuang 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 Dawning Network Technology Corp. Ltd. Wuhan China

ISBN: (纸本)9781510682924

In the practical application of electromagnetic transient real-time simulation computing platform, due to the large amount of calculation and long calculation time of electromagnetic transient simulation calculation, in order to improve the calculation speed and accuracy, it is usually necessary to use multiple CPU cores for parallel calculation at the same time, and ensure that each task synchronization and coordination among the computing cores. So, it is of great significance to select an appropriate task synchronization method for the realization of the electromagnetic transient real-time simulation computing platform. The paper puts forward a kind of based on TSC (Time Stamp Counter) Time Stamp Counter polling Time slot task synchronization method, this method supports high precision Time synchronization and coordination between different tasks, and can adapt to the transmission and efficient processing of large amounts of data. The test results prove that this method can avoid the overhead and delay of a large number of interrupts and thread switching, improve the efficiency, stability and predictability of task execution, and meet the performance requirements of the task synchronization method based on the centralized tightly coupled electromagnetic transient real-time simulation prototype. © 2024 SPIE.

关键词： Electromagnetic transients

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Preventive Control Methods for power systems Based on Improved Deep Reinforcement Learning 6

Preventive Control Methods for Power Systems Based on Improv...

引用

6th International Conference on power and Energy Technology, ICPET 2024

作者： Zhang, Jie Zhu, Yihua Liang, Zhuohang Zhang, Qingqing Ma, Qinfeng Liu, Mingshun An, Su Dai, Jiang 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 Power Dispatching and Control Center of Guizhou Power Grid Co. Ltd Guiyang China

ISBN: (纸本)9798350375855

Convergence speed, accuracy, generalization and decision transparency are very important indices for transient stability preventive control generation methods based on AI. This paper proposes a transient stability preventive control generation method based on graph deep reinforcement learning and its interpretation method using mutual information optimization. Firstly, a transient stability preventive control generation model is built based on the framework of deep reinforcement learning algorithm. The grid domain buses are mapped to the graph domain nodes and the grid graph data samples are generated. The graph convolutional neural network via initial residual and identity mapping (GCNII) is used to build the action network and the evaluation network. Secondly, based on the GCNII network, a transient stability assessment model is built and pre-trained, and graph data samples containing varies of topological structures and power flow states are applied to train the model. Then the interpretation method based on mutual information optimization is used to analyze the decision-making basis of the transient stability assessment model. The dominant factor affecting the transient instability of power system is extracted and its contribution degree is quantified, and the abduction analysis of transient stability is realized. Finally, the pre-trained transient stability assessment model is embedded in the deep reinforcement learning framework as a guide, and the power flow adjustment method is used to preferentially correct the weak factors and their influencing factors so that the transient stability prevention control strategies are obtained. The feasibility and effectiveness of the proposed method are verified in the IEEE-39 node test system. © 2024 IEEE.

关键词： Transient stability

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Distributed power system simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MADRL 4

Distributed Power System Simulation and Monitoring Supported...

引用

4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Luo, Hui Zhang, Jie Hu, Yufeng Zhang, Jingyue Guo, Haiping Huang, Libin Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid 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 National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Cloud platforms

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Application of deep reinforcement learning in power system scheduling strategy simulation 4

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

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4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

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

ISBN: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Deep reinforcement learning

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Design and Implementation of power system simulation Platform Based on Cloud Computing 4

Design and Implementation of Power System Simulation Platfor...

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4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue 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: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Cloud platforms

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