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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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research on Power Electronics Converter real-time Simulation Modeling Method Based on SILdP

Research on Power Electronics Converter Real-time Simulation...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Jin Liu Pengbo Han Zeqi Hong Hui Luo Siyang An Shujun Yao School of Electrical and Electronic Engineering North China Electric Power University Beijing China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid 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

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Generally, detailed model of power electronics converter is used in electromagnetic transient simulations. It will use large sources of hardware in FPGA and the speed of simulation is slow. This paper is focus on the modeling method of real-time simulation, and proposed a new model of converter which based on the semi-implicit latency decoupling and paralleling (SILdP) technology. According to the proposed model, the SILdP model of voltage source converter(VSC) is build up. Base on the comparative between the SILdP VSC model and the simulation result waveform of PSCAd model, give the verification that the accuracy and effectiveness of the proposed SILdP VSC model. Finally, the SILdP VSC model is finished the verification in the FPGA experiment platform.

关键词： Accuracy Computational modeling Simulation Voltage Mathematical models real-time systems Numerical models

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Large-scale non-real-time Simulation Platform for Power systems Based on Cloud Computing

Large-scale non-real-time Simulation Platform for Power Syst...

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

作者： Jingyue Zhang Yuanhong Lu Yufeng Hu Jie Zhang Haiping Guo Libin Huang 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 China Southern Power Grid Co. Ltd Guangzhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

The rapid evolution of smart grids has increased the complexity of power system structures and operational characteristics, presenting new challenges in system management and reliability. Traditional power system simulation tools struggle with non-real-time data processing and scalability due to limitations in their architectural designs. Moreover, existing cloud simulation platforms are constrained by closed hardware architectures and lack sufficient non-real-time capabilities. As a solution, we propose a novel large-scale non-real-time simulation platform for power systems based on cloud computing, designed with a modular architecture. By leveraging advanced technologies in cloud computing, parallel computing, and non-real-time simulation, our platform addresses critical needs for scalability, efficiency, and cost-effectiveness. We discuss how integrating these technologies enhances computational performance and supports complex simulation tasks, ultimately improving decision-making and operational efficiency in power system management.

关键词： Cloud computing Scalability Power system management decision making Computer architecture Parallel processing data processing Computational efficiency Smart grids reliability

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A real-time Critical-scenario-generation Framework for Testing Autonomous driving system

arXiv

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arXiv 2022年

作者： Xie, Yizhou dai, Kunpeng Zhang, Yong School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Laboratory for Automotive Electronic Control Technology Shanghai Intelligent and Connected Vehicle R&D Center Shanghai China

In order to find the most likely failure scenarios which may occur under certain given operation domain, critical-scenario-based test is supposed as an effective and widely used method, which gives suggestions for designers to improve the developing algorithm. However, for the state of art, critical-scenario generation approaches commonly utilize random-search or reinforcement learning methods to generate series of scenarios for a specific algorithm, which takes amounts of computing resource for testing a developing target that is always changing, and inapplicable for testing a real-time system. In this paper, we proposed a real-time critical-scenario-generation (rTCSG) framework to address the above challenges. In our framework, an aggressive-driving algorithm is proposed in controlling the virtual agent vehicles, a specially designed cost function is presented to guide scenarios to evolve towards critical conditions, and a self-adaptive coefficient iteration is designed that enable the approach to operate successfully in different conditions. With our proposed method, the critical-scenarios can be directly generated for the target under test which is a black-box system, and the real-time critical-scenario test can be brought into reality. The simulation results show that our approach is able to obtain more critical scenarios in most conditions than current methods, with a higher stability of success. For a real-time testing, our approach improves the efficiency around 16 times. © 2022, CC BY.

关键词： Testing

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A new optical concept for high duty cycle vertical alignment liquid crystal displays 57th

A new optical concept for high duty cycle vertical alignment...

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57th SId International Symposium, Seminar and Exhibition, display Week, 2020

作者： Wu, Zirong Chen, Congxin Zhao, Zuzheng Optoelectronics and Intelligent Control R&D Center of Tsinghua University in Shenzhen and Shenzhen AV-display Co. Ltd. Shenzhen China

Optical film compensated passive matrix vertically aligned liquid crystal displays (PMVA-LCds) become more and more popular in many applications such as, industral control, automotive, e-metering, medical, sports, smart home etc. because of its high contrast ratio and wide viewing angle. However, the current solution of PMVA-LCd has a shortcoming on its steepness of the transmission-voltage curve (T-V curve), which limits its driving duty cycle . Here we demonstrate a new development of a PMVA-LCd to achieve high duty cycle, up to 1/128, by electro-optical simulations and experiments. © 2020 SId.

关键词： Liquid crystal displays

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Unified Grid-Forming control for MMC-MTdC systems with renewable Power Integration

Unified Grid-Forming Control for MMC-MTDC Systems with Renew...

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HVdC (HVdC), International Conference on

作者： renxin Yang Haiqing Cai Mingzhang Su Haohan Gu Zhihao Chen Xu Cai Key Laboratory of Control of Power Transmission and Conversion Shanghai Jiao Tong University Shanghai China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre 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: (数字)9798350387759

ISBN: (纸本)9798350387766

Using modular multilevel converter-based multi-terminal high voltage direct current (MMC-MTdC) transmission to integrate large-scale renewable power into the main power grid is an important development trend in China. To cope with the stability issues under weak grid conditions, different grid-forming control strategies have been proposed for the MMC in MTdC transmission systems. These control strategies have different structures due to different control targets and are difficult to unify. To solve this problem, a novel grid-forming control structure is proposed in this paper. The MMC emulates the synchronous generator (SG), and the submodule capacitor voltage emulates the SG rotor speed. On this basis, by utilizing the modulation control degree freedom, a unified dC-side controller is proposed to realize different control targets. Finally, the simulation model of a 4-terminal MMC-MTdC transmission system with renewable power integration is established in the PSCAd/EMTdC. The effectiveness of the proposed strategy is verified by the simulation results.

关键词： renewable energy sources Simulation HVdC transmission Capacitors rotors Switches control systems Synchronous generators Stability analysis Grid forming

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