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6th IEEE Conference on Energy Internet and Energy system Integration, EI2 2022

作者： Ma, Qinfeng Zhu, Yihua Liu, Mingshun Chang, Dongxu Pu, Qingxin Li, Chengxiang Power Dispatching Control Center of Guizhou Power Grid Co. Ltd Guizhou China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute China Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (纸本)9798350347159

The stability control system is an important means to ensure the safe and stable operation of the power system. In view of the shortcomings of the existing offline/real-time simulation test methods, a closed-loop simulation scheme based on virtual component technology is proposed. The virtual component technology of the stability control device is researched, and the virtualization method of the external hardware interface of the device is designed. On this basis, a closed-loop simulation framework of the stability control system under the offline/real-time simulation test environment is proposed. The functional logic of each component under the framework are analyzed. The calculation and feedback process of the entire closed-loop simulation system are analyzed in depth, which effectively solves the difficulty of simulation analysis and test verification of large-scale stability control system, providing a new method for the research and verification of stability control system strategy. © 2022 IEEE.

关键词： Control system stability

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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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Fault Diagnosis for Small Samples Based on VMR

Fault Diagnosis for Small Samples Based on VMR

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Chinese Automation Congress (CAC)

作者： Xuejin Wu Yanghu Hu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis.

关键词： Fault diagnosis Training Neural networks Rolling bearings Feature extraction Data models Signal reconstruction Convolutional neural networks Long short term memory Overfitting

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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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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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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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A study on the fretting wear characteristic of Inconcel 718 alloys formed by selective laser melting(SLM)

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Wear 2025年 571卷

作者： Xu, Zhi-biao Lu, Hao-wen Zeng, Xin-yi Wang, Xiao-yi Wu, Hao Peng, Jin-fang Zhu, Min-hao College of Intelligent Manufacturing Guangzhou Maritime University Guangzhou510725 China School of Rail Transportation Wuyi University Jiangmen529020 China Guangdong Key Laboratory of Materials and Equipment in Harsh Marine Environment School of Naval Architecture and Ocean Engineering Guangzhou Maritime University Guangzhou510725 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu 610031 China Shandong Key Laboratory of Advanced Aluminum Materials and Technology Binzhou256606 China

The selective laser melting (SLM) technology has plays pivotal role in aerospace equipment, medical instrument, mold manufacturing and nuclear power industries due to its ability to rapidly fabricate intricate parts in recent *** methods like casting and forging often lead to Inconel 718 alloy parts with issues such as element segregation, coarse grain and structural simplicity. All such shortcomings are overcome by SLM technology, it's enabling to production Inconel 718 alloy parts with complex, fine grained and high density. Given the increasing reliance on SLM for creating Inconel 718 alloys, there is growing interest in the operational characteristics of these materials, including their mechanical, fatigue, and fretting wear properties. This study explores the fretting wear properties of Inconel 718 alloy produced by SLM, using an in-house designed fretting wear tester under various normal loads and displacement amplitudes. The coefficient of friction (COF) curve and friction force displacement (Ft-D) data during the fretting wear process were analyzed for a detailed dynamic responds. Additionally, the fretting wear damage and cross-section morphology were examined using a scanning electron microscope (SEM) and a three-dimensional profilometer (3D).The results show that the COF curve and Ft-D curve during the fretting wear process are greatly affected by the displacement amplitude and normal load. With the increase of normal load or the decrease of displacement amplitude, the fretting running state changes from the gross slip regime (GSR) to the mixed slip regime (MSR) and finally to the partial slip regime (PSR), and the most serious material damage occurs when the fretting state runs in the MSR. During the shift from the PSR to the MSR and the GSR, the fretting wear mechanism evolves from predominantly adhesive wear and abrasive wear to a dominance of fatigue wear and material delamination. The fretting wear damage of the material is influenced b

关键词： Fatigue damage

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A Solar-Blind Photodetector with Ultrahigh Rectification Ratio and Photoresponsivity Based on the Mote2/Ta:Β-Ga2o3 Pn Junction

SSRN

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SSRN 2023年

作者： Zeng, Guang Zhang, Meng-Ru Chen, Yu-Chang Li, Xiao-Xi Chen, Ding-Bo Shi, Cai-Yu Zhao, Xue-Feng Chen, Na Wang, Ting-Yun Zhang, David Wei Lu, Hong-Liang State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China Jia-Shan Fudan Institute Zhejiang Province Jiaxing314100 China State Key Laboratory of Integrated Chip and System Frontier Institute of Chip and System Fudan University Shanghai200433 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute Communication and Data Science Shanghai University Shanghai200444 China

High-performance solar-blind photodetectors have attracted increasing attention due to their important implications in military, medical, and industrial applications. Among a large number of wide-bandgap semiconductors, the quasi-two-dimensional β-Ga2O3 material is considered as a novel candidate for the solar-blind region (200–280 nm) owing to its suitable bandgap and superior optoelectronic properties. Herein, we report an outstanding solar-blind photodetector based on the MoTe2/Ta:β-Ga2O3 pn junction, which exhibits excellent optoelectrical properties with a superhigh rectification ratio of ~107, an ultrahigh responsivity (R ~358.9 A/W), a large detectivity (D* ~3.1 × 1012 Jones), an excellent external quantum efficiency (EQE ~1.8 × 105%), a fast photoresponse (21.1/84.5 ms), and a high rejection ratio (~1.1 × 103) under forward bias (1.5 V) and deep-ultraviolet (UV) illumination. These superior photoresponses result from the rapid separation of photoexcited electron-hole pairs driven by the synergistic effect of the forward bias and built-in electric field of the pn junction. Moreover, the MoTe2/Ta:β-Ga2O3 pn junction photodetector also demonstrates competitive optoelectronic characteristics in self-powered mode with a ultralow dark current of 8.5 fA and a fairly high R of 9.2 mA/W. As a result, this work provides an alternative solution for designing and fabricating high-performance solar-blind photodetectors with potential applications in UV imaging, environmental monitoring, and insecure communication. © 2023, The Authors. All rights reserved.

关键词： Military applications

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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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