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Conference on Electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Yunxiao Zhang Yuanxiang Zhou Ling Zhang Chenyuan Teng Zhongliu Zhou Meng Huang State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China The Wind Solar Storage Division of State Key Laboratory of Power System and Generation Equipment School of Electrical Engineering Xinjiang University Urumqi China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Electrical tree is one of the major issues for insulation failure in high-voltage direct current (HVDC) cable. In this paper, electrical tree initiation and growth characteristics of silicone rubber (SIR) are studied by optical tree observation system under DC and AC voltage with varied temperature. Moreover, tree channels are pictured and analyzed by 3D fluorescence imaging technology. Results show that the average tree initiation voltage, whether DC or AC, decreases exponentially with increasing temperature. However, the initiation voltage under positive DC voltages is much higher than that under AC voltages. AC tree growth pattern could be roughly divided into two modes: bush-like growing pattern and pine-like growing pattern, while DC trees stop growing after voltage stabilizes. 3D fluorescence tests show that DC tree branches are sparser and the channels are thinner. Based on the trap theory, mechanism of DC electrical tree in SIR is clarified. It is believed that different charge transport dynamics are the main reason for the differences in electrical tree behavior under DC and AC voltages.

关键词： Three-dimensional displays Temperature Shape HVDC transmission Conferences Fluorescence Space charge

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Wide-Area Damping control Enhanced by Non-Invasive Data Verification Against False Data Injection Attacks

SSRN

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SSRN 2024年

作者： Sun, Zhenglong Long, Hao Li, Zewei Gao, Bo Wei, Xiaoqiang Belikov, Juri Levron, Yoash Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy State Grid Jiashan County Power Supply Company Jiaxing China State Grid Heilongjiang Electric Power Company Limited Heilongjiang China The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia Northeast Electric Power University China

The increasing interconnectivity of power systems and the integration of renewable energy sources into the grid have exacerbated the low-frequency oscillation problem, which directly impacts the stability of the power grid. The performance of wide-area damping control (WADC), which is an effective oscillation suppression technique , is susceptible to false data injection attacks (FDIA). This study proposes a non-intrusive data verification method using an adaptive variational autoencoder (AVAE) combined with a deep convolutional neural network (DCNN) to enhance WADC’s resistance to FDIA. The method integrates data dimensionality reduction of AVAE and reconstruction capabilities with DCNN’s feature extraction advantages. Itemploys adaptive thresholds to dynamically track grid data fluctuations, accurately distinguishing between normal and abnormal measurements and improving attack detection and performance discrimination. In addition, the study proposes an intelligent repair process based on latent representations for anomalous data in the repair module, using hierarchical clustering and Gaussian mixture models post-Bayesian inference to repair data. AVAE then reconstructs the repaired data, mitigating attack impacts. simulations on IEEE 39-Bus, 68-Bus, and 118-bus systems demonstrate that this method effectively detects network attacks and restores manipulated signals with minimal reconstruction errors, thus ensuring grid stability and reliability. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Research on Active Support control Strategy and its Performance of renewable energy power Station with Multiple energy Sources

Research on Active Support Control Strategy and its Performa...

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IEEE Conference on energy Internet and energy system Integration (EI2)

作者： Guanming Zeng Liang Tu Yihua Zhu Yun Hu Chao Luo Shengnan Li 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 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 CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Yunnan Power Grid Co. Ltd. Electric Power Research Institute Kunming Yunnan

The increasing integration of renewable energy reduces the inertia and frequency support capacity of the power system. The issue of frequency stability has become one of the core issues in the security and stability of power system and the efficient consumption of renewable energy nowadays. To improve the performance of renewable energy power station (REPS), based on the analysis of typical active support control logic and relative performance requirements of REPS with single energy source (SES), an active support control strategy and coordinated control logic of active power of REPS with multiple energy sources (MES) are proposed in this paper. The performance of proposed strategy is theoretically analyzed from the aspect of dominant energy source and the effect of the capacity of energy storage. The effectiveness of the proposed strategy and the accuracy of the theoretical analysis of performance are verified by realtime simulation.

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A capacitor-type DC circuit breaker for offshore all-DC grids

A capacitor-type DC circuit breaker for offshore all-DC grid...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Qianqian Xu Qihui Liu Xipeng Cai Yihua Zhu Chao Luo State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Laboratory of HVDC Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&D Centre Guangzhou China Laboratory of HVDC Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&D Centre Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Full DC of offshore wind farms is the future development trend of large-scale offshore wind farms, however, the probability of DC line faults is high, and the use of DC circuit breakers can realise the fault ride-through of offshore full DC wind farms, improve the safe operation of the system during faults, and is one of the mainstream solutions to realise DC fault isolation. In this paper, a capacitive DC circuit breaker suitable for offshore full DC grid is proposed, which can effectively suppress the fault current by introducing a current limiting device. The topology and working principle of this circuit breaker are analysed, and the circuit breaker fault handling process is theoretically analysed. Finally, for the single-end equivalent system using capacitor-type DC circuit breaker, a simulation model is established in Matlab/Simulink software for verification, and the simulation results show that the proposed circuit breaker has a better current limiting effect and has the ability to isolate faults quickly.

关键词： Thyristors Limiting Circuit breakers simulation Wind farms Software Topology Circuit faults Fault currents Switching circuits

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DC Overvoltage Analysis of GFM-controlled Wind Farm Connected via MMC-HVDC system Caused by Receiving-end AC Faults

DC Overvoltage Analysis of GFM-controlled Wind Farm Connecte...

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International Conference on power Electronics and Motion control (IPEMC)

作者： Siying Chen Min Cheng Yingbiao Li Jiawei Yu Yihua Zhu Mingkang Wu Jiabing Hu Jianan Yan State Key Laboratory of Advanced Electromagnetic Technology and School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Yunnan Power Grid China CSG Key Laboratory for Power System Simulation (Electric Power Research Institute China Southern Power Grid) State Key Laboratory of HVDC (Electric Power Research Institute China Southern Power Grid) National Energy Power Grid Technology R&D Centre Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

DC overvoltage occurs when short circuit fault exists at the receiving-end and is harmful to the safety of the system where wind farms are connected to the grid through high-voltage direct current (HVDC) transmission based on the modular multilevel converter (MMC). To address this issue, this paper first analyzed the mechanism of the DC overvoltage and innovatively proposed a strategy in which grid-forming-controlled wind farms are integrated into the AC grid through MMC-HVDC, whose sending-end applies grid-following control and constant DC voltage control. However, secondary DC overvoltage appears during the fault recovery stage and is mainly caused by the saturation of the PI controller of constant active power control. simulations are carried out on a test with MATLAB/Simulink, and results verify the efficacy of the proposed methods in suppressing DC overvoltage that appeared during receiving-end faults and their recovery stage.

关键词： PI control simulation power control Wind farms Robustness Wind turbines Safety

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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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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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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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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 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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