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A capacitor-type DC circuit breaker for offshore all-DC grids 4

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

引用

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

作者： Xu, Qianqian Liu, Qihui Cai, Xipeng Zhu, Yihua Luo, Chao North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China National Energy Power Grid Technology R&d Centre Laboratory of Hvdc 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 Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Offshore wind farms

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Influence Analysis and Countermeasure Research of Dynamic Characteristics of Measurement system on Bridge Arm Current Differential Protection in MMC-HVDC system 18

Influence Analysis and Countermeasure Research of Dynamic Ch...

引用

18th International Conference on AC and DC power Transmission, ACDC 2022

作者： Chen, Qinlei Guo, Qi Lin, Xuehua Huang, Libin Li, Shuyong 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)9781839537615

ISBN: (纸本)9781839537615

Aiming at the action phenomenon of bridge arm current differential protection after AC fault recovery of a modular multilevel converter high voltage direct current (MMC-HVDC) project, the characteristics of differential current are *** influence of temporary block strategy and dynamic characteristics of measurement system on differential current are studied, and the mechanism of differential current and the reasons for the action of bridge arm current differential protection are revealed. Based on the real-time digital physics closed-loop simulation platform, the action phenomenon of bridge arm current differential protection in the field is reproduced and the influence of the dynamic characteristics of the measurement system on the bridge arm current differential protection of the bridge arm is analyzed. The optimization measures are proposed from two aspects: the dynamic performance of the measurement system and the configuration of bridge arm current differential protection. The research results provide technical support for the design of MMC-HVDC project and the impact assessment of dynamic performance of measurement system on MMC-HVDC protection. © ACDC 2022. All rights reserved.

关键词： simulation platform

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Research on Automatic Construction and Correlation Method of Virtual and Real Loops in UHV DC control and Protection system

Research on Automatic Construction and Correlation Method of...

引用

2021 IEEE Sustainable power and energy Conference, iSPEC 2021

作者： Lu, Yuanhong Guo, Qi Liu, Zhijiang Li, Shuyong Guo, Haiping Zeng, Guanming China Southern Power Grid State Key Laboratory of Hvdc Electric Power Research Institute Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China Guangdong Prov. Key Laboratory O F Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China

ISBN: (纸本)9781665414395

At present, in the research on virtual and real loops of control and protection systems, scholars at home and abroad are mainly focusing on the field of AC digital substations, and there is less research on the virtual and real loops of UHV DC control and protection systems. Aiming at this problem, this paper proposes an automatic construction and correlation method for virtual and real loops of UHV DC control and protection system. This method automatically constructs the real circuit by analyzing the design drawings and the optical cable inventory, analyzing the DC visualization program to automatically construct the virtual circuit, and then automatically correlates the virtual and real circuits based on the graph depth traversal algorithm. The practical application in UHV DC engineering shows that this method can help relevant personnel complete the automatic construction and correlation of virtual and real loops, and improve the convenience of operation and maintenance of the UHV DC control and protection system. © 2021 IEEE.

关键词： Correlation methods

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power Output Monitoring and Anomaly Identification of Photovoltaic systems Using Graphical Modeling 4

Power Output Monitoring and Anomaly Identification of Photov...

引用

4th International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2020

作者： Chen, Yonghua Zhang, Xiaochen Liu, Gang Wu, Rui Liu, Chao Jiang, Dongxiang Nari Technology Co. Ltd Nanjing211106 China Tsinghua University Department of Energy and Power Engineering Beijing100084 China Tsinghua University State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Energy and Power Engineering Beijing100084 China

ISBN: (纸本)9781728170503

Photovoltaic cells are being increasingly installed worldwide as a type of important renewable resource for power generation. Among the installed photovoltaic systems, data are collected from the control and monitoring system, and the anomaly detection researches are mostly based on the individual photovoltaic cells and the systematic behavior in a group of photovoltaic systems is not well studied. Based on the assumption that the photovoltaic cells installed in the same location are with very close solar radiation, the power output of each subsystem is considered in this work for monitoring the operating status of the subsystem and identifying the anomalous subsystem, where spatiotemporal pattern network, a probabilistic graphical modeling approach, is applied. The results show that the presented framework is able to detect the anomaly and locate the anomalous subsystem. © 2020 IEEE.

关键词： Condition monitoring

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Effect of Positive Temperature Coefficient Materials on the AC Breakdown Strength and Dielectric Properties of Epoxy Composites 4th

Effect of Positive Temperature Coefficient Materials on the ...

引用

4th International Symposium on Insulation and Discharge Computation for power Equipment, IDCOMPU 2023

作者： Teng, Chenyuan Li, Shuo Zhou, Yuanxiang Zhang, Ling Zhang, Yunxiao Huang, Meng College of Information Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819974122

In operation, the negative temperature coefficient (NTC) electrical resistivity characteristics of polymeric insulating materials causes DC electric field distortion within power equipment insulation and lead to the failure. The doping of positive temperature coefficient (PTC) ceramic particles can suppress the NTC characteristics of the composites, but the electrical properties under AC voltages are unrevealed. In this paper, epoxy composites (0–20 wt%) doped with polydopamine-coated PTC particles were prepared, whose AC breakdown strength and dielectric properties were tested. The results shows that the decrease of AC dielectric strength is smaller than that under DC voltages after the doping PTC particles, and the coating of polydopamine can enhance the AC dielectric strength. As the doping content increases, the dielectric constant and tanδ of the epoxy composites increases, and the maximum increase of dielectric loss power is about 2% of the loss power caused by the current-carrying conductor. The research provides a theoretical basis for the optimization of the insulating performance of the equipment under the AC-DC composite voltages. © Beijing Paike Culture Commu. Co., Ltd 2024.

关键词： Epoxy resins

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Stochastic power Flow Distributed Algorithm for Distribution Network Based on Scenario Equivalence Between Regions

Stochastic Power Flow Distributed Algorithm for Distribution...

引用

International Conference on power system technology (powerCON)

作者： Zhibin Jing Jiacheng Sun Qianpeng Hao Qiang Li Jie Ren Ye Zhang Hanjie Zhao Junhui Li Cuiping Li Inner Mongolia Power Company Limited Dispatch Control Branch Hohhot China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China

A distributed stochastic power flow algorithm for distribution network based on inter-region scenario equivalence is proposed for the problem that the power injection of each node needs to be simulated with a large number of samples in the distribution network stochastic power flow algorithm, thus causing low computational efficiency. Firstly, the distribution network is divided into a backbone region and several sub-regions according to its radial structure characteristics, and the sampled power samples are partitioned; secondly, based on the inter-region power flow coupling relationship, the inter-region power flow action is replaced with the effect on the boundary quantity; then, a scenario equivalence method is proposed to merge the power scenarios that cause the same boundary injected power and boundary voltage. Finally, a distributed algorithm for distribution network stochastic currents is designed, and each region can complete the distributed solution of distribution network stochastic currents through the exchange of boundary equivalence scenario information and internal calculation, and the accuracy and effectiveness of the proposed method in reducing time are verified in the IEEE123 node system calculation example.

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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...

引用

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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The Development of simulation Mirror system and its Application in HVDC 6

The Development of Simulation Mirror System and its Applicat...

引用

6th International Conference on HVDC, HVDC 2024

作者： Ou, Kaijian Wu, Wencong Cai, Haiqing State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre. Guangzhou China Guangdong Prov. 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)9798350387759

The testing of HVDC new control function and training of HVDC operation personnel require the integration of power system simulation and dispatching automation. This paper presents and builds AC&DC power system simulation mirror system, which is the closed-loop of real-time digital simulation and dispatching automation. Moreover, the applications of simulation mirror system on HVDC new control function testing and HVDC simulation training cabinet are also introduced in this paper. © 2024 IEEE.

关键词： automatic generation control (A GC) automatic HVDC control (ADC) dispatching automation high voltage direct curren t(HVD C) real-time digital simulator(RTDS)

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Technical Research on simulation and Predictive Analysis of Wind Farms in power systems 4

Technical Research on Simulation and Predictive Analysis of ...

引用

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

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

ISBN: (纸本)9798331518066

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. © 2024 IEEE.

关键词： Wind forecasting

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Real-time Optimal energy Management of Combined Cooling, Heating and power systems Based on Knowledge conlusion

Real-time Optimal Energy Management of Combined Cooling, Hea...

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China International Electrical and energy Conference (CIEEC)

作者： Chunjiang He Yijian Wang Yang Xu Yang Cui State Key Laboratory of Power Grid Security and Energy Conservation China Electric Power Research Institute Beijing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin

Combined Cooling, Heating and power(CCHP) system is an important way to improve the efficiency of energy utilization and wind power consumption. In this case, increasing the operational flexibility of CCHP systems can help increase renewable energy consumption and reduce carbon emissions. So, it is necessary to provide real-time optimal energy management(OEM) for CCHP systems. In this paper. Firstly, a new training method is proposed for the LightGBM to improve the accuracy of real-time wind power prediction. In addition, to solve the nonlinear OEM of CCHP in real-time, a novel deep reinforcement learning algorithm-Soft Actor-Critic, is used in this paper. By combining the mechanism of CCHP with the reward function through knowledge fusion, the non-convex models can be translated into Markov Decision Processes(MDP), and then according to the Bellman optimal equation, the real-time optimized operation of the CCHP system is realized. Finally, the effectiveness of the proposed policy is verified by the actual operation data of a CCHP system in Northwest China.

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