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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yanzhen Zhou Qiang Liu Qinglai Guo Hongbin Sun Xianyu Zha Feng Xue Wenlu Zhao Chen Li Zhijian Zhang Liangliang Hao Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Smart Grid Protection and Control State Grid Electric Power Research Institute Jiangsu China State Grid Xinyuan Maintenance Branch State Grid Xinyuan Company Ltd. Beijing China State Grid Beijing Electric Power Dispatching and Control Center Beijing China School of Electrical Engineering Beijing Jiaotong University Beijing China

Machine learning methods have been considered as a promising method for online transient stability analysis. This paper presents a deep learning approach for transient stability prediction and unstable generator identification based on convolutional neural networks (CNNs). The post-disturbance dynamic responses of all the generators are utilized as the input features. A binary classification CNN model is generated for transient stability prediction. And a multi-class classification CNN model is built for unstable generator identification. Moreover, the multi-class CNN model can provide the unstable probability of each generator using the probability outputs and the label information. Comprehensive studies are conducted on the New England 10-machine 39-bus system to verify the effectiveness of the proposed approach.

关键词： Generators Power system stability Stability analysis Transient analysis Predictive models Thermal stability

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Call for papers of ZIJIN-2018 International Forum on smart grid protection and control

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Journal of Modern Power Systems and Clean Energy 2018年第3期6卷 619-619页

作者： state key laboratory of smart grid protection and control, SKL of SGPC State Key Laboratory of Smart Grid Protection and Control SKL of SGPC

It is a pleasure to invite experts and scholars from around the world to participate in the 3rd International Forum on smart grid protection and control, which will be held in nanjing, Jiangsu Province, China from Aug. 18th to 19th, 2018. The purposes of the forum are to strengthen the international academic exchanges in the field of smart grid protection.

关键词： Call for papers of ZIJIN-2018 International Forum on smart grid protection and control

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Reactive Power Coordination control Based on Vsc to Improve Lcc's Ability to Withstand Commutation Failure

SSRN

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SSRN 2023年

作者： Shu, Hongchun Shao, Zongxue Zhao, Wei State Key Laboratory of Collaborative Innovation Center for Smart Grid Fault Detection Protection and Control Jointly Kunming University of Science and Technology Kunming650500 China Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China

In order to suppress the continuous commutation failure of the line commutated converter (LCC) in the multi-infeed DC transmission system, the ability of the voltage commutated converter (VSC) in the hybrid multi-infeed DC transmission system to provide fast reactive power support is fully utilized, and a dynamic reactive power support coordination control strategy based on LCC current error controller and VSC constant AC voltage control is proposed. When the LCC experiences continuous commutation failure, the compensation amount of the extinction angle is closely related to the DC current error. The compensation amount of the extinction angle can be obtained according to the DC current error during the transient process. The reactive power support required by the LCC is calculated based on the compensated extinction angle, and the required reactive power support is prioritized to be added to the outer loop reactive power control of the VSC inverter side. By constructing a test system for doubly-fed DC transmission with flexible DC in PSCAD/EMTDC, the test results have confirmed the efficacy of the proposed dynamic reactive power coordination control strategy. The strategy has demonstrated a remarkable ability to enhance LCC's ability to suppress continuous commutation failure. © 2023, The Authors. All rights reserved.

关键词： Reactive power

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New Intelligent Sensing Terminal System Architecture of Internet of Things

New Intelligent Sensing Terminal System Architecture of Inte...

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International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2022

作者： Zhou, Lidan Li, Jing Yao, Gang Wang, Hongyu Li, Jian Liu, Siyang Nie, Yongjie School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai Minhang District200240 China Wiscom System Co. LTD. Nanjing China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China Electric Power Research Institute Yunnan Power Grid Co. Ltd. Yunnan Kunming650217 China Yunnan Key Laboratory of Green Energy Electric Power Measurement Digitalization Control and Protection Yunnan650217 China

ISBN: (纸本)9789819904150

In view of the fragmentation of the functions, forms and basic software and hardware of the existing IoT terminals, we propose an IoT intelligent sensing terminal system architecture, in which the intelligent sensing layer collects external data and information and transmits it to the computing fusion collaboration layer for data processing, fusion and task collaboration. The local application layer generates processing results and interacts with the cloud application layer through the communication layer. The new intelligent sensing terminal system of the IoT has an integrated local four-tier architecture, which can meet the needs of the large-scale development of the IoT. In addition, it can work uniformly under the control of the cloud, or complete the underlying tasks independently. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Distributed Optimization of Multi-Microgrid Integrated Energy System with Coordinated control of Energy Storage and Carbon Emission

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SSRN 2023年

作者： Shi, Linjun Cen, Zimeng Li, Yang Wu, Feng Lin, Keman Yang, Dongmei Lee, Kwang Y. College of Energy and Electrical engineering Hoahai University Jiangsu Nanjing211100 China State Key Laboratory of Smart Grid Protection and Control Nari Group Corporation Jiangsu Nanjing211106 China Department of Electrical & Computer Engineering Baylor University WacoTX76798 United States Yonsei Frontier Lab Yonsei University Seoul03722 Korea Republic of

The mutual optimization of multi-microgrid integrated energy system (MMIES) can effectively improve the overall economic and environmental benefits, which is drawing increasing interest in recent years. Aiming at the scenario where an MMIES is connected to the same node, the energy storage coordination control strategy and carbon emission management strategy are proposed, and an adaptive step-size method is applied to improve the distributed optimization of MMIES based on alternating direction multiplier method (ADMM). Firstly, the basic framework of MMIES containing cold, heat, electricity and gas energy is established, and a coordinated control strategy limiting the time of charge and discharge of energy storage system (ESS) is proposed. Then a multi-objective optimization model based on operating cost and environmental cost about CO2 emissions is formulated. Considering that different microgrids may be managed by different operators and different convergence speed of multi-objective optimization iteration, an adaptive step-size distributed iterative optimization method based on ADMM is used, which can effectively reduce the cost and protect the privacy of each microgrid. Finally, a system composed of three microgrids is taken as an example for simulation analysis. The results of distributed optimization are consistent with those of centralized optimization, and the proposed coordinated control strategy can effectively control the charge and discharge times of ESS, which verifies the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Carbon

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Remaining Useful Life Prognosis of Power Semiconductor Devices Based on On-state Resistance Time Series Data and Deep Learning

Remaining Useful Life Prognosis of Power Semiconductor Devic...

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2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Xiaolong Zhang Lin Zheng Heng Tian Li Liu Qun Ma Huafeng Wang Chong Gao State Key Laboratory of Advanced Power Transmission Technology State Grid Smart Grid Research Institute Co. Ltd. Beijing China Beijing Kedong Power Control System Co. Ltd. Beijing China State Grid Electric Power Research Institute Co. Ltd. Nanjing China State Grid Zhoushan power supply company Zhoushan China

Power semiconductor devices are the essential core components of modern power electronic equipment. Predicting the remaining useful life (RUL) of power semiconductor devices can help formulate reasonable maintenance strategies for power electronic equipment, thereby improving the availability of energy conversion systems. This article proposes a deep learning method for predicting the RUL of power semiconductor devices, which uses on-state resistance as a critical state parameter. A long short-term memory (LSTM) neural network (NN) model and a convolutional neural network (CNN) model are used to predict the on-state resistance time series data trend. NN models can predict a short section of future data with past data input. The RUL of power semiconductor devices is estimated through curve fitting based on past and predictive data. The proposed method is validated using the power MOSFET accelerated aging dataset from NASA, and the validation results show that the performance of the proposed method is superior to that of existing methods, such as extended Kalman filtering (EKF) and particle filtering (PF).

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Design and Implementation of Lightweight Manmachine Terminal Screen Interactive Client

Design and Implementation of Lightweight Manmachine Terminal...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Zhimeng Lv Lingyan Que Caishen Fang Xingzhi Wang Yuan He Kun Huang NARI Group Corporation (State Grid Electric Power Research Institute) NARI Technology Co. Ltd. Nanjing China State Grid Zhejiang Electric Power Co. Ltd. No.8 of Huanglong Road Xihu District Hangzhou China East-China Branch of State Grid Co. Ltd. No. 201 Nanjing East Road Huangpu District Shanghai NARI Group Corporation (State Grid Electric Power Research Institute) (National Key Laboratory of smart grid protection and operation control) NARI Technology Co. Ltd. Nanjing China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

According to the characteristics of the “physical distribution and logical integration” of the new generation dispatching automation system, this paper designs a lightweight human-machine terminal display architecture. It also proposes the componentization of the screen and its rendering technology to realize the display of electrical primitives, chart primitives, and non-viewable primitives on lightweight man-machine terminals. Besides, this paper proposes the GIS visual display technology to meet the needs of geographic information display in the dispatch automation system and put forward the interaction technology of picture linkage to improve the interaction flexibility of picture components. Relevant research results have developed prototype system and applied in the field pilot.

关键词： Couplings Visualization Automation Prototypes Rendering (computer graphics) Man-machine systems Geographic information systems

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Analysis on Differential protection Sensitivity of Traction Transformers Based on Different Conversion Modes

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IOP Conference Series: Earth and Environmental Science 2020年第1期617卷

作者： Liu Xiaobao Zheng Yuping Long feng Wu Tonghua Pan Shuyan An Lin State Key Laboratory of Smart Grid Protection and Control Nari Group Corporation Nanjing Jiangsu 211106 China

With the rapid development of high-speed rail technology in China, the traction transformers of various types have been used more and more extensively. The differential protection of traction transformers has different differential conversion modes [1-2] which are different in their sensitivities. In this paper, taking the YNV traction transformer as an example, by comparing the sensitivities of two different common differential conversion modes and through analysis, it shows that the sensitivities of differential protection are slightly different. The sensitivities of differential protections based on different principles depend on the retention degree of differential current to the positive sequence, negative sequence and zero sequence components in the internal fault current. The sensitivity of phase current minus zero sequence differential is the highest when single-phase grounded, which completely retains all the sequence components in the internal fault current. The sensitivity of interphase differential protection is the highest when interphase fault occurs. The widely used phase current differential and phase current minus zero sequence differential do not reflect the zero-sequence component of internal fault current, which provides the basis for field fault analysis and setting.

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Deep Integration and Learning of Measurement Data in Active Distribution Power Networks for Load Flow Estimation Considering Incomplete Information

Deep Integration and Learning of Measurement Data in Active ...

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China International Electrical and Energy Conference (CIEEC)

作者： Ruisheng Diao Aihua Zhou Ruiyuan Zeng Anqi Wang Xiaofeng Shen Jingde Shun Hua Gu State Key Laboratory of Clean Energy Utilization ZJU-UIUC Institute Zhejiang University Haining Zhejiang China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing Jiangsu China ZJU-UIUC Institute Zhejiang University Haining China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai China

To reach the goals of carbon peaking by 2030 and carbon neutrality by 2060 in China, a growing penetration of PV generation is integrated into the modern distribution power grid, causing challenges in operating the grid to meet security requirements due to the increased dynamics and stochastics. A deep neural network (DNN)-based method is proposed in this paper for estimating load flow solutions in active distribution power networks that typically suffer from incomplete measurements. Adaptive DNN models are trained from massive historical observations obtained from actual measurements or high-fidelity simulations, whose hyperparameters are tuned automatically. Comprehensive case studies are conducted on the IEEE 123-node feeder model with renewable generation considering various levels of missing information as inputs, which validate the effectiveness and robustness of the proposed method.

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An Intelligent Multi-Source Data Clustering and Analysis Method for AC/DC Hybrid Power grid Planning with Increased Complexity

An Intelligent Multi-Source Data Clustering and Analysis Met...

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China International Electrical and Energy Conference (CIEEC)

作者： Tu Lan Aihua Zhou Ruisheng Diao Fangyuan Sun Ruiyuan Zeng Xiaofeng Shen Chenhong Huang Zhen Zheng ZJU-UIUC Institute Zhejiang University Haining Zhejiang China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing Jiangsu China State Key Laboratory of Clean Energy Utilization ZJU-UIUC Institute Zhejiang University Haining Zhejiang China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai China

In order to better analyze the response characteristics of an AC/DC hybrid power grid with increased complexity following various types of disturbances, this paper proposes an intelligent method for clustering dynamic responses of multi-source data obtained from actual measurements or high-fidelity simulators. Base cases for AC/DC hybrid power grid planning students are created first, and a list of high-impact contingencies is searched using a graph algorithm. Then, the electromechanical-electromagnetic hybrid simulation method is adopted to create multi-source simulation data in terms of dynamic voltage, frequency, etc. An unsupervised learning algorithm and cluster analysis are then conducted to extract key features of dynamic response characteristics of the AC/DC hybrid power grid and create specific cluster labels. Advanced analysis, e.g., fast evaluation of risks from multi-source steady-state data, can then be enabled for further analysis. The proposed approach's effectiveness is verified on the actual AC/DC power grid model in East China.

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