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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Huang, Xifang Zhu, Ling Li, Wei Li, Mingfei Quan, Xiangiun Wu, Zaijun Research Institute NARI/SGEPRI State Key Laboratory of Smart Grid Protection and Control Nari Group Corporation/State Grid Electric Power Nanjing China Southeast University Seu School of Electrical Engineering Nanjing China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

Due to its different characteristics from traditional synchronous generator, grid-forming converter (GFM-converter) affects the transient characteristics of the system when it is linked to the power grid. The mathematical model of the GFM-converter is built to describe its dynamic characteristics. Then, the transient process of the GFM-converter is analyzed qualitatively with the power-angle curve. The phase diagram method is used to study quantitatively and verify the validity of the above qualitative analysis. Finally, the influence of virtual inertia JGFM on the transient response is analyzed. In the case that equilibrium point exists after fault, decreasing virtual inertia improves the transient stability of the GFM-converter by increasing the damping ratio. When the equilibrium point does not exist after fault, the increase of JGFM reduces the virtual power angle at the time of fault clearing, and the power imbalance degree of the GFM-converter is reduced, thus its transient stability performance is improved. © 2022 IEEE.

关键词： Phase diagrams

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Comparison of Adaptive Virtual Impedance Coefficient to Suppress Power Oscillation in Multi-VSG Grids

Comparison of Adaptive Virtual Impedance Coefficient to Supp...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Jie Ji Xingshuo Li Shuye Ding Yizhi Chen School of Electrical and Automation Engineering Nanjing Normal University Nanjing China State Key Laboratory of Smart Grid Protection and Control Nari Group Corporation Nanjing China

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

The virtual synchronous generator(VSG) technology offers inertia support but also introduces active power oscillation, which are more serious when in multi-VSG. The previous studies proposed adaptive virtual impedance aimed at suppressing power oscillation in multi-VSG system. However, further research is still needed to thoroughly examine the adaptive virtual impedance coefficient. In this article, how the power oscillation of multi-VSG to be influenced by adaptive virtual impedance coefficient is investigated. Firstly, an analysis is conducted on the active power oscillation mechanism. Subsequently, the variation law of adaptive virtual impedance coefficient affecting active power oscillation is studied. Finally, The effects of different adaptive virtual impedance coefficient values is compared in simulations and experiment.

关键词： Adaptation models Adaptive systems Impedance Frequency synchronization Oscillators Frequency control

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An Feature Data Extraction Method Based on BPCA-SPCA

An Feature Data Extraction Method Based on BPCA-SPCA

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Xiangyu Xing Bingrong Yan Yan Su Lingyun Tang College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China Nari Group Corporation State Key Laboratory of Smart Grid Protection and Control Nanjing China

ISBN: (数字)9798350388855

ISBN: (纸本)9798350388862

With the increasing integration of systems and the rapid development of sensor monitoring technology, the types of monitoring data are more diverse and the number of samples are larger in flight test and operation than before. Due to the large volume and high complexity of monitoring data, the system fault diagnosis and prediction based on monitoring data is inefficient. In order to solve the problem of monitoring data redundancy, a feature extraction algorithm based on BPCA-SPCA is proposed. A system with high failure rate is selected as an example to extract the features of the system monitoring data and realize the verification of data sparseness. The experimental results show that based on BPCA-SPCA the principal components extracted monitoring data can effectively express the features of the original data, and the feature sparse vector can provide effective input information for subsequent fault diagnosis and indirectly improve the efficiency of fault diagnosis.

关键词： Fault diagnosis Redundancy Feature extraction Prediction algorithms Vectors Inference algorithms Sensors Complexity theory Data mining Monitoring

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Capacitance Current Compensation Method for Cable-Overhead Hybrid Transmission Lines 5th

Capacitance Current Compensation Method for Cable-Overhead H...

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International Top-Level Forum on Engineering Science and Technology Development Strategy and 5th PURPLE MOUNTAIN FORUM on smart Grid protection and control, PMF2020

作者： Yao, Gang Wu, Tonghua Zheng, Yuping Teng, Xianliang Hong, Feng Chen, Guozhou NARI Group Corporation Nanjing211106 China State Key Laboratory of Smart Grid Protection and Control Nanjing211106 China

ISBN: (纸本)9789811597459

At present, the capacitance current compensation technology for transmission line differential protection is based on uniform parameters of entire lines, so it cannot be adapted to cable-overhead hybrid transmission lines. In this paper, a steady-state compensation method based on segment parameters, a time-domain compensation method based on whole line equivalent parameters and a time-domain compensation method based on segment parameters are proposed, which can adapt to different application scenarios, such as hybrid lines with shunt reactance at the ends and junctions. A PSCAD/EMTDC simulation shows that when a hybrid transmission line is short, the time-domain compensation method based on whole line equivalent parameters is simple to implement, and it can effectively compensate the capacitance current of hybrid transmission lines. The time-domain compensation method based on segment parameters can accurately compensate the capacitance current of hybrid transmission lines, taking into account the reliability and sensitivity of differential protection. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cables

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A Flow-Guided Anomaly Detection Method For Substation Equipment in Changing Scenarios

A Flow-Guided Anomaly Detection Method For Substation Equipm...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Qingwei Wang, Jing Wang, Peng Zhou, Wenhao Li, Yingxuan Zhao, Chunhui State Key Laboratory of Smart Grid Protection and Control NAPJ Technology Development Company Limited Nanjing China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665426473

The anomaly detection for substation equipment is of great significance to maintain the safe operation of substations. With the development of deep learning, many anomaly detection methods based on deep convolutional neural networks (CNN) have been proposed. However, the challenge lies in the diversity of the environment and the variety of anomaly types in the substation. This paper proposes a Flow-Guided Anomaly Detection Method, which contains a data augmentation module and a flow-guided feature fusion module. The data augmentation module utilizes CycleGAN to transfer the style of the images to adapt to changing scenarios, such as different illumination and season conditions. The flow-guided feature fusion module extracts the optical flow between the test image and the template image by a pre-trained FlowNet, which represents the change information between the image pair and is universal for different anomaly types. Then the appearance information and change information of the image are aggregated to obtain the fused feature, which contains more semantic information. Finally, the fused feature is utilized to accurately detect a wider range of abnormal types. Experiments show that our proposed method has superior performance in anomaly detection for substation equipment and is more robust to changing scenarios. © 2021 IEEE

关键词： Anomaly detection

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Source-Load Joint Probability Prediction Based on Transformer Model 5

Source-Load Joint Probability Prediction Based on Transforme...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Hu, Pingzhao Shan, Xin Hu, Diangang Fu, Jiayu Wang, Chen Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing China Co. Ltd. State Grid Nari Technology Co. Ltd. State Key Laboratory of Smart Grid Protection and Operation Control Nanjing China State Grid Gansu Electric Power Co. Ltd. Gansu China

ISBN: (纸本)9781665499132

Renewable energy power generation prediction, including wind and photovoltaic power prediction, is usually performed separately from load prediction, while there are significant internal relationships between renewable energy power generation and load consumption. Therefore, exploring the relationship between source and load, and implementing sourceload joint prediction may improve the accuracy of source-load prediction simultaneously. This paper proposes a source-load joint probability prediction method, PCMDT (Probability-CNN-Multi-Decoder-Transformer), based on Transformer model. Firstly, we analyzed the possible aspects of the relationship between renewable energy and load. Then the CMDT(CNN-Multi-Decoder-Transformer) model was used to realize the point prediction of renewable energy power generation and load power. Next, the conditional quantile was obtained by optimizing the quantile loss function (Pinball Loss) to realize the joint probability prediction of source and load. Finally, the model was tested on a public state-level dataset. Comparing the results of source-load joint prediction with non-joint prediction, the proposed model can improve the accuracy of renewable energy and load prediction. © 2022 IEEE.

关键词： Forecasting

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A Flow-Appearance Fusion Defect Detection Method for Substation Equipment in Multi-Scene

A Flow-Appearance Fusion Defect Detection Method for Substat...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Qingwei Wang, Jing Wang, Peng Zhou, Wenhao Zhao, Chunhui State Key Laboratory of Smart Grid Protection and Control NAPJ Technology Development Company Limited Nanjing China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665426473

In the daily maintenance process of substation, the defect detection of substation equipment is a critical problem. This task is challenging considering of the variety of defects, the rarely sample of various defects and the complex environment. This paper presents a flow-appearance fusion defect detection method for substation equipment in multiple scenes, which contains a Siamese optical flow module and a defect location module. The Siamese optical flow module is a pretrained optical flow network, which is utilized to obtain forward and backward optical flow between the normal image and the detect image. The optical flow represents the common changing features of different types of defects in multiple scenes. The defect location module utilizes MI-darknet53 to realize the extraction and fusion of appearance and flow features. Then the extracted multiscale features are used to build the feature pyramid network (FPN), which combines semantic defect information in high level and geometric defect information in low level. Finally, FPN is utilized to predict object-level defect areas in three different scales. Experiments show that our proposed method has good reliability and superior performance in the defect detection for the dataset of multi-scene substation equipment. © 2021 IEEE

关键词： Optical flows

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Evaluation of Wind Power Acceptance Capacity Based on N-1 Static Voltage Stability Region

SSRN

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SSRN 2024年

作者： Shu, Hongchun He, Yefu Wang, Guangxue Shi, Botao Zhang, Hanxin Zaho, Zhixin State Key Laboratory Collaborative Innovation Center for Smart Grid Fault Detection Protection and Control Jointly Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Green Energy and Digital Power Measurement Control and Protection Faculty of Land Resource Engineering Kunming University of Science and Technology China

Wind power generation is random and volatile with significant seasonal characteristics. Large-scale grid-connected wind power has an impact on the static safety of the system, especially under N-1 fault conditions. Therefore, this paper proposes an evaluation method of wind power acceptance capacity based on N-1 static voltage stability region to assess the maximum wind power acceptance capacity of the system. To accurately describe the dynamic correlation and seasonal characteristics of wind power generation in adjacent wind farms, a wind power output model is established based on k-medoids clustering and dynamic Copula function. Then, the seasonal generated scene data replaces the original wind power data for acceptance capacity assessment. The N-1 static voltage stability region model for wind power access is established, and the characterization method of wind power access to the N-1 static voltage stability region is determined by targeting search, searching, and selecting the most serious N-1 state. Monte Carlo is utilized to simulate the mathematical model of wind power acceptance capacity evaluation under different seasons, and combined with the N-1 static voltage stability domain, the maximum wind power acceptance capacity curve of the system is obtained. To verify the effectiveness of the proposed method, the IEEE-30 system connected to wind power is taken as an example for simulation analysis. The results show that the proposed method can quickly evaluate the maximum wind power acceptance capacity in the N-1 state, providing a basis for the planning and operation of a new energy power system with practical significance. © 2024, The Authors. All rights reserved.

关键词： Power generation

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Fuzzy Adaptive PI Circulating Current Suppressing control for MMC-HVDC

Fuzzy Adaptive PI Circulating Current Suppressing Control fo...

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IEEE Information Technology, Networking, Electronic and Automation control Conference

作者： Wujie Chao Junwei Huang Chaoping Deng Liyu Dai State Grid Fujian Electric Power Research Institute Fuzhou China Fujian Key Laboratory of Protection and Operation Control for Smart Grid China

Recent years, modular multilevel converter (MMC) is one of the most promising topologies applied to high voltage direct current transmission (HVDC). In order to solve the circulating currents caused by structural defects of MMC, a fuzzy adaptive PI circulating current suppressing controller is proposed based on mathematical model of MMC and functioning fuzzy subset inference method: The fuzzy number is calculated according to the error of system state, error variation, membership function and fuzzy control rules; The center of gravity method is used for defuzzification, and finally the PI parameters are modified. Finally, the PSCAD/EMTDC simulation model with fuzzy adaptive PI controller is established. The results show that the fuzzy adaptive PI circulating current suppressing controller is effective for MMC-HVDC.

关键词： Adaptation models Multilevel converters PI control HVDC transmission High-voltage techniques Mathematical models Stability analysis

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Construction of Dynamic Knowledge Graph for Grid day-ahead Scheduling Optimization Decision 10

Construction of Dynamic Knowledge Graph for Grid day-ahead S...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Yan, Qing Ding, Maosheng Lu, Jixiang Lu, Jinjun Yang, Huibiao Xie, Feng Nanjing China Nari Technology Co. Ltd. Nanjing China State Key Laboratory of Smart Grid Protection and Control Nanjing China State Grid Ningxia Electric Power Co. Ltd. Ningxia China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

As a new generation technology representative of knowledge engineering, knowledge graph is a large-scale semantic network, which has natural advantages in extracting knowledge from multi-source heterogeneous information, complex and highly correlated knowledge representation and reasoning. Based on the analysis of the application status of the grid day-ahead scheduling optimization decision-making technology and the demand for improving the intelligent processing capability, the key technologies and methods for the application of knowledge graph in the grid day-ahead scheduling optimization decision-making are proposed. The key technologies, application ideas and process methods of knowledge graphs are analyzed from the aspects of the design of knowledge ontology framework in the field of day-to-day dispatching of power grids, high-quality phrase mining and entity recognition, relation extraction and knowledge fusion, and scene-based dynamic knowledge graph update. Finally, the application and challenges of knowledge graph in the field of grid day-ahead scheduling in the future are summarized and prospected. © 2022 IEEE.

关键词： Knowledge graph

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