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4th IEEE International Conference on Electrical Materials and Power Equipment, ICEMPE 2023

作者： Zhu, Xiaoqian Zhou, Yuanxiang Zhang, Yunxiao Chen, Ming Liu, Xuanhao Tsinghua University State Key Lab of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Beijing China Fuzhou University College of Electrical Engineering and Automation Fuzhou China Xiamen Power Supply Company State Grid Fujian Electric Power Company Xiamen China Xinjiang University The Wind Solar Storage Division State Key Laboratory of Control and Simulation of Power System and Generation Equipment School of Electrical Engineering Urumqi China

ISBN: (纸本)9798350334678

Enhancing the electrical performance of silicone rubber (SiR) for cable accessories is noteworthy as they are almost the weakest parts of the cable system. Two types of voltage stabilizers were grafted onto the surface of nano-zirconia (ZrO2) particles, and the grafted nanoparticles were added to the SiR. Measurements such as breakdown strength, and conductivity were carried out to evaluate the electrical performance of SiR nanocomposites. The electrical performance measurement results indicated that the breakdown strength of the nanocomposite SiR improved while the conductance current was reduced. The trap distribution showed that the improvement of the electrical performance of the nanocomposite SiR is mainly due to the decrease of the shallow trap density or the increase of the deep trap density. © 2023 IEEE.

关键词： Nanoparticles

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Enhanced Transient Stability Strategy for Grid-Forming Converter Based on Current Limiting

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CSEE Journal of Power and Energy systems 2025年第3期11卷 960-971页

作者： Zhenglong Sun Zhifeng He Naiyuan Liu Rui Zhang Bo Wang Guowei Cai Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China

With the sustained increase in the penetration rate of grid-forming (GFM) converters in power systems, problems such as overcurrent and transient instability during system faults have drawn significant attention. This paper proposes an enhanced dynamic dq-axis current-limiting strategy (ED-CLiS) for GFM converters, which improves transient stability across all stages before and after current limiting. First, a dynamic dq-axis current-limiting strategy (D-CLiS) is proposed. This strategy regulates the power angle to prevent it from going out of control during current limiting, which is achieved by controlling the $d$ - and $q$ -axis current reference values to reshape the ${P}-\delta^{\prime}$ curve. Additionally, a large reverse acceleration area can induce excessive angle $\delta^{\prime}$ swings, causing the D-CLiS to be triggered repeatedly and resulting in converter instability after the control exit, even without faults. A reference power control structure (RPCS) is proposed, which suppresses the phenomenon by adjusting the power reference value to control the converter's reverse acceleration area. Finally, the control strategy is verified through simulations.

关键词： Transient analysis Power system stability Grid forming Stability criteria Voltage control Circuit stability Limiting Mathematical models Circuit faults Synchronous generators

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Self-Supervised Learning with Min-Max Distance Approach for Few-Shot Steel Surface Defects Recognition 43

Self-Supervised Learning with Min-Max Distance Approach for ...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Tianlei Chen, Xinyao Li, Zeliang Zhou, Zhencai Zhou, Zhiheng Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Province Kejie Machinery Automation Company Technical Department Jiangmen China

ISBN: (纸本)9789887581581

Surface defects in steel are crucial for quality inspection in the manufacturing of industrial metal products. While deep learning methods have been extensively studied in this field, the lack of large-scale annotated defect samples imposes significant labeling pressure, making training fully supervised deep models challenging. In response to this issue, we employ self-supervised learning for few-shot recognition, effectively learning representations from limited unlabeled data and achieving defect recognition with a small amount of labeled samples. In particular, we construct Other encoder with multi-view information and Base encoder with single view information based on the distillation self-supervised learning framework through the multi-crops mechanism, and the Other encoders and Base encoders perform information share. Our main contribution is that, based on this framework, we propose a new regularization method to solve the problem of instances deviating from the center of self-supervised clustering by minimizing the maximum instance distance (MMD). In testing, fine-tuning the MMD pre-trained model with a small number of labeled data achieves superior recognition performance on the steel surface defect dataset NEU, significantly outperforming other testing methods. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Self-supervised learning

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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Data envelopment analysis to determine reliability targets for power distribution systems

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Applied Energy 2025年 394卷

作者： Moghimy Fam, Amin Safdarian, Amir Fotuhi-Firuzabad, Mahmud Lehtonen, Matti Pourakbari-Kasmaei, Mahdi Department of Electrical Engineering and Automation Aalto University Maarintie 8 Uusimaa Espoo02150 Finland Centre of Excellence in Power System Control and Management Electrical Engineering Department Sharif University of Technology Tehran Iran

Distribution system (DS) reliability has the greatest impact on customer service availability. Enhancing the reliability level of a DS is a costly task, thus, determining an efficient and economical target levels for reliability indices is of high importance. To achieve this goal, this paper proposes a comprehensive Data Envelopment Analysis (DEA) based method (DBM). DBM integrates the effect of technical factors in the target determination process for the level of reliability indices in addition to including the conventional average method as one of its steps. By introducing a reliability–cost curve, DBM becomes capable of setting reliability targets based on the practical effect of investment in the system. Furthermore, this feature enables the estimation of the additional investment required to meet the targets. The results indicate the superiority of the proposed method in comparison with the conventional average method from the economic aspect and show the value of integrating the technical factors into it. The determined reliability targets are at least 2.85 % more cost-efficient and technically sounder for the feeders with a high value of reliability. The proposed method, due to its general features, can be applied in the benchmarking of other engineering systems where similar units are being operated and a relation between the unit's inputs and outputs can be established. © 2025 The Authors

关键词： Data envelopment analysis

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Integrated Power and Hydrogen Trading in Multimicrogrid Coupled with Offsite Hydrogen Refueling Stations 6

Integrated Power and Hydrogen Trading in Multimicrogrid Coup...

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

作者： Li, Longyan Ning, Chao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350347159

This paper proposes a novel two-level electricity and hydrogen market framework for multi-microgrids (MMGs) coupled with offsite hydrogen refueling stations (HRSs), aiming to strengthen the synergy between electricity and hydrogen. The local hydrogen market among the MMG and HRSs is modeled by multi-leader multi-follower Stackelberg game theory, in which microgrids play the role of leaders to adjust hydrogen prices while HRSs participate as followers to determine their hydrogen purchase scheme. Moreover, to deal with economic and technical issues of the MMG in a holistic manner, Nash bargaining theory is used to model the electricity transaction among microgrids. A distributed algorithm is developed to solve such games, thus reducing the cost of information communication and the risk of privacy exposure. The case study demonstrates that this market mechanism guarantees benefits for all parties and boosts the independence of MMGs by reducing transactions with the main grid. © 2022 IEEE.

关键词： Game theory

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Model Predictive control Based on Amplitude Decaying Aggregation Strategy for Hydrogen Gas Turbine

Model Predictive Control Based on Amplitude Decaying Aggrega...

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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

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Mathematical Modeling of the Impact of a 5 Day Interim Relaxation at 21 Days in 42 days Lockdown Strategy on COVID-19 Transmission in India 10

Mathematical Modeling of the Impact of a 5 Day Interim Relax...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Immaculyne, Flavia Bakiyaambikapathy Krishnamurthy, Kamalanand De Britto, Lourduraj Benoi Cresent Schlumberger- Reda Production System Singapore Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Anna University Department of Instrumentation Engineering MIT Campus Chennai India Vector Control Research Centre Indian Council of Medical Research Puducherry India

ISBN: (纸本)9798350350951

As of the first week of May 2020, the COVID-19 pandemic is present in 212 nations globally. Many countries have enforced lockdown measures strategically to slow down the epidemic's spread, with the objective of relieving pressure on healthcare systems. Mathematical modelling serves as a useful tool for the prediction of the future numbers of the infected population and for analyzing the effects of various intervention strategies. In this work, a mathematical model based on ordinary differential equations has been developed for analyzing the impact of an interim relaxation of 5 days at 21 days in a 42 days lockdown period. Results of the simulation demonstrate that the relaxation of 5 days during the 42 days lockdown does not result in any adverse effects if the transmission rate remains the same. However, the model-based analysis suggests that an increase in the transmission rate by a factor of 1.2 times during the relaxation period due to the possible aggregation of the susceptible with the infected, it could shift the growth pattern of infected cases from sub-exponential to exponential. © 2024 IEEE.

关键词： COVID-19

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An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing 5

An Expansion-based Document Ranking Framework Incorporated w...

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5th International Conference on Intelligent Autonomous systems, ICoIAS 2022

作者： Qi, Yinqiao Xin, Jinghao Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15. © 2022 IEEE.

关键词： Semantics

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