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2024 Boao New power System International Forum - power System and New Energy Technology Innovation Forum, NPSIF 2024

作者： Zhang, Xiaotian Lang, Wangjie Zhao, Yiyang Gong, Chao Yantai Research Institute Harbin Engineering University Yantai China Research Center for Intelligent Equipment Zhejiang Lab Hangzhou China Guigang Power Supply Bureau CSG Guangxi Power Grid Co. Ltd. Guigang China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798331504557

Nowadays, electric drive systems are increasingly used in many industrial scenarios, especially in electric vehicles (EVs). Many permanent magnet synchronous motors (PMSMs) lack stable and accurate temperature monitoring and can only rely on expensive motor designs to ensure safe operation at high temperatures. Temperature monitoring using traditional thermal modeling methods requires workers to have strong expertise, such as the selection of a priori parameters. This paper proposes a transferable estimation method for the temperature of the permanent magnets (PMs) of the motor rotor. The domain adaptation module is added to the 1-D convolutional neural network (CNN) to realize the transfer function of the training model. Before the model pre-training stage, the generative adversarial network (GAN) technique is applied to data augmentation for improving the generalization ability of the pre-trained model. Since the training of the domain adaptation module does not require labeled data, the model can be migrated across multiple different motors and more accurately estimate the temperature of the PMs. The final experimental part verifies the accuracy of this method through the professional measured temperature data of the PMSM. The mean square errors of proposed method are under 0.51. © 2024 IEEE.

关键词： Mean square error

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research on EV charging load response management method based on short-term prediction results

Research on EV charging load response management method base...

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2024 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2024

作者： Pang, Songling Zhao, Yunan Ge, Ganheng Tang, Jinrui Peng, Yong Electric Research Institute Of Hainan Power Grid Co. Ltd Tropical Smart Grid Lab Haikou China Wuhan University Of Technology School Of Automation Wuhan China China Power Engineering Consulting Group Central Southern China Electric Power Design Institute Co. Ltd Wuhan China

ISBN: (纸本)9798331529277

An electrical vehicle (EV) charging load response management method based on short-term load forecasting results is proposed and discussed in this paper. Firstly, each user in the region makes an online reservation for their respective desired charging time and charging duration. And then, based on the signals submitted by all EVs in the region, the suggested charging start time for each EV is differentiated by considering the status of the EV's battery, the short-term forecasts of the charging load, and the response management objectives. After receiving the suggested time pushed by the system, each EV can voluntarily choose whether to accept the response management strategy or not. The proposed method fully coordinates the system optimization operation of the distribution network and EV charging behavior management. And the driver's participation is fully considered in the short-term EV charging load demand-side response process. The proposed method provides an important basic support for the system-level demand-side response regulation in the long-time scale. © 2024 IEEE.

关键词： Demand response

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Multi-stage Robust Implicit Decision Rule for Optimal Control Problem of Energy Storage System

Multi-stage Robust Implicit Decision Rule for Optimal Contro...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Jiexing Zhao Qiaozhu Zhai Yuzhou Zhou Xiaoyu Cao Jianchen Hu Fei Xue Xutao Li School of Automation Science and Engineering and the MOEKLINNS Lab Xi’an Jiaotong University Xi’an China Ningxia Electric Power Research Institute Ningxia China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

The optimal control problem including random variables is difficult to solve. Existing methods typically rely on defining explicit decision functions to make the problem tractable. However, in practical numerical testing, we observed that some strongly coupled constraints, such as energy storage level limits, will impose strict restrictions on these simplified decision functions, potentially leading to significantly suboptimal solutions. Motivated by these challenges, this paper proposes a multi-stage robust implicit decision rule for the scheduling problem of energy storage systems. The main idea is to find an explicitly feasible decision function space to guarantee the multi-stage operating feasibility. When random variables are observed, decisions are adaptively optimized within the feasible decision space by solving a straightforward mathematical programming. Explicit decision functions are not required, ultimately enhancing the feasibility and optimality of the stochastic optimization for energy storage systems. Numerical tests are implemented on a real-world microgrid, verifying the effectiveness of the proposed method.

关键词： Uncertainty Costs Optimal control Microgrids Aerospace electronics Random variables Optimization Energy storage Testing Mathematical programming

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Optimal Model of Hydrogen Production Coupled Sewage Treatment from Renewable Energy for Hydrogen and Oxygen Utilization 10

Optimal Model of Hydrogen Production Coupled Sewage Treatmen...

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10th IEEE International power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Qi, Sihang Hu, Kewei Wang, Zhan Ma, Ruodong Zhang, Yu Xiong, Binyu Wuhan University of Technology School of Automation Wuhan China Huazhong University of Science and Technology State Key Lab. of Adv. Electromagnetic Eng. and Technol. Sch. of Elec. and Electronic Engineering Wuhan China State Grid-Hubei-Electric Power Research Institute Wuhan China

ISBN: (纸本)9798350351330

To avoid the waste of oxygen, a byproduct of hydrogen production from electricity, this article proposes a new utilization scenario that combines renewable energy hydrogen production with sewage treatment, and supplies pure oxygen from hydrogen production to the aerobic aeration tank for sewage treatment. This scenario involves combining pure oxygen with air, achieving the optimal pure oxygen ratio through adjustment, and then being aerated by an aerator into an aerobic bioreactor. While achieving comprehensive utilization of hydrogen and oxygen, it greatly enhances the economy of renewable energy hydrogen production. A new application scenario for renewable energy hydrogen production is provided, and simulation results show that its daily operating cost is reduced by 67.5%. The energy consumption of the aerator is reduced by 28.5%, and the light rejection rate is reduced by 30%. © 2024 IEEE.

关键词： Oxygen

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Motion estimation method of metal particle defect in oil chamber of on-load tap changer 6

Motion estimation method of metal particle defect in oil cha...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Chang, Jiuqian Hu, Jie Zhou, Yingchao Shao, Mingxin Gao, Changsheng School of Electrical Engineering and Automation Hefei University of Technology Hefei China Integrated Electronic Systems Lab Co. Ltd. Jinan China State Grid East Inner Mongolia Electricity Power Compancy Limited Hohhot China China Electric Power Research Institute Co. Ltd. Beijing China Chaohu Power Supply Company Anhui Province Chaohu238005 China

ISBN: (纸本)9798350346671

The on-load tap changer is the core component of the transformer. During the operation of the on-load tap changer, the moving and fixed contact system of the switching mechanism collides and closes quickly after the release of the energy storage of the fast mechanism. This violent collision inevitably produces the scattering of metal particle defects. In order to study the influence of metal particles on insulation, it is necessary to carry out the simulation experiment of metal particle defect motion in the oil chamber of on-load tap changer. In order to carry out the experiment effectively, this paper proposes a method to estimate the motion state and rising height of the simulated motion of metal particle defect with the increase of oil flow. The size of metal particle defect and the velocity of oil flow can be used to estimate the rising motion state and maximum height, and reduce the experimental difficulty of the study of metal particle defect motion induced discharge in the oil tank of on-load tap changer. © 2023 IEEE.

关键词： Defects

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Data-Driven Output Containment Control of Heterogeneous Multiagent Systems: A Hierarchical Scheme 63

Data-Driven Output Containment Control of Heterogeneous Mult...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sader, Malika Li, Wenyu Yin, Yanhui Li, Zhongmei Huang, Dexian Liu, Zhongxin He, Xiao Shang, Chao Tsinghua University Department of Automation Beijing100084 China Nankai University Coll. of Artif. Intell. Tianjin Key Lab. of Interventional Brain-Comp. Interface and Intelligent Rehab. Tianjin300350 China North-East Electric Power University School of Automation Engineering Jilin132000 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Tsinghua University Department of Automation Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350316339

In this article, we propose a data-driven solution to the output containment control problem of multiagent systems characterized by heterogeneous and unknown dynamics. The proposed data-driven scheme is hierarchical, which includes a network layer and a physical layer, bypassing the traditional modeling exercise and eliminating the need for explicit state-space models. In the network layer, a fully distributed observer without knowing the dynamics of leaders is designed to generate a containment trajectory. A data-driven approach based on data sampled from an auxiliary system is developed to overcome the dependence on the explicit state-space models for design of control gains. This paves the way for deriving a datadriven solution to the regulator equation, which is derived by employing the data informativity condition and relevant data in the physical layer. The results demonstrate that the closedloop system is asymptotically stable, and the regulated output containment converges to zero. Compared to generic modelbased hierarchical schemes, the assumptions that the system matrices are completely known and homogeneous on system dynamics can be relaxed. © 2024 IEEE.

关键词： Hierarchical systems

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Transferable Neural Network Method Used for PMSM Permanent Magnet Temperature Estimation in Electrical Drive System

Transferable Neural Network Method Used for PMSM Permanent M...

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power System and New Energy Technology Innovation Forum (NPSIF), Boao New power System International Forum -

作者： Xiaotian Zhang Wangjie Lang Yiyang Zhao Chao Gong Yantai Research Institute Harbin Engineering University Yantai China Research Center for Intelligent Equipment Zhejiang Lab Hangzhou China Guigang Power Supply Bureau CSG Guangxi Power Grid Co. LTD. Guigang China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331504557

ISBN: (纸本)9798331504564

关键词： Temperature measurement Temperature sensors Adaptation models Temperature distribution Temperature dependence Accuracy Estimation Predictive models Sampling methods Data models

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Simultaneous Identification of Process Structure, Parameters and Time-delay Based on Non-negative Garrote

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International Journal of automation and computing 2020年第6期17卷 873-882页

作者： Jian-Guo Wang Qian-Ping Xiao Tiao Shen Shi-Wei Ma Wen-Tao Rao Yong-Jie Zhang School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation TechnologyShanghai 200072China Institute of Environment and Resources Baosteel Research InstituteShanghai 201900China

In practice,the model structure,parameters and time-delay of the actual process may vary ***,the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical *** view of the fact that variable selection procedure can ensure a compact model with robust input-output,relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure,parameters and time-delay,non-negative garrote(NNG)algorithm is introduced and applied to system identification and the corresponding procedures are *** application of NNG variable selection algorithm to the identification of single input single output(SISO)system,multiple input multiple output(MIN1O)system and Wood-Berry tower industry are *** identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square(OLS)*** derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure,parameters and time-delay with high precision.

关键词： Model structure model parameter time-delay non-negative garrote variable selection.

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research on EV charging load response management method based on short-term prediction results

Research on EV charging load response management method base...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Songling Pang Yunan Zhao Ganheng Ge Jinrui Tang Yong Peng Tropical Smart Grid Lab Electric Research Institute of Hainan Power Grid Co. Ltd Haikou China School of Automation Wuhan University of Technology Wuhan China Central Southern China Electric Power Design Institute Co. Ltd China Power Engineering Consulting Group Wuhan China

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

关键词： Load forecasting Electricity Transportation Tariffs Distribution networks Electrification Electric vehicle charging Batteries Optimization

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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