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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Shuaiqi Shen Chong Yu Kuan Zhang Xi Chen Huimin Chen Song Ci University of Nebraska-Lincoln Omaha NE USA Beijing Electric Power Research Institute State Grid Corporation of China State Key Laboratory for Safety Control and Simulation of Power Systems and Large Power Generation Equipment Tsinghua University Beijing P. R. China

With the upcoming next generation wireless network, vehicles are expected to be empowered by artificial intelligence (AI). By connecting vehicles and cloud server via wireless communication, federated learning (FL) allows vehicles to collaboratively train deep learning models to support intelligent services, such as autonomous driving. However, the large number of vehicles and increasing size of model parameters bring challenges to FL-empowered connected vehicles. Since communication bandwidth is insufficient to upload full-precision local models from numerous vehicles, model compression is usually conducted to reduce transmitted data size. Nevertheless, conventional model compression methods may not be practical for resource-constrained vehicles due to the increasing computational overhead for FL training. The overhead for downloading global model can also be omitted by existing methods since they are originally designed for centralized learning instead of FL. In this paper, we propose a ternary quantization based model compression method on communication-efficient FL for resource-constrained connected vehicles. Specifically, we firstly propose a ternary quantization based local model training algorithm that optimizes quantization factors and parameters simultaneously. Then, we design a communication-efficient FL approach that reduces overhead for both upstream and downstream communications. Finally, simulation results validate that the proposed method demands the lowest communication and computational overheads for FL training, while maintaining desired model accuracy compared to existing model compression methods.

关键词： Training Quantization (signal) Connected vehicles Computational modeling simulation Wireless networks Collaborative work

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Coordination and Optimization of Multi-controller Parameters in power system Based on a Sphere Artificial Immune Algorithm

Coordination and Optimization of Multi-controller Parameters...

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第40届中国控制会议

作者： Yu Miao Du Weijie Li Jinglin School of Mechanical-electronic and Automobile Engineering Beijing University of Civil Engineering and Architecture Beijing Engineering Research Center of Monitoring for Construction Safety State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua University

Interconnected power system is becoming more and more complex, in order to improve the performance of power system, the number and type of controllers are increasing. The interaction among controllers may be detrimental to the performance of power system, which would weak the performance of controllers. The purpose of this paper is to solve the problem of coordination control among controllers, firstly, a multi-controller parameter coordination model is given. Then, a spherical artificial immune algorithm is proposed to coordinate and optimize parameters among multi-damping controllers. This algorithm can reduce the computational complexity and optimize parameters globally. Finally, this algorithm is verified by the simulation of the four-machine two-area system. Compared with one similar method, simulation results show that this algorithm can effectively realize the coordination control among multi-damping controllers.

关键词： Sphere artificial immune system power system coordination control parameter optimization

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Two points are enough

arXiv

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arXiv 2024年

作者： Liu, Hao Zhao, Yanbin Zheng, Huarong Fan, Xiulin Deng, Zhihua Chen, Mengchi Wang, Xingkai Liu, Zhiyang Lu, Jianguo Chen, Jian State Key Laboratory of Fluid Power Components and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Ocean College Zhejiang University Hangzhou China State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Hangzhou China Nanyang Technological University Singapore School of Control Science and Engineering Zhejiang University Hangzhou China School of Materials Science and Engineering Tianjin University Tianjin China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

Prognosis and diagnosis play an important role in accelerating the development of lithium-ion batteries, as well as reliable and long-life operation. In this work, we answer an important question: What is the minimum amount of data required to extract features for accurate battery prognosis and diagnosis? Based on the first principle, we successfully extracted the best two-point feature (BTPF) for accurate battery prognosis and diagnosis using the fewest data points (only two) and the simplest feature selection method (Pearson correlation coefficient). The BTPF extraction method is tested on 820 cells from 6 open-source datasets (covering five different chemistry types, seven manufacturers, and three data types). It achieves comparable accuracy to state-of-the-art features in both prognosis and diagnosis tasks. This work challenges the cognition of existing studies on the difficulty of battery prognosis and diagnosis tasks, subverts the fixed pattern of establishing prognosis and diagnosis methods for complex dynamic systems through deliberate feature engineering, highlights the promise of data-driven methods for field battery prognosis and diagnosis applications, and provides a new benchmark for future studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Variable Selection Method Based on Partial Mutual Information and Its Application to NOx Emission Prediction 39

Variable Selection Method Based on Partial Mutual Informatio...

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39th Chinese control Conference, CCC 2020

作者： Tianmu, Q.I.N. Jinzhe, Zhang Mo, Y.O.U. Tingting, Yang North China Electric Power Research Institute Co. Ltd. Beijing100045 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing102206 China

ISBN: (纸本)9789881563903

Data-driven modeling methods are widely used in industrial processes as the foundation of control and optimization. The selection of optimal variable set plays an important role in model performance. In order to enhance the model prediction accuracy, a partial mutual information (PMI) method was proposed to select the optimal variable set. Benchmarks were used to validate the effectiveness of PMI method. Then, PMI method was applied to select main influencing factors of NOx emission of coal-fired boiler and the selection results were used as inputs of three different data-driven models. The comparison between the models with or without variable selection was made. The results showed that the PMI method enhanced the model prediction accuracy and avoided the over-fitting problem. © 2020 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Coal fired boilers

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Reliability Evaluation of Distribution Grid Considering Flexible Grid Connection of Distributed Photovoltaic

Reliability Evaluation of Distribution Grid Considering Flex...

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作者： Zecheng Li Yanying Sun Chen Feng State Grid Ningxia Electric Power Research Institute School of Mechanical Electronic and Information Engineering China University of Mining and Technology (Beijing) The Key Laboratory of Power System Intelligent Dispatch and Control Shandong University

Aiming at the problem that the flexible access of distributed photovoltaics to the distribution network affects its reliability,this paper establishes a model of the distributed photovoltaic output probability based on the typical output characteristics of distributed photovoltaics ***,the power grid risk assessment indicators are determined to facilitate the quantification of distribution network risks,including voltage over-limit,power over-limit,and load loss risk ***,a distribution network model containing distributed photovoltaic grid-connected is established on the MATLAB simulation platform,and the risk assessment of the distribution network containing distributed photovoltaic grid-connected is carried out.

关键词： distributed photovoltaic voltage limit power limit loss of load risk assessment

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Fast monte carlo simulation of dynamic power systems under continuous random disturbances

Fast monte carlo simulation of dynamic power systems under c...

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2020 IEEE power and Energy Society General Meeting, PESGM 2020

作者： Qiu, Yiwei Lin, Jin Chen, Xiaoshuang Liu, Feng Songy, Yonghua Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100084 China The University of Macau Department of Electrical and Computer Engineering 999078 China

ISBN: (纸本)9781728155081

Continuous-time random disturbances from the renewable generation pose a significant impact on power system dynamic behavior. In evaluating this impact, the disturbances must be considered as continuous-time random processes instead of random variables that do not vary with time to ensure accuracy. Monte Carlo simulation (MCs) is a nonintrusive method to evaluate such impact that can be performed on commercial power system simulation software and is easy for power utilities to use, but is computationally cumbersome. Fast samplings methods such as Latin hypercube sampling (LHS) have been introduced to speed up sampling random variables, but yet cannot be applied to sample continuous disturbances. To overcome this limitation, this paper proposes a fast MCs method that enables the LHS to speed up sampling continuous disturbances, which is based on the Itô process model of the disturbances and the approximation of the Itô process by functions of independent normal random variables. A case study of the IEEE 39-Bus system shows that the proposed method is 47.6 and 6.7 times faster to converge compared to the traditional MCs in evaluating the expectation and variance of the system dynamic response. © 2020 IEEE.

关键词： Random processes

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Accelerating exploitation and integration of global renewable energy

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Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

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Digital Watermarking-Based Detection against Small-Scale False Data Injection Attacks

Digital Watermarking-Based Detection against Small-Scale Fal...

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第32届中国过程控制会议(CPCC2021)

作者： Lei Yang Jing Wu Chengnian Long Shaoyuan Li Daojian Hong Qian Zhang Department of Automation Key Laboratory of System Control and Information ProcessingMinistry of EducationShanghai Jiao Tong University Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd

False data injection(FDI) attacks can compromise data integrity and mislead control center to make wrong decisions by injecting false data into correct measurements,which may threaten the safe operation and reliable control of the smart *** is very challenge to defense the FDI attack,specially when the attack is stealthy and *** this paper,we propose a digital watermarking-based detection scheme to detect stealth small-scale FDI *** extracting the integrity of the received data with wavelet transform,a sensitive and fragile watermarking is generated,which can encrypt the data in a certain *** analysis show that the proposed scheme can resist stealth small-scale FDI attacks and has little effect on the original measures data,which guarantee the integrity and availability of measurement.

关键词： Small-scale False data injection attack wavelet transform digital watermarking data integrity detection

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Analysis And Decision-Making Of Excess Renewable Energy Consumption Trading Between Electricity Retailers Based On Evolutionary Game

Analysis And Decision-Making Of Excess Renewable Energy Cons...

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

作者： Bo Liang Lei Xie Hanqi Li Shangwen Dai Yang Yang Guanting Chen State Grid Shandong Electric Power Company Marketing Service Center (Metrology Center) State Grid Shandong Electric Power Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University) Shandong University Jinan China

ISBN: (纸本)9781665434263

The excess renewable energy consumption trading is one of the supplementary trading ways to promote the fulfillment of the responsibility of renewable energy consumption in China. To aim at the problem of how the market participants to understand and accept the excess consumption trade, a transaction decision-making method based on evolutionary game theory is proposed in the paper. Next, the paper analyses the influence of different factors such as the incentive and punishment measures for renewable energy consumption, the green certificate price and the transaction price of excess consumption on the willingness and enthusiasm of electricity retailers to participate in excess consumption trading. Analysis and simulation results show that the appropriate deregulation of regulatory incentives, higher green certificates price and reasonable excess consumption trading price can promote the cooperation between the retailers, which is conducive to the realization of excess consumption trading.

关键词： Symbiosis Renewable energy sources Atmospheric measurements simulation Green products Energy measurement Games

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system power electronics

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