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Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method

Chinese Journal of Mechanical Engineering 2024年第3期37卷 170-188页

作者： Yanan Wang Lin Chen Junjiang Liu Baijie Qiao Weifeng He Xuefeng Chen National Key Lab of Aerospace power system and Plasma Technology Xi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring *** l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased *** address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge *** mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity ***,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated *** effectiveness and performance of the proposed method are validated on an aircraft skin-like composite ***,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is *** turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization *** impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

关键词： Impact force identification Non-convex sparse regularization Alternating direction method of multipliers Proximal operators

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Parallel system Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active power Corrective Control

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CSEE Journal of power and Energy systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex system Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

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Infrared and Visible Image fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

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Computers, Materials & Continua 2024年第4期79卷 1441-1461页

作者： Chunming Wu Wukai Liu Xin Ma Key Laboratory of Modern power system Simulation and Control&Renewable Energy Technology School of Electrical EngineeringNortheast Electric Power UniversityJilin132012China School of Electrical Engineering Northeast Electric Power UniversityJilin132012China School of Aeronautical Engineering Jilin Institute of Chemical TechnologyJilin132022China

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

关键词： Image fusion Res2Net-Transformer infrared image visible image

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A modified PVDF-HFP/PMMA crosslinked co-polymer for high-performance all-solid-state lithium metal batteries

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Particuology 2024年第10期93卷 203-210页

作者： Sijia Wang Liang He Mengting Wang Xingtong Guo Xiangyun Qiu Shoudong Xu Petr Senin Ting Bian Tao Wei School of Energy and power Jiangsu University of Science and TechnologyZhenjiang212003China power&Energy Storage system Research Center College of Mechanical and Electrical EngineeringQingdao UniversityQingdao266071China College of Chemical Engineering and Technology Taiyuan University of TechnologyTaiyuan030024China Institute of Mechanics and power Engineering Ogarev Mordovia State UniversitySaransk430005Russia

For all-solid-state lithium batteries(ASSLBs),polymer-blended solid composite electrolytes(SCEs)have drawn wide interest owing to their significance in improving the interfacial solid-solid contacts and inhibiting the growth of lithium *** this work,SCEs based on PVDF-HFP/PMMA matrix containing MOFs(NH2-MIL-53(Al))and LiTFSI were designed and synthesized employing an easy solution casting *** synthesized samples were examined by XRD,SEM,EDS,and electrochemical *** was found that MPP-2 SCE not only has excellent ionic conductivity at 60℃ of 5.54×10^(−4) S cm^(−1),but also exhibits superior interfacial compatibility in Li||Li symmetric batteries,which can constantly cycle for about 800 h at 0.1 mA cm^(−2) with no *** assembled Li|MPP-2|LiFePO4 cell exhibited a first discharge specific capacity of up to 157.1 mAh g^(−1) at 60℃ and 0.2 *** work may help to further advance the progress of ASSLBs in the future.

关键词： NH_(2)-MIL-53(AI) All solid-state lithium batteries(ASSLBs) PMMA PVDF-HFP Solid composite electrolytes(SCEs)

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A New Method for the Aging Evaluation of Oil-Paper Insulation Using η-Butanol and Methanol

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CSEE Journal of power and Energy systems 2024年第2期10卷 717-726页

作者： Jianyi Wang Yuanxiang Zhou Jun Liu Zhaowei Wang Song Bai Jiayu Lu Department of High Voltage China Electric Power Research InstituteBeijing 100192China State Key Laboratory of power Grid Environmental Protection Beijing 100192China Electrical Engineering Department Tsinghua UniversityBeijing 100084China Solar Storage Division of State Key Lab of power system and Generation Equipment School of Electrical EngineeringXinjiang UniversityUrumqi 830047China State Grid Wuwei Electric power Supply Company Wuwei 733000China

To improve the accuracy and efficiency of the aging life prediction and assessment of transformer oil-paper insulation,and to make up for the deficiencies of traditional characterizers,such as 2-furfural,carbon monoxide,and carbon dioxide,a method for the simultaneous determination of methanol,ethanol,n−propanol,and η-butanol in oil with a single injection is established by headspace-gas chromatography-mass *** measured results show that the determination limits of the four alcohol characterizers can be controlled to 10μg/kg *** on this method,the change patterns of the above four alcohols with thermal aging time and degree of polymerization are obtained through thermal aging experimental ***,η-propanol,and η-butanol in oil indicate nearly linear correlations with thermal aging time and degree of polymerization,similar to that of *** analyzing 52 sets of measured data of 500 kV EHV transformers in operation,η-butanol is found to have excellent performance,and a new method to evaluate the aging state of oil-paper insulation employing η-butanol and methanol is proposed along with the aging attention value *** measured data of 500 kV EHV transformers in operation indicate that the combination of η-butanol and methanol as the preferred characterizers can effectively compensate for the shortcoming of traditional characterizers in the early stages of aging,and the feasibility of the method is *** possible pathways for the generation of η-butanol by cellulose cleavage during the aging of oil-paper are proposed from the chemical structure of cellulose.

关键词： Aging characterization determination method methanol η-butanol oil-paper insulation 500 kV transformers

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power Flow Model for Medium-voltage Distribution systems Considering Measurement and Structure Characteristics

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Journal of Modern power systems and Clean Energy 2024年第2期12卷 571-583页

作者： Zhengmei Lu Wei Yan Dezhi Huang Junjie Tang Ferdinanda Ponci Antonello Monti State Key Laboratory of power Transmission Equipment&system Security and New Technology Chongqing UniversityChongqing 400044China State Grid Zhejiang Yiwu power Supply Company State Grid Zhejiang Electric Power CompanyYiwu 322001China Institute for Automation of Complex power systems of the E.ON Energy Research Center at RWTH Aachen University Aachen52074Germany Digital Energy Fraunhofer FIT Aachen52072Germany IEEE

Medium-voltage distribution systems(MVDSs)mainly consist of a feeder head,lines,distribution transformers,and the equivalent load or power supply interfaced with the distribution *** information of such load or power supply can be measured via the three-wattmeter method(THM)and the two-wattmeter method(TWM).The measurements can be used to perform the control of the power supply and simulate the characteristics of the load,so the models of the load and the power supply need to consider the measurement *** research works on three-phase power flow(PF)just consider the measurement characteristics of ***,the PF equation of the bus measured via TWM is firstly *** on conventional measurements,an accurate and general model of the grounded and ungrounded slack bus is ***,the influence arising from the connection type and angle shift of distribution transformers on the admittance matrix is considered,and thus a general three-phase transformer model is summarized,which is applicable for all the transformers mentioned ***,Newton's method is adopted to solve the PF calculation,and the performance of the proposed PF model is demonstrated through designed tests.

关键词： Medium-voltage distribution system three-phase power flow calculation measurement characteristic slack bus modeling distribution transformer

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Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

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Energy Engineering 2024年第6期121卷 1671-1695页

作者： Guanfu Wang Yudie Sun Jinling Li Yu Jiang Chunhui Li Huanan Yu He Wang Shiqiang Li State Grid Liaoning Electric power Co. Ltd.Liaoyang Power Supply CompanyLiaoyang111000China Key Laboratory of Modern power system Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China Jilin Northeast Electric power University Science and Technology Development Co. Ltd.Jilin132012China

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network ***, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

关键词： Self-adaptive the uncertainty of sources and load deep reinforcement learning dynamic economic scheduling

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Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-scale Renewable Energy

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Journal of Modern power systems and Clean Energy 2024年第2期12卷 359-370页

作者： Jianlin Li Zhijin Fang Qian Wang Mengyuan Zhang Yaxin Li Weijun Zhang Beijing Future Technology Innovation Centre for Electrochemical Energy Storage system Integration North China University of Technology Beijing China State Grid Fujian Electric power Research Institute Fuzhou China

As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system *** effectively promote the efficiency and economics of energy storage,centralized shared energy storage(SES)station with multiple energy storage batteries is developed to enable energy trading among a group of *** this paper,we propose the optimal operation with dynamic partitioning strategy for the centralized SES station,considering the day-ahead demands of large-scale renewable energy power *** implement a multi-entity cooperative optimization operation model based on Nash bargaining *** model is decomposed into two subproblems:the operation profit maximization problem with energy trading and the leasing payment bargaining *** distributed alternating direction multiplier method(ADMM)is employed to address the subproblems *** reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities,enhances the actual utilization rate of energy storage,and increases the profits of each participating *** results confirm the practicality and effectiveness of the strategy.

关键词： Shared energy storage(SES) dynamic partitioning strategy optimal operation Nash bargaining theory actual utilization rate of energy storage

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Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

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Nuclear Science and Techniques 2024年第8期35卷 177-203页

作者： Li‑Zhan Hong He‑Lin Gong Hong‑Jun Ji Jia‑Liang Lu Han Li Qing Li Paris Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Science and Technology on Reactor system Design Technology Laboratory Nuclear Power Institute of ChinaChengdu 610041China

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy *** previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their *** study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these *** efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive *** methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational *** numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 ***,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management *** developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

关键词： Parameter identification State estimation Reactor operation digital twin Reduced order model Inverse problem

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Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

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Journal of Modern power systems and Clean Energy 2024年第3期12卷 803-818页

作者： Xiangjun Zeng Ming Yang Chen Feng Mingqiang Wang Lingqin Xia the College of Electrical Engineering&New Energy China Three Gorges UniversityYichang 443002China the Key Laboratory of power system Intelligent Dispatch and Control Shandong UniversityJinan 250061China

The operating conditions of wind turbines(WTs)in the same wind farm(WF)may share similarities due to their shared manufacturing process,control strategy,and operating ***,the similarities of WTs are seldom considered in WT anomaly detection,resulting in the disregard of useful *** paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA)data of multiple WTs in the same ***,a similarity assessment method based on a comparison of different observation time series is proposed,which objectively quantifies the similarities of WT operating ***,the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM)*** models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target ***,an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify *** effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

关键词： Anomaly detection information entropy long short-term memory similarity assessment wind farm wind turbines

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