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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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A modified lumped parameter model of distribution transformer winding

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Global Energy Interconnection 2020年第2期3卷 158-165页

作者： Qingqing Ding Yao Yao Bingqian Wang Jingwei Fu Wei Zhang Chao Zeng Xiaoping Li Stanimir Valtchev State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityHaidian DistrictBeijing100084P.R.China State Grid Hubei Electric Power Co. Research Institute(HBEPRI)Hongshan DistrictWuhan 430077P.R.China Flexible Transmission Engineering Department Nari-Relays Electric Company210003 NanjingP.R.China State Grid Hubei Electric Power Co. LtdHongshan District430077 WuhanP.R.China Department of Electrical and Computer Engineering Faculty of Science and TechnologyUNLPortugal

The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately,but it requires complex *** parameter model requires less calculations,but its applicable frequency range is not *** paper studies the amplitude-frequency characteristics of the lightning wave,compares the transformer modelling methods and finally proposes a modified lumped parameter model,based on the above *** proposed model minimizes the errors provoked by the lumped parameter approximation,and the hyperbolic functions of the distributed parameter *** this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage *** proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

关键词： Wide band frequency response Distributed parameter model Lumped parameter model Distribution transformer Lightning protection

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Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition

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Measurement 2025年 256卷

作者： Jingchao Zhang Shaoqun Zhang Yixue Dong Chen Li Guoqian Jiang Yingwei Li Xiaoli Li School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao 066004 China Intelligent Control System R&D Department Computer Application Technology Research Institute China Ordnance Industry Beijing 100089 China School of Electrical Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China Pazhou Lab Guangzhou 510330 China

Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.

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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

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Data-driven Optimal power Flow: A Physics-Informed Machine Learning Approach

arXiv

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arXiv 2020年

作者： Lei, Xingyu Yang, Zhifang Yu, Juan Zhao, Junbo Gao, Qian Yu, Hongxin State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400030 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States State Grid Chongqing Electric Power Company Electric Power Research Institute Chongqing401123 China

This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not require the time-consuming parameter tuning process compared with the deep learning algorithms. However, the direct application of SELM for OPF is not tractable due to the complicated relationship between the system operating status and the OPF solutions. To this end, a data-driven OPF regression framework is developed that decomposes the OPF model features into three stages. This not only reduces the learning complexity but also helps correct the learning bias. A sample pre-classification strategy based on active constraint identification is also developed to achieve enhanced feature attractions. Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives. It is also shown that the proposed method can be easily extended to address different test systems by adjusting only a few hyperparameters. Copyright © 2020, The Authors. All rights reserved.

关键词： Acoustic generators

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A power flow tracing based load curtailment technique

A power flow tracing based load curtailment technique

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Wu, Tao Hu, Bo Xie, Kaigui Tai, Heng-Ming Wang, Shuocheng Li, Bo State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing China Department of Electrical and Computer Engineering University of Tulsa Tulsa United States State Grid Chongqing Economic Research Institute Chongqing China

ISBN: (纸本)9781728135205

A reasonable and efficient load curtailment method enables to restore the system quickly as well as reduce the economic losses both for the power providers and customers when the electrical power system is under contingency. This paper proposes a power flow tracing based load curtailment technique. When the outage occurs, the proposed method determines the load curtailment of each bus according to the power flow tracing results. The technical requirements can be satisfied by just curtailing these loads. It takes the distribution in power transfers between units, transmission lines and loads into account. An extended incidence matrix based power flow tracing scheme is developed for efficient load curtailment. Results in the load curtailment and reliability analysis of the RBTS system demonstrate the correctness of the proposed method. © 2019 IEEE.

关键词： Reliability analysis

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Dual Grid Voltage Modulated Direct power Control of Grid-Connected Voltage Source Converter under Unbalanced Network Condition

Dual Grid Voltage Modulated Direct Power Control of Grid-Con...

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Gao, Shuning Zhao, Haoran Gui, Yonghao School of Electrical Engineering Shandong University Ji'nan250061 China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Ji'nan250061 China Automation and Control Section Department of Electronic Systems Aalborg University Aalborg Denmark

ISBN: (纸本)9781728135205

The Grid voltage Modulated Direct power Control (GVM-DPC) for Voltage Source Converter (VSC) is a recently proposed superior control strategies which features several advantages such as fast power reference tracking, simple structure, no need to use synchronizing techniques, strong robustness and so on. In this paper, an improved Dual Grid Voltage Modulated Direct power Control (D-GVM-DPC) strategies for unbalanced grid voltage condition is proposed and compared with regular voltage oriented dual PI control techniques. The idea of this improvement is to separately control the positive and negative sequence power components based on the original GVM-DPC controller. The simulation results shows that the proposed strategy is superior to the dual current control schemes been used with voltage oriented control strategy. © 2019 IEEE.

关键词： Electric power transmission networks

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Reviewing continual learning from the perspective of human-level intelligence

arXiv

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arXiv 2021年

作者： Chang, Yifan Li, Wenbo Peng, Jian Tang, Bo Kang, Yu Lei, Yinjie Gui, Yuanmiao Zhu, Qing Liu, Yu Li, Haifeng The State Key Laboratory of Fire Science Department of Automation and Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China The Department of Electrical and Computer Engineering Mississippi State University United States College of Electronics and Information Engineering Sichuan University Chengdu China Faculty of Geosciences and Environmental Engineering The Southwest Jiaotong University Chengdu China Institute of Remote Sensing and Geographic Information System Peking Universit Beijing China School of Geosciences and Info-Physics Central South University South Lushan Road Changsha410083 China

Humans continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection);ii) infer on new information continuously (information prospection:);iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence;ii) providing a detailed and extensive overview on CL topics;iii) presenting some novel ideas on the potential development of CL. © 2021, CC BY.

关键词： Petroleum reservoir evaluation

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Coupling between Automatic Generation Control and Automatic Voltage Regulator While Considering Generation Rate Constraint of Two Areas

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Journal of Physics: Conference Series 2021年第1期2022卷

作者： Omer Elmukhtar Jianhua Zhang Department of computer science and control engineering North China Electric Power University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

This paper describes the implementation of automatic generation control (AGC) and automatic voltage regulator (AVR) under generation rate constraint (GRC) and coupling between automatic generation control and automatic voltage regulator while considering generation rate constrain of two areas for improve and better performance characteristic of load frequency control (LFC) problem. our control aim has to normalize the area control error (ACE), frequency error and tie-line power error in spite of the presences of system uncertainties and external load disturbance, So that the required frequency and power interchange with adjacent structures are maintained in order to reduce the transient deviations and to provide zero steady state error in proper short time, the behavior of the planned is checked by MATLAB SIMULINK software.

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Opacity of Nondeterministic Transition systems: A (Bi)Simulation Relation Approach

Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

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作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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