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24th International Middle East Power System Conference, MEPCON 2023

作者： Aboelmagd, Mohamed R. Selim, Ali Abdel-Akher, Mamdouh Aswan University Electrical Engineering Dept Aswan Egypt Almaaqal University Control and Computer Engineering Dept Basra Iraq Aswan University Faculty of Engineering Department of Electrical Engineering Aswan Egypt

ISBN: (纸本)9798350358469

Accurate forecasting in photovoltaic energy is one of the most essential integrated and influential events for managing the system of new and renewable energy sources in the power grid. The paper presents a practical approach using neural networks to predict photovoltaic energy generation. Applying neural networks in PV power generation forecasting is challenging due to unpredictable environmental changes such as solar radiation, temperature, humidity, wind speed, etc. Neural network methods are distinguished by achieving high performance in prediction compared to traditional mathematical models that have difficulties dealing with complex nonlinear relationships, resulting in substandard prediction. The proposed neural network-based forecasting model offers an effective way to improve the precision of estimating PV generation. It has a great deal of promise to enhance the integration and dependability of solar energy in the current electricity landscape because of its capacity to capture complex linkages in the data and adapt to changing circumstances. © 2023 IEEE.

关键词： Forecasting

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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Interpretation of ADRC in Q-Structure 6

Interpretation of ADRC in Q-Structure

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Xu, Jiapeng Chen, Xiang Tan, Ying Zhou, Kemin University of Windsor Department of Electrical and Computer Engineering Windsor Canada The University of Melbourne Department of Mechanical Engineering Melbourne Australia Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (纸本)9798350356618

This paper presents a control structure featuring an operator Q driven by the residual signal, which indicates the difference between the measurement output and the estimated output from an observer. The form of this observer is very general, as long as it can generate the estimated state or relevant information that will be incorporated into this control structure. The operator Q introduces an extra design freedom to address uncertainties, such that linear active disturbance rejection control (LADRC) can be interpreted as a special case in the Q-structure when a linear extended state observer is used. © 2024 IEEE.

关键词： Disturbance rejection

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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Science China(Information Sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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A Distributed Optimization Algorithm Considering Inequality Constraints and its Application in Economic Dispatch Problem 7

A Distributed Optimization Algorithm Considering Inequality ...

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7th International Conference on Power and Energy Applications, ICPEA 2024

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Liaoning Dalian116023 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada School of Electrical Engineering Dalian University of Technology Liaoning Dalian116023 China

ISBN: (纸本)9798350356113

The economic dispatch problem (EDP) is crucial in optimizing and controlling power systems. As modern power system become more complex, traditional centralized communication methods are becoming less reliable. Therefore, distributed optimization method that relie on communication between neighboring nodes without the need for a central node are proposed. Existing distributed optimization methods for economic dispatch problems mainly focus on equality constraints like supply-demand balance, while problems with global inequality constraints are rarely studied. To address this gap, we propose a distributed optimization algorithm based on the primal-dual method and gradient tracking technique that can handle both global equality and inequality constraints. We thoroughly analyze its convergence and successfully apply it to the EDP with emission constraints to demonstrate its effectiveness. © 2024 IEEE.

关键词： Optimization algorithms

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A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation 6

A Novel Random Pulse Width Modulation for PMSM Drive System ...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Kan, Jinpeng Meng, Yongqing Shi, Yixin Li, Fengjiao Chen, Wenjie Li, Changyi Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation. © 2024 IEEE.

关键词： Pulse width modulation

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A Novel Zero Common-Mode Voltage Modulation Strategy for Dual Three-Phase PWM Inverter Based on Unified Voltage Modulation 4

A Novel Zero Common-Mode Voltage Modulation Strategy for Dua...

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4th International Conference on Energy, Power and electrical engineering, EPEE 2024

作者： Kan, Jinpeng Meng, Yongqing Li, Changyi Chen, Wenjie Li, Fengjiao Shi, Yixin Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798331518066

Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operation of the system. This article proposes a zero common-mode voltage modulation strategy based on unified voltage modulation for a 60° Y-connected six-phase permanent magnet synchronous motor system driven by a dual three-phase inverter. The theoretical basis and zero common-mode implementation scheme of unified modulation are analyzed, and the effectiveness of the proposed zero common-mode voltage modulation strategy is finally verified through simulation. © 2024 IEEE.

关键词： Pulse width modulation

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