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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Ran Ding Rongfu Sun Yan Geng Yiming Yao Ergang Zhao Lina Xu State Grid Jibei Electric Power Company Limited Beijing China Dept. of Electrical Engineering State Key Lab. of Control and Simulation of Power System and Generation Equipment Tsinghua University Beijing China

The interaction between various energy supply systems (cold, heat, electricity, gas, etc.) is becoming more tightly coupled as social economy and technology advance. Multi-energy systems are ineffective because different types of energy systems run independently. In order to protect the data privacy of each sub-system, this research suggests an ideal resource management framework for a multi-energy group control system. The energy hub (EH) paradigm is first suggested. When deploying EHs, the physical constraints of various energy systems are taken into account. Through scenario-based stochastic programming, the uncertainties of renewable energy are considered. According to the leader-follower relationship between multi-energy group control system operators and energy hub operators (EHOs), Benders decomposition is applied to decentralize the decision-making process. Finally, an example is given to illustrate the effectiveness of the proposed model and algorithm.

关键词： Data privacy Renewable energy sources Uncertainty Decision making Stochastic processes Process control Programming

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Network-Constrained Unit Commitment with Flexible Temporal Resolution

arXiv

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arXiv 2024年

作者： Yu, Zekuan Zhong, Haiwang Ruan, Guangchun Yan, Xinfei The State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China The Sichuan Energy Internet Research Institute Tsinghua University China The Lab for Information & Decision Systems Massachusetts Institute of Technology Massachusetts United States

Modern network-constrained unit commitment (NCUC) bears a heavy computational burden due to the ever-growing model scale. This situation becomes more challenging when detailed operational characteristics, complicated constraints, and multiple objectives are considered. We propose a novel simplification method to determine the flexible temporal resolution for acceleration and near-optimal solutions. The flexible temporal resolution is determined by analyzing the impact on generators in each adaptive time period with awareness of congestion effects. Additionally, multiple improvements are employed on the existing NCUC model compatible with flexible temporal resolution to reduce the number of integer variables while preserving the original features. A case study using the IEEE 118-bus and the Polish 2736-bus systems verifies that the proposed method achieves substantial acceleration with low cost variation and high accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy management

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Investigation on surface integrity in laser-assisted machining of Inconel 718 based on in-situ observation

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Procedia CIRP 2022年 108卷 129-134页

作者： Hang Zhang Rong Yan Ben Deng Jieyu Lin Minghui Yang Fangyu Peng National NC System Engineering Research Center School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China State Key Lab of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Inconel 718, a nickel-based superalloy, is widely used in the key parts of aero-engine owing to its high performance at elevated temperatures. Meanwhile, Inconel 718 is a typical difficult-to-cut material which is hard to obtain ideal surface integrity by conventional machining (CM). Laser-assisted machining (LAM) has a good potential to improve the machinability. In order to study the processing mechanism in the LAM of Inconel 718, the in-situ and ex-situ studies were presented in this paper. In the in-situ analysis, LAM and CM experiments were carried out based on in-situ observation and digital image correlation (DIC) technology. The Von Mises strain and equivalent plastic strain rate in the primary shear zone (PSZ) and cutting force were measured. According to the DIC results, under the experimental conditions of this paper, the average Von Mises strain and average equivalent plastic strain rate of PSZ in LAM increase by about 10% and 20% respectively due to the softening of Inconel 718 by laser heating, compared with CM. In-situ analysis shows that Inconel 718 in LAM is more prone to plastic flow, resulting in lower cutting forces. As for the ex-situ characterization, the surface roughness, residual stress and tool wear were measured. The measured results show that LAM can significantly reduce surface roughness and tool wear, and cause compressive residual stress on the machined surface.

关键词： Laser-assisted machining surface integrity in-situ observation Inconel 718

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A general end-to-end diagnosis framework for manufacturing systems

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National Science Review 2020年第2期7卷 418-429页

作者： Ye Yuan Guijun Ma Cheng Cheng Beitong Zhou Huan Zhao Hai-Tao Zhang Han Ding School of Artificial Intelligence and Automation MOE Key Lab of Intelligent Control and Image Processing Huazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven,end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts,indicating its potential use as a critical cornerstone in smart manufacturing.

关键词： manufacturing systems deep learning diagnosis and monitoring

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A Pricing Rule for Third-Party Platoon Coordination Service Provider

A Pricing Rule for Third-Party Platoon Coordination Service ...

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Asian control Conference

作者： Ting Bai Alexander Johansson Shaoyuan Li Karl Henrik Johansson Jonas Må rtensson Integrated Transport Research Lab Digital Futures and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

We model a platooning system including trucks and a third-party service provider that performs platoon coordination, distributes the platooning profit within platoons, and charges the trucks in exchange for its services. This paper studies one class of pricing rules, where the third-party service provider keeps part of the platooning profit each time a platoon is formed. Furthermore, we propose a platoon coordination solution based on distributed model predictive control in which the pricing rule is integrated. To evaluate the effect of the pricing on the platooning system, we perform a simulation over the Swedish road network. The simulation shows that the platooning rate and profit highly depend on the pricing. This suggests that pricing needs to be set carefully to obtain a satisfactory platooning system in the future.

关键词： Roads Pricing Predictive control

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A Novel All-phase Fast Fourier Transform-based Parameters Identification of High Frequency Oscillations

A Novel All-phase Fast Fourier Transform-based Parameters Id...

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Annual Meeting Industrial and Commercial Power systems Technical Conference

作者： Feiyu Sun Dongsheng Cai Zhenyuan Zhang Qi Huang College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang P.R. China

ISBN: (数字)9798350345308

ISBN: (纸本)9798350345315

Wind and solar energy inverter-based resources (IBRs) has been employed extensively with the aim of carbon neutrality. Power systems also make extensive use of power electronic converters, leading to interactions between IBRs and with the grid, causing high frequency oscillation (HFO) problems. It damages equipment and jeopardizes the security of the system. To identify HFO components above 100 Hz, this research use Phasor Measurement Unit (PMU) instantaneous value data to determine the HFO associated characteristics. To reduce the influence of spectral leakage and the fence effect, as well as to improve the identification accuracy of the related parameters, this paper adopts the RV self-convolutional window based Discrete Fourier Transform (DFT) and the Hanning double-window based all-phase Fast Fourier Transform (apFFT) hybrid algorithm to identify the HFO modal parameters. We take into account the identification accuracy in complex cases, such as non-nominal frequencies, with noise effects and multiple HFO modes. The simulation results show that the proposed method has high identification bandwidth, accuracy and strong flexibility.

关键词： Accuracy Wind energy Simulation Discrete Fourier transforms Noise Bandwidth Hafnium oxide

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Research on wafer surface defect pattern detection method based on incremental learning 3

Research on wafer surface defect pattern detection method ba...

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2021 3rd International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2021

作者： Yu, Naigong Li, Xin Xu, Qiao Jiang, Kai Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Lab of the Computational Intelligence and Intelligent System Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

Wafer manufacturing is an important step in quality control and analysis in the semiconductor industry. The defect pattern classification algorithm of wafer maps has received extensive attention from academia and industry. At present, most methods for detecting wafer surface defect patterns focus on static data model classification and analysis. However, in the production process, static data models cannot satisfy the dynamic analysis of wafer defect patterns in the form of streaming data. In this regard, this paper proposes a wafer surface defect pattern detection method based on incremental learning. Our experiment uses Resnet as the backbone network, and the data set uses the WM811K wafer data set. Experiments have proved that our method can achieve better classification accuracy in the field of wafer defect detection, which provides the possibility for continuous learning of wafer defects in the future. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Surface defects

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RHNet: A Real-Time High-Resolution Network for Surface Defect Detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

arXiv

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arXiv 2023年

作者： Wang, Qi Chen, Li Zhan, Zhiyuan Zhang, Jianhua Yin, Zhong The Department of Control Science and Engineering the School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The OsloMet Artificial Intelligence Lab Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway The Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website: https://***/7ohnKeats/SCVCNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Variable Frequency Phase-Shift Modulation Technique for Single Stage Dual-Active-Bridge AC-DC Converter

Variable Frequency Phase-Shift Modulation Technique for Sing...

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International Conference on Power Electronics (ICPE)

作者： Dongxin Guo Panbao Wang Chunguang Ren Josep M. Guerrero School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanxi Key Lab. of Power System Operation and Control Taiyuan University of Technology Taiyuan China Department of Energy Technology Aalborg University Aalborg Denmark

This article proposes a variable frequency phase-shift modulation technique for single stage dual-active-bridge (DAB) AC-DC converter. This isolated single-stage single-phase converter consists of a DAB and a full bridge (FB) inverter. The proposed strategy applies a composite control strategy combining frequency modulation and extended phase shift (EPS) method to overcome the drawback of the non-linear power control of traditional phase-shift control scheme, which significantly reduces the complexity of the controller algorithm. Moreover, the synchronous inverter control applied to the back-end FB inverter is designed, which can realize the energy conversions in a single-stage, and a high frequency (HF) filter capacitor can replace the electrolytic capacitor at the output side DC-link. Thus, the safety of system operation improves and the power density of the system increases. Finally, simulation results confirm the validity and practical feasibility of the proposed control strategy.

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