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Power Tech Conference

作者： Tianhao Liu Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

With the penetration of renewable energy sources (RES) increasing rapidly, cascading failures become more complex due to the uncertainty and vulnerability of RES. This paper presents an online cascading failure searching method based on gradient boosting decision tree (GBDT) for the hybrid AC/DC system with high penetration of wind power. First, the cascading failure risk index is established, which considers the impact, probability, and loss of failures. Then, a cascading failure searching based on Monte Carlo tree search and dynamic simulation is performed to acquire the high-risk cascading failures, which are utilized as the training samples. Finally, GBDT is deployed to construct the relationship between operating conditions and failure propagation directions. Online cascading failure searching is realized by combining GBDT failure estimation and dynamic failure simulation. Simulation results of the modified New England 39-bus test system demonstrate that the proposed method can fast and accurately online screen the high-risk cascading failures considering the uncertainty of wind power.

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High-performance chiral all-optical OR logic gate based on topological edge states of valley photonic crystal

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Chinese Physics B 2023年第7期32卷 392-398页

作者：王晓蓉费宏明林瀚武敏康丽娟张明达刘欣杨毅彪肖连团 College of Physics Taiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China School of Science RMIT UniversityMelbourneVictoria 3000Australia

For all-optical communication and information processing,it is necessary to develop all-optical logic gates based on photonic structures that can directly perform logic ***-optical logic gates have been demonstrated based on conventional waveguides and interferometry,as well as photonic crystal ***,any defects in those structures will introduce high scattering loss,which compromises the fidelity and contrast ratio of the information *** on the spin-valley locking effect that can achieve defect-immune unidirectional transmission of topological edge states in valley photonic crystals(VPCs),we propose a high-performance all-optical logic OR gate based on a VPC *** tuning the working bandwidth of the two input channels,we prevent interference between the two channels to achieve a stable and high-fidelity *** transmittance of both channels is higher than 0.8,and a high contrast ratio of 28.8 dB is ***,the chirality of the logic gate originated from the spin-valley locking effect allows using different circularly polarized light as inputs,representing“1”or“0”,which is highly desired in quantum *** device’s footprint is 18μm×12μm,allowing high-density on-chip *** addition,this design can be experimentally fabricated using current nanofabrication techniques and will have potential applications in optical communication,information processing,and quantum computing.

关键词： topological photonics topological edge state valley photonic crystal all-optical logic gate

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Identification of Transient Voltage Stability Weakness in Power systems Based on Grad-CAM 7

Identification of Transient Voltage Stability Weakness in Po...

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7th IEEE International Conference on Power and Renewable Energy, ICPRE 2022

作者： Qin, Changlong Jia, Yujian Li, Zhendong Li, Guang Li, Li Zhu, Yuanzhen State Grid Jinan Power Supply Company Jinan China Shandong University Key Laboratory of Power System Intelligent Dispatch Control of Ministry of Education Jinan China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

With the changes in generation and load characteristics, transient voltage problems have a serious impact on the safe and stable operation of the power system. In this paper, on the basis of constructing a fast evaluation model of transient voltage stability based on convolutional neural network, the key electrical quantities that the model focuses on in the evaluation process are pointed out by Grad-CAM algorithm, and the weakness of system voltage stability are identified. The simulation results show that the proposed method can explore the links that are neglected by traditional methods, and the identified weakness consider both topological and state information, which verifies the superiority of the method in this paper. © 2022 IEEE.

关键词： Convolution

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EV Charging Load Forecasting Method Considering Time-of-Use Price and Meteorological Factors 7

EV Charging Load Forecasting Method Considering Time-of-Use ...

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

作者： Li, Guangxiao Yang, Yilu Wang, Chunyi Xu, Ziyu Wang, Yue Liu, Yutian State Grid Jining Power Supply Company Development Planning Department Jining China State Grid Shandong Electric Power Company Development Planning Department Jinan China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798350356113

The randomness of load fluctuations at electric vehicle (EV) charging stations presents a significant challenge for the accurate load forecasting. Considering time-of-use (TOU) price and meteorological factors, EV charging load forecasting method for EV charging station is proposed. First, the factors affecting charging load are analyzed using the Pearson correlation coefficient and the maximum information coefficient (MIC). key factors such as TOU price, temperature, weekday type, and recent historical load data are determined. Next, convolutional neural network (CNN) is used to extract important features and capture complex nonlinear relationships from key factors. Finally, the extracted features are processed by gated recurrent unit (GRU) network to forecast charging load at different time scales. Case study is conducted using load data from EV charging station with a capacity of 1800 kW. The results demonstrate that the CNN-GRU model significantly improves forecasting accuracy at 4h, 24h, 72h, and 168h forecasting lengths compared to other models. © 2024 IEEE.

关键词： Charging stations

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Embedding P2p Transaction into Demand Response Exchange: A Cooperative Demand Response Management Framework for Ies

SSRN

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SSRN 2024年

作者： Wang, Kang Wang, Chengfu Dong, Xiaoming Yao, Wenliang Zhang, Zhenwei Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Shandong Province Jinan250061 China Shandong University China

The efficient utilization of integrated demand response (IDR) is substantially hindered by the centralized pattern of IDR management, which constrains the potential of IDR to enhance system flexibility and maintain the balance between power supply and demand. In this pattern, the integrated energy operator (IEO) acquires IDR resources from integrated load aggregators (ILAs), while the demand response transactions among ILAs, treated as a unique commodity, lack adequate attention. Therefore, it is imperative to utilize IDR resources in decentralized manner and facilitate the synergy between IEO and ILAs. To this end, a cooperative demand response management (DRM) framework for integrated energy system (IES) is proposed. Firstly, peer-to-peer (P2P) transaction is integrated with demand response exchange (DRX) mechanism to design decentralized market for DR transaction. Meanwhile, Nash bargaining theory and alternating direction method of multipliers (ADMM) algorithm are applied to achieve the optimal allocation of interests and preservation of privacy, respectively. Secondly, the price elasticity matrix of gas-electricity, which depicts cross-price elasticity of natural gas demand with respect to electricity in retail market and vice versa, is introduced to furtherly exploit the flexibility of the demand side resource. Thirdly, the cooperative DRM framework is established, where the P2P market synergizes with the traditional retail market to foster the efficient utilization of IDR resources. Case study demonstrate that the proposed DRM framework effectively facilitates a win-win situation between the ILAs and IEO to harness the potential of IDR. © 2024, The Authors. All rights reserved.

关键词： Electric loads

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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Multi-Human Pose Estimation by Deep Learning-Based Sequential Approach for Human keypoint Position and Human Body Detection

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Journal of Shanghai Jiaotong University (Science) 2023年 1-11页

作者： Tahir, Rizwan Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Recent multimedia and computer vision research has focused on analyzing human behavior and activity using images. Skeleton estimation, known as pose estimation, has received a significant attention. For human pose estimation, deep learning approaches primarily emphasize on the keypoint features. Conversely, in the case of occluded or incomplete poses, the keypoint feature is insufficiently substantial, especially when there are multiple humans in a single frame. Other features, such as the body border and visibility conditions, can contribute to pose estimation in addition to the keypoint feature. Our model framework integrates multiple features, namely the human body mask features, which can serve as a constraint to keypoint location estimation, the body keypoint features, and the keypoint visibility via mask region-based convolutional neural network (Mask-RCNN). A sequential multi-feature learning setup is formed to share multi-features across the structure, whereas, in the Mask-RCNN, the only feature that could be shared through the system is the region of interest feature. By two-way up-scaling with the shared weight process to produce the mask, we have addressed the problems of improper segmentation, small intrusion, and object loss when Mask-RCNN is used, for instance, segmentation. Accuracy is indicated by the percentage of correct keypoint, and our model can identify 86.1% of the correct keypoints. © 2023, Shanghai Jiao Tong University.

关键词： Convolutional neural networks

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Design and Application of Wideband Monitoring Master Station in Power systems

Design and Application of Wideband Monitoring Master Station...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Yiming Zeng Fang Shi Hengxu Zhang Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

The use of power electronics to integrate increasing renewable energy resources into power grids has changed the wideband characteristic of the power system signals. The traditional wide-area measurement system (WAMS) based on synchronized phasor measurement unit (PMU) can only measure the fundamental frequency component, making it unable to meet the measurement requirements of harmonics and interharmonic components. To address this problem, the wideband synchronized measurement unit (WPMU) has been developed, which supports real-time measurement of harmonic and interharmonic components, in addition to the functionalities of PMU. As a result, upgrading the WAMS master station is necessary to make the integration of wideband measurement available. This paper analyzes the characteristics of wideband measurement data, explores its practical applications and discusses the idea of upgrading the WAMS master station for wideband measurement, including designing its data transmission architecture, selecting a suitable database, and outlining advanced application methods. Finally, the paper presents the analysis of one actual engineering application, where the wideband measurement devices are installed, and the data is sent to the master station in real time. This work provides a reference for the design and development of a wideband WAMS master station, and contributes to the safety and stability of the power grid operation by providing wideband measurement information.

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STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting

arXiv

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arXiv 2024年

作者： Dong, Xiaochong Zhang, Xuemin Yang, Ming Mei, Shengwei State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China

Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significant modeling challenges. To address this, we propose a spatio-temporal dynamic hypergraph learning (STDHL) model. This model uses a hypergraph structure to represent spatial features among wind farms. Unlike traditional graph structures, which only capture pair-wise node features, hypergraphs create hyperedges connecting multiple nodes, enabling the representation and transmission of higher-order spatial features. The STDHL model incorporates a novel dynamic hypergraph convolutional layer to model dynamic spatial correlations and a grouped temporal convolutional layer for channel-independent temporal modeling. The model uses spatio-temporal encoders to extract features from multi-source covariates, which are mapped to quantile results through a forecast decoder. Experimental results using the GEFCom dataset show that the STDHL model outperforms existing state-of-the-art methods. Furthermore, an in-depth analysis highlights the critical role of spatio-temporal covariates in improving ultra-short-term forecasting accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Wind forecasting

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