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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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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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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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Rnbformer: A High-Performance Roadside Noise Barriers Recognition Algorithm 24

Rnbformer: A High-Performance Roadside Noise Barriers Recogn...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Zhang, Zirui Wu, Jiaheng Huang, Heqiang Chen, Junzhou Hu, Hongyu Zhang, Ronghui Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen Uniyersity Guangdong Guangzhou510275 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun Jilin Changchun130022 China

ISBN: (纸本)9798400716416

Traffic noise pollution is a significant source of urban pollution. Roadside noise barriers (RNBs) serve as a primary solution to urban traffic noise pollution, but achieving precise positioning of RNBs requires on-site inspections and manual labeling, which is time-consuming and labor-intensive. Deploying an RNB recognition algorithm on inspection vehicles equipped with cameras and positioning devices is an efficient approach to establishing a high-precision RNB positioning database. We propose a high-accuracy RNB recognition algorithm, RNBFormer. In the model design, we integrate both convolutional and transformer structures to enhance the model's global feature extraction capability while optimizing the representation of local features. Additionally, we incorporate a channel attention mechanism to provide more discriminative features at the channel level. We trained and tested our model on a publicly available dataset, achieving a classification accuracy of 98.93%. © 2024 ACM.

关键词： Convolution

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An Approach for Configuring Thermal Storage Capacity of Concentrated Solar Power Considering Low-Carbon Economic dispatch

An Approach for Configuring Thermal Storage Capacity of Conc...

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International Conference on Power system Technology (POWERCON)

作者： Nuo Xu Xiaodong Chu Hua Ye Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan R.R. China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan P.R. China

Concentrated Solar Power (CSP) has gradually become an emerging development direction in the clean energy area because of its high energy efficiency, renewable and abundant nature. Thermal storage devices are used to regulate the power generation characteristics of CSP plants, and a rational configuration of thermal storage capacity can effectively reduce the system's carbon footprint. Considering the generation cost of thermal power generators, operation and maintenance cost of CSP plants, and system spinning reserve cost, a low-carbon economic dispatch scheme for power systems including CSP plants that takes carbon trading into account is proposed in this paper. On this basis, a thermal storage capacity allocation method for CSP plants is proposed, taking into account the thermal storage cost and dispatching cost. Additionally, simulation verification is performed on IEEE 30 network, and the findings demonstrate that the designed thermal storage capacity configuration strategy effectively reduces carbon emissions and overall system costs.

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A joint sparse Bayesian learning for wideband DOA estimation

A joint sparse Bayesian learning for wideband DOA estimation

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2024 Conference on Spectral Technology and Applications, CSTA 2024

作者： Zhao, Yonghong Liu, Jisong Wang, Junlong Dong, Shuxin Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Department of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510683082

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method. © 2024 SPIE.

关键词： Direction of arrival

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Modeling and Parameter Identification of Constant-Speed Centrifugal Chiller Based on Experiments

Modeling and Parameter Identification of Constant-Speed Cent...

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2023 International Conference on Power system Technology, PowerCon 2023

作者： Han, Xinchan Zhang, Wen Cheng, Dingyi Zhang, Tingting Liu, Meng Xie, Yuxin Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China State Grid Shandong Electric Power Research Institute Jinan China JinXianDai Information Industry Co. Ltd. Jinan China

ISBN: (纸本)9798350300222

The expanding penetration of intermittent renewable energy resources is motivating the development of demand response (DR) services for balancing generation and load. Central air conditionings (CACs) are potential candidates due to their thermal energy storage capability. A suitable load model is the foundation for DR services. However, CACs contain multiple control subsystems whose parameters are difficult to obtain because of trade secrets. This paper focuses on the modeling and parameter identification of the constant-speed centrifugal chiller, which accounts for 60-70% of the power of CAC. A thermal-electrical model of a constant-speed centrifugal chiller is established to describe the dynamic operation process. In addition, the critical control parameters and the delay time of control systems are identified based on experimental data for a practical building chiller. The experimental results verified the effectiveness of the proposed model. © 2023 IEEE.

关键词： Air conditioning

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