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International Conference on Power and Energy Technology (ICPET)

作者： Xiaoting Lv Hongtao Wang Xuemei Fan Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

In recent years, power outages have occurred frequently around the world, and rapid load recovery after power outages can reduce economic losses caused by power outages. In the restoration model, the power flow calculation is a nonlinear equality constraint. The idea based on mathematical optimization is to linearize the power flow equation based on certain assumptions or perform convex relaxation. Such processing may affect the solution accuracy or speed. Therefore, a data-driven constraints linearization method is proposed, using a large amount of simulation data to linearly fit the power flow through the partial least square method. Then, based on the linear constraints, the load restoration problem is established as a mixed integer linear programming (MILP) problem to reduce the difficulty of solving, improve calculation accuracy and provide dispatchers with optimal recovery decisions. Finally, the validity of the method proposed in this paper is verified in IEEE39 node system.

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River Turbidity Monitoring Based on Semi-supervised Transfer Learning 9th

River Turbidity Monitoring Based on Semi-supervised Transfe...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Xie, Shuangyi Liao, Xin Bai, Ruxue Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

River turbidity is an important index to evaluate the quality of water environment, which is of great significance to environmental monitoring. Most of the existing monitoring methods rely on turbidity labels measured by river water quality monitoring stations. However, due to the limitation of spatial distribution of water quality monitoring stations, those turbidity data outside the monitoring range are difficult to obtain and the correlation of turbidity data between monitoring stations is poor. Using such data directly might weaken the generalization ability that the model has. In this paper, we propose a new river turbidity monitoring model based on semi-supervised transfer learning (RTMM-SSTL) to solve these problems. First, in the pre-training stage, we innovatively propose a semi-supervised training method combined with big data with pseudo labels for river turbidity monitoring to solve the problem of weakened model generalization ability. Then, the model is fine-tuned using GEE hyper spectral data with ground truth to further improve the monitoring capability of the model. Experiments conducted on the river turbidity monitoring task demonstrate that the proposed model is superior to the advanced learning models, and further proves that our semi-supervised transfer learning method performs better than the state-of-the-art supervision models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rivers

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Multi-Objective Upgrade Planning Strategy for Harnessing Operational Flexibility of Coal-Fired Power Generators

Multi-Objective Upgrade Planning Strategy for Harnessing Ope...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Zhengxing Ren Xiaodong Chu Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

The high penetration of renewable energy in power systems leads to significant net load fluctuations. Coal-fired power generators conventionally designed for baseload operations are expected to be flexible with cycling operations. Necessary upgrades received by the power generators to improve their operational flexibility will cause associated costs. An upgrade planning strategy is proposed in this paper for harnessing operational flexibility of coal-fired power generators. First, a multi-objective optimization model of coal-fired power generators is established in which the upgrade cost, operating cost, and the upgraded load following capacity of three stages are comprehensively integrated. Next, an improved multi-objective particle swarm optimization algorithm is used to solve the proposed model. Finally, a multi-criteria decision making method is applied to select the best upgrade plan. The case study results of the IEEE 39-bus system demonstrate the feasibility and effectiveness of the proposed planning strategy.

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Research on Lightweight Small Object Detection Method with Fusion Attention Mechanism

Research on Lightweight Small Object Detection Method with F...

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2023 China Automation Congress, CAC 2023

作者： Dai, Haowen Liu, Bingyou Wan, Guoyang Qi, Jingjing Wan, Luanfei School of Electrical Engineering Anhui Polytechnic University Wuhu China Anhui Polytechnic University The Key Lab. of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Wuhu China Anhui Dar Intelligent Control System Co. Ltd. Wuhu China Wuhu Institute of Technology Wuhu China

ISBN: (纸本)9798350303759

In the task of small object detection, due to the relatively small size of the objects, complex situations such as occlusion and overlap may arise in high-density scenes. These factors can significantly increase the issues of missed detections and false detections in object detection tasks. To address the aforementioned issues, this paper proposes a lightweight small object detection model, YOLOv5s-MGC. Firstly, we introduce an improved Mobilenetv3 network to replace the backbone network of YOLOv5s, aiming to improve the network's capability for effective feature extraction. Secondly, we incorporate the GSConv module into the feature fusion network to reduce model parameters and strengthen the network's ability in fusing feature information. Finally, we have integrated Convolutional Block Attention Module (CBAM) at the forefront of the detection heads, achieving the fusion of fine-grained features. We assessed our approach on the openly accessible dataset 'SHWD' and contrasted the results with the baseline YOLOv5s and various SOTA object detection models. Precisely, our method attains an accuracy of 91.1%, exceeding the baseline module by 1.6%. Furthermore, the module size is reduced to 1.97MB and it achieves a 52.4 FPS. In comparison to other prevailing algorithms, YOLOv5s-MGC enables efficient and accurate small object detection. © 2023 IEEE.

关键词： Object detection

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The Effect of Phase Locking Error on control of Grid-Connected Inverter in Distribution Network

The Effect of Phase Locking Error on Control of Grid-Connect...

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International Conference on Power system Technology (POWERCON)

作者： Wang Yuanqi Yun Zhihao Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

The phase-locked loop provides a phase reference for the coordinate transformation in grid-connected inverters to ensure synchronous operation and power output. In the distribution network, under the conditions of three-phase unbalance because of the diversity of loads, untransposed line and the complex operating conditions, the phase-locked loop can produce phase locking errors that affect the inverter power output and thus the regulation of the active distribution network. Therefore, by analyzing the inverter control, the influence of phase error of the phase-locked loop on the Park transformation and modulation signal is deduced, and the relationship between phase error, voltage, current, current reference value, and modulation signal is quantified. The simulation results prove the correctness of the analysis.

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Chaos synchronization of semiconductor lasers over 1040-km fiber relay transmission with hybrid amplification

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Photonics Research 2023年第6期11卷 953-960页

作者： LONGSHENG WANG JUNLI WANG YUSHAN WU YUEHUI SUN SONGSUI LI LIANSHAN YAN YUNCAI WANG ANBANG WANG Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan 030024China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of Photonics Information Technology Guangzhou 510006China Center for Information Photonics&Communications School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China

Optical chaos communication and key distribution have been extensively demonstrated with high-speed advantage but only within the metropolitan-area network range of which the transmission distance is restricted to around 300 *** secure-transmission requirement of the backbone fiber link,the critical threshold is to realize long-reach chaos ***,we propose and demonstrate a scheme of long-reach chaos synchronization using fiber relay transmission with hybrid amplification of an erbium-doped fiber amplifier(EDFA)and a distributed fiber Raman amplifier(DFRA).Experiments and simulations show that the hybrid amplification extends the chaos-fidelity transmission distance thanks to that the low-noise DFRA suppresses the amplified spontaneous emission noise and self-phase *** of the hybrid-relay conditions are studied,including launching power,gain ratio of DFRA to EDFA,single-span fiber length,and number of fiber span.A 1040-km chaos synchronization with a synchronization coefficient beyond 0.90 is experimentally achieved,which underlies the backbone network-oriented optical chaos communication and key distribution.

关键词： synchronization fiber amplifier

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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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Online Cascading Failure Searching Based on Gradient Boosting Decision Tree

Online Cascading Failure Searching Based on Gradient Boostin...

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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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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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