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4th International Conference on intelligent Power and systems, ICIPS 2024

作者： Zhang, Jingyue Zhang, Jie Hu, Yufeng Lu, Yuanhong Huang, Libin Zhao, Yanjun Guo, Haiping China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China China Southern Power Grid National Energy Power Grid Technology R & D Centre Guangzhou Guangzhou510663 China China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangzhou510663 China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China National Energy Power Grid Technology R & D Centre Guangzhou Guangzhou510663 China

ISBN: (纸本)9798331532765

Addressing the stability challenges posed by the unpredictability and intermittent nature of wind power output during grid integration, and aiming to enhance the understanding of factors influencing grid stability upon wind power system connection and to implement efficient control measures, a grid- connected model of a doubly fed induction generator (DFIG) was established utilizing a customized version of the RTRES (Real-Time Power system Simulation) software, specifically designed for emerging power systems and aligned with wind farm realities. Stability simulations for grid connection were conducted under both voltage regulation mode and reactive power regulation mode across a range of wind speeds, revealing distinct technical behaviors. The findings suggest that the generator's terminal voltage, terminal current, active power, reactive power, and rotor speed exhibit strong correlations with variations in wind speed, and that all parameters are capable of stabilizing following initial disturbances. © 2024 IEEE.

关键词： Windmill

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Short-Term Load Prediction of EV Charging Station Based on LSTM Recursion

Short-Term Load Prediction of EV Charging Station Based on L...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Ziyu Xu Kuan Cao Yutian Liu Chunyi Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Development Planning Department State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

With the rapidly increasing market share of electric vehicles (EV) in recent years, the randomness and uncertainty of EV charging load have posed challenges to the operation of distribution networks. To improve accuracy in load prediction of EV charging station, a short-term load prediction method based on the long short-term memory (LSTM) recursive neural network is proposed in the paper. First, the Canopy-based K-means clustering algorithm is improved to divide the similarly daily load sets of EV charging station. Then, the characteristic information of load days, such as meteorological data, is processed to obtain the typical feature vector of each load set. Next, the bidirectional long short-term memory (BiLSTM) models corresponding to each load set are trained based on the sliding window principle and phase space reconstruction. By calculating the Euclidean distance of the feature vectors between the predicted day and each load set, the most suitable pre-trained BiLSTM model is selected to predict the charging station load. Finally, simulation results demonstrate that the proposed method can effectively improve the load prediction accuracy.

关键词： Recurrent neural networks Accuracy Uncertainty Simulation Predictive models Feature extraction Electric vehicle charging

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A hybrid algorithm for underwater image restoration based on color correction and image sharpening

A hybrid algorithm for underwater image restoration based on...

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作者： Meng, Haiyang Yan, Yongjie Cai, Chengtao Qiao, Renjie Wang, Feng College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Ministry of Education Harbin150001 China Harbin150001 China State Key Laboratory of Air Traffic Management System and Technology Nanjing China

In this paper, an effective method is proposed for restoring underwater image based on color correction and image sharpening. The main purpose of this method is to improve the visibility of underwater images. Traditional methods generally adopt color balancing method to restore the images. However, we found that color balancing has a poor effectiveness on underwater image when the values of blue channel are greater than other two channels, specially, red channel is great of small than blue channel. Therefore, we propose a hybrid method for color correction process based on a principle that exploits the relationship of three channels (red channel, green channel, and blue channel). On the other words, color balancing will be employed to restore the images when the values of red channel approximate to blue channel, while the DCP-base method will be used if otherwise. To enhance the sharpness of underwater image, we employed a sharpening process based on Maximum a Posteriori (MAP) method when color correction has finished. The proposed method also been validated through carrying out experiments on several underwater image datasets which are provided by previous researchers. Our validation has proved that the proposed method has a better performance than the state-of-the-art methods. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image reconstruction

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Distributed Photovoltaic Power Forecasting Method Based on AP Clustering and Transfer Learning

Distributed Photovoltaic Power Forecasting Method Based on A...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Baolong Yang Ming Yang Qiangsheng Bu Chuanqi Wang Yuanhe Zhang Menglin Li Fei Luo Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China Research Institute State Grid Jiangsu Electric Power CO. LTD. Nanjing China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Highly accurate photovoltaic (PV) power forecast technology can provide a scientific basis for grid planning to ensure safe, economical, and efficient grid operation. The investment cost for a specialized power prediction system for each PV plant is high. Consequently, a distributed PV (DPV) power forecast method based on affinity propagation (AP) clustering and transfer learning is proposed in this paper. First, the dynamic time warping (DTW) algorithm determines the similarity of power curves between DPV plants; on this basis, multiple DPV power plants in the area to be forecasted are divided into station clusters by using AP clustering; and the PV power plants in each station cluster are divided into source and target domain by transfer learning, combined with long-short term memory (LSTM) network, the power forecasting model is established, and enabling transfer of power models from source to target domain. Finally, data from 20 DPV plants in China are used to demonstrate the effectiveness of the approach. Compared to separate modeling, the proposed method reduces the average training time by 58% while maintaining the forecasting accuracy.

关键词： Photovoltaic systems Training Accuracy Costs Heuristic algorithms Transfer learning Weather forecasting Predictive models Forecasting Long short term memory

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A Pilot Study on Fabric-based Pneumatic Soft Gloves for Assisting Patients with Severe Brachial Plexus Injury

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Feng, Miao Yang, Dezhi Sun, Jianing Lao, Jie Rui, Jing Gu, Guoying Shanghai Jiao Tong University Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai200240 China Southwest Technology and Engineering Research Institute Chongqing400039 China Fudan University Department of Hand Surgery Huashan Hospital China NHC Key Laboratory of Limbs Reconstruction Shanghai Key Laboratory of Peripheral Nerve and Microsurgery Shanghai200040 China

Objective: robotic gloves show promise in hand assistance due to their wearability and home-based potential, yet empirical research remains limited. This pilot study presents a fabric-based pneumatic soft glove, aiming to identify its potential and challenges in clinical practice by evaluating its effectiveness in assisting patients with severe brachial plexus injury (BPI). Methods: The glove integrates a thumb abduction actuator and four bidirectional fabric-based pneumatic actuators (FPAs) with asymmetric chambers for high output force. Sixteen healthy volunteers and five individuals with BPI, all of whom lacked active hand and wrist movements, were recruited. Participants performed object grasping across 25 cm. The healthy group performed seven tasks using objects weighing up to 2 kg, with muscle activities recorded for analysis. The BPI group further performed tasks with eight objects from the action research arm test (ARAT) and twelve objects for activities of daily living (ADLs), encompassing various sizes, weights, and geometries. Results: In the healthy group, sEMG showed a decrease in 89.3% of trials, with 56.0% of these decreases being significant (p © 1964-2012 IEEE.

关键词： Pneumatic actuators

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Towards More Accurate Tiny Object Tracking: Benchmark and Algorithm

Towards More Accurate Tiny Object Tracking: Benchmark and Al...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jizhe Yu Yu Liu Hongkui Wei Kaiping Xu DUT School of Software Technology & DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Despite the significant progress in tiny object tracking algorithms based on convolutional neural networks, the performance falls short of the ideal level due to dataset challenges like limited scale, limited match backdrop, low match popularity, and low-resolution(720p) images. The lack of specialized large-scale tiny object tracking datasets leads data-hungry tracking algorithms to rely on small and saturated datasets from TrackNet. This paper propels the development of tiny object tracking and contributes the first large-scale dataset, Badminton100K, for tiny ball tracking. We provide over 100K annotations covering over 200 videos from 12 professional match events. Our dataset covers high-resolution(1080p) match videos in broad and diverse contexts. Moreover, considering the fast-moving tiny balls in videos often leads to challenges such as blur, afterimage, overlap, and disappearance. As another contribution, we also propose a novel Multi-stage and Multi-scale Fusion Network (MMFNet), which can extract local high-resolution details in the shallow stages and capture global semantic information in the deep stages, addressing these challenging tasks through multiple stages multi-scale feature fusion, enabling accurate identification and localization of tiny balls. Extensive experiments show that our method achieves unprecedented state-of-the-art tracking performance on Badminton100K. The dataset and the algorithm code are available at https://***/Gi-gigi/MMFNet.

关键词： Location awareness Accuracy Image resolution Semantics Transfer learning Propulsion Feature extraction

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Transient Voltage Stability Assessment Method based on gcForest

Transient Voltage Stability Assessment Method based on gcFor...

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2021 International Conference on Electrical, Electronics and Computing Technology, EECT 2021

作者： Zhiyuan, Sun Mingpo, L.I. Jie, Zhang Binjiang, Hu Guo, Qi Yihua, Zhu Guangxi Power Grid Electrical Power Research Institute China State Key Laboratory of HVDC Electric Power Research Institute CSG China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China CSG Key Laboratory of Power System Simulation

Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi Power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition

arXiv

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arXiv 2024年

作者： Wang, Mei Deng, Weihong Hu, Jiani Su, Sen The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

The study of oracle characters plays an important role in Chinese archaeology and philology. However, the difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. In this paper, we develop a novel unsupervised domain adaptation (UDA) method, i.e., unsupervised attention regularization network (UARN), to transfer recognition knowledge from labeled handprinted oracle characters to unlabeled scanned data. First, we experimentally prove that existing UDA methods are not always consistent with human priors and cannot achieve optimal performance on the target domain. For these oracle characters with flip-insensitivity and high inter-class similarity, model interpretations are not flip-consistent and class-separable. To tackle this challenge, we take into consideration visual perceptual plausibility when adapting. Specifically, our method enforces attention consistency between the original and flipped images to achieve the model robustness to flipping. Simultaneously, we constrain attention separability between the pseudo class and the most confusing class to improve the model discriminability. Extensive experiments demonstrate that UARN shows better interpretability and achieves state-of-the-art performance on Oracle-241 dataset, substantially outperforming the previously structure-texture separation network by 8.5%. © 2024, CC BY.

关键词： Character recognition

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Study on the excitation inrush suppression strategy of phase-selective switching converter with closing resis-tor switch considering dispersion 10

Study on the excitation inrush suppression strategy of phase...

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10th China International Conference on Electricity Distribution, CICED 2022

作者： Liu, Zhijiang Lin, Xuehua Huang, Libin Li, ShuYong State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid. Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9781665452687

ISBN: (纸本)9781665452687

A detailed analysis of the excitation inrush current bias phenomenon in the event is carried out in the context of a certain time when charging of a converter is carried out in an ultra-high voltage DC project of the Southern China Power Grid to obtain the cause of the event, which is confirmed by RTDS tests. By analysing the relationship between the moment of closing of different auxiliary contacts and the intermittent angle of the excitation inrush current, the shortest input time at which the closing resistance can play a role in weakening the excitation inrush current is proposed at different moments of closing. Finally, considering the discrete nature of circuit breaker closing, the proposed closing resistor best access time and the best closing angle, the effectiveness of the proposed strategy is verified by simulation tests. © 2022 IEEE.

关键词： Electric circuit breakers

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Research on flame classification and recognition based on object detection and similarity fusion

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The Journal of China Universities of Posts and Telecommunications 2021年第5期28卷 59-67页

作者： He Xin Zhou Junhua Liao Zhonghua Zhai Xiang Sun Siyuan School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation CenterBeijing 100854China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China

The color, shape, and other appearance characteristics of the flame emitted by different flame engines are different. In order to make a preliminary judgment on the category of the device to which it belongs through studying exterior characteristics of the flame, this paper uses the flame of matches, lighters, and candles to simulate different types of flames. It is hoped that the flames can be located and classified by detecting the characteristics of flames using the object detection algorithm. First, different types of fire are collected for the dataset of experiments. The mmDetection toolbox is then used to build several different object detection frameworks, in which the dataset can be trained and tested. The object detection model suitable for this kind of problem is obtained through the evaluation index analysis. The model is ResNet50-based faster-region-convolutional neural network(Faster R-CNN), whose mean average-precision(mAP) is 93.6%. Besides, after clipping the detected flames through object detection, a similarity fusion algorithm is used to aggregate and classify the three types of flames. Finally, the color components are analyzed to obtain the red, green, blue(RGB) color histograms of the three flames.

关键词： flame classification object detection similarity fusion

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