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Power Sharing of Different Distributed Generator Sets in Microgrid Based on Consensus control

International Journal of Circuit Theory and Applications 2025年第0期

作者： Li, Fangyuan Zuo, Xuke Cheng, Zixia Liu, Yanhong School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou China Robot Perception and Control Engineering Research Center in Henan Province Henan Zhengzhou China

Microgrids incorporate renewable energy sources, battery energy storage systems (BESS), and local loads to operate either with the main grid or independently. In microgrids, virtual synchronous generators (VSGs) and droop control are commonly used to regulate renewable energy sources, providing frequency and voltage control. The VSG and droop control adjust active power based on frequency deviation. However, their effectiveness depends on parameters and does not fully address power-sharing issues. Traditionally, power sharing is handled by secondary control via the microgrid central controller (MGCC), introducing a centralized, hierarchical approach. To enhance decentralization in microgrids, this paper proposes a consensus based power sharing scheme and its corresponding control for the distributed generators (DGs). The output power levels of DG units are determined by their capacities, ensuring efficient operation. Unlike existing approaches using a hierarchical framework, this work integrates primary and secondary control into a unified distributed control framework. Building on VSG control for microgrid voltage regulation, consensus control is added to manage the distribution of active power. A rigorous theoretical analysis is conducted to validate the proposed method. The method is capable of ensuring active power sharing without relying on an MGCC, and the sharing is also based on the ratio of rated capacities. The controller proposed in this paper achieves the expected control performance, effectively distributing active power sharing in the microgrid. Finally, the effectiveness and performance of the proposed method are verified through comprehensive case studies. © 2025 John Wiley & Sons Ltd.

关键词： Decentralized control

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Trajectory tracking control for nonlinear high-order PDE system based on iterative learning method 38

Trajectory tracking control for nonlinear high-order PDE sys...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Liu, Yaqiang Zhang, Weili Peng, Jinzhu Zhang, Wenlong Liu, Yanhong Zhengzhou University Henan Senyuan Electric Co. Ltd. School of Electrical and Information Engineering Zhengzhou China Zhengzhou University Sch. of Elec. and Info. Eng. Robot Perception and Control Engineering Laboratory of Henan Province Zhengzhou China Zhengzhou University School of Electrical and Information Engineering Zhengzhou China

ISBN: (纸本)9798350303636

This paper addresses a trajectory tracking control method based on iterative learning for the nonlinear fourth-order partial differential equation (PDE) system. A noval iterative learning control law is designed that enables the repeatable Kuramoto-Sivashinsky equation system track the desired trajectory. The Poincar'e-Wirtinger inequality and Cauchy-Schwartz inequality are applied to deal with the function problem in this paper. By using the Gronwall-Bellman lemma and integration by parts technique, a Lyapunov function is constructed and an LMI sufficient condition is provided, respectively. The effectiveness of the proposed iterative learning control method is verified through the convergence proof of the closed-loop Kuramoto-Sivashinsky equation system. Finally, the trajectory tracking curve simulation after multiple iterations is provied in this paper. © 2023 IEEE.

关键词： convergence analysis iterative learning control multiple actuators and sensors nonlinear high-order PDE system

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Insulator Fault Diagnosis Based on Improved Transfer Learning from UAV Images

Insulator Fault Diagnosis Based on Improved Transfer Learnin...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Yang, Lei Shen, Ji Wu, Man Liu, Yanhong Zhengzhou University School of Electrical Engineering 450001 China Robot Perception and Control Engineering Laboratory of Henan Province 450001 China Beijing Aerospace Control Center P.O.Box 5130 118 points boxes Haidian District Beijing China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Insulator fault diagnosis is a daily but key task for the power transmission system. Long-term exposure to complex natural environment will cause different insulator defects. As a common defects, missing-cap defects of insulators will not only affect the structural strength of power insulators, but also bring a certain effect to the stable power transmission. With the rapid development of machine learning, some machine learning-based defect recognition methods have been proposed for fast and high-precision power inspection. However, the handcrafted features could not effectively express the aerial images against complex inspection environment to affect detection performance of the shallow learning algorithms. And the detection precision of deep learning algorithms will be affected by the unbalanced small-scale defects. Therefore, the fast and high-precision power inspection still faces a certain challenge in the smart grid. To address the above issues, fusion with the deep convolutional neural network (DCNN) and transfer learning, a novel fault diagnosis algorithm of power insulators is proposed to provide a fast and accurate power inspection scheme. To remove complex backgrounds, a fast insulator location algorithm based on the lightweight YOLOV4 model is proposed which is served for the following defect recognition. On the basis, to imitate human vision, a defect recognition algorithm is proposed based on multi-feature fusion. Meanwhile, to ensure the feature expression ability of transfer learning on power insulators, a novel optimization strategy of transfer learning is proposed to improve the recognition precision. Experiments show that the proposed method could acquire a good recognition performance than other recognition models. © 2022 IEEE.

关键词： Inspection

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Electromyographic Signal Based Dynamic Hand Gesture Recognition Using Transfer Learning

Electromyographic Signal Based Dynamic Hand Gesture Recognit...

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Chinese Intelligent Automation Conference, CIAC 2021

作者： Song, Shouan Yang, Lei Huo, Benyan Wu, Man Liu, Yanhong Yu, Hongnian School of Electrical Engineering Zhengzhou University Zhengzhou China Robot Perception and Control Engineering Laboratory of Henan Province Zhengzhou China Beijing Aerospace Control Center 26 Beiqing Road Haidian District Beijing China Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9789811663710

Recent years, the research of gesture recognition based on surface EMG signal has become an active topic. The conventional methods mainly focus on feature engineering, but the sEMG signal is non-stationary temporally, which makes proper feature design and selection very complicated. To tackle this problem, this paper proposes a novel dynamic gesture recognition method by employing Convolutional Neural Networks. Short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are introduced to model the time-frequency features of a single channel and the relationship between different channels. Meanwhile, the performance of deep learning-based methods relies on a large amount of training data. In the context of sEMG based gesture recognition, one user cannot be expected to generate tens of thousands of examples at a time. Hence, transfer learning (TL) techniques is utilized to alleviate the data generation burden imposed on a single individual and enhance the performance of the Convolutional Neural Networks. Experiment results on the sEMG data set recorded with Myo Armband indicate that the transfer learning augmented ConvNet can achieve an accuracy of 99.41%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Reinforcement Learning Collaborative Action Decision Mechanism with Mixed Distribution Coefficients for robotic Grasping

Reinforcement Learning Collaborative Action Decision Mechani...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Weili Zhang Yaqiang Liu Jinzhu Peng Yanhong Liu School of Electrical and Information Engineering Robot Perception and Control Engineering Research Centrer of Henan Province Zhengzhou University Zhengzhou China School of Electrical and Information Engineering Zhengzhou University Henan Senyuan Electric Co. Ltd Zhengzhou China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

robotic arms have high precision, flexibility and automation capabilities, and are widely used in industry, medical and logistics fields. In this paper, we propose a robotic push-grasp collaborative action decision mechanism with mixed distribution coefficients based on reinforcement learning. Two fully convolutional action value functions are designed to perform corresponding pushing and grasping actions, respectively. Each fully convolutional action value function maps visual observations into the best action predictions of the Q-learning algorithm. In order to evaluate the pushing action more comprehensively, a new mixed distribution coefficient measure is proposed to address the distribution of blocks in the scene. The corresponding reward is given depending on whether the pushing action makes the scene more conducive to grasping. In addition, the mixed distribution coefficient measure is introduced into the push-grasp collaborative action decision mechanism. It can optimize the learning network training process, thereby the robot arm can make the most beneficial grasping and pushing action decision based on the real-time training results. Experimental results show that the proposed method has better performance for robotic grasping in complex scenes.

关键词： Training Visualization Automation Q-learning Service robots System performance Collaboration

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Trajectory tracking control for nonlinear high-order PDE system based on iterative learning method

Trajectory tracking control for nonlinear high-order PDE sys...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yaqiang Liu Weili Zhang Jinzhu Peng Wenlong Zhang Yanhong Liu School of Electrical and Information Engineering Zhengzhou University Henan Senyuan Electric Co. Ltd. Zhengzhou China School of Electrical and Information Engineering Robot Perception and Control Engineering Laboratory of Henan Province Zhengzhou University Zhengzhou China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

关键词：

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Electromyographic Signal Based Dynamic Hand Gesture Recognition Using Transfer Learning

Electromyographic Signal Based Dynamic Hand Gesture Recognit...

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2021中国智能自动化大会(CIAC 2021)

作者： Shouan Song Lei Yang Benyan Huo Man Wu Yanhong Liu Hongnian Yu School of Electrical Engineering Zhengzhou University Robot Perception and Control Engineering Laboratory of Henan Province Beijing Aerospace Control Center Built Environment Edinburgh Napier University

Recent years, the research of gesture recognition based on surface EMG signal has become an active topic. The conventional methods mainly focus on feature engineering, but the sEMG signal is nonstationary temporally, which makes proper feature design and selection very complicated. To tackle this problem, this paper proposes a novel dynamic gesture recognition method by employing Convolutional Neural Networks. Short-time Fourier transform(STFT) and continuous wavelet transform(CWT) are introduced to model the time-frequency features of a single channel and the relationship between different channels. Meanwhile, the performance of deep learning-based methods relies on a large amount of training data. In the context of sEMG based gesture recognition, one user cannot be expected to generate tens of thousands of examples at a time. Hence, transfer learning(TL) techniques is utilized to alleviate the data generation burden imposed on a single individual and enhance the performance of the Convolutional Neural *** results on the sEMG data set recorded with Myo Armband indicate that the transfer learning augmented Conv Net can achieve an accuracy of 99.41%.

关键词： Surface electromyographic Short time fourier transform Continuous wavelet transform Convolutional neural network Transfer learning

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PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Zhang, Xu Yang, Kailun Lin, Jiacheng Yuan, Jin Li, Zhiyong Li, Shutao College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Electrical and Information Engineering The Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Hunan University Changsha410082 China

Integration of diverse visual prompts like clicks, scribbles, and boxes in interactive image segmentation significantly facilitates users’ interaction as well as improves interaction efficiency. However, existing studies primarily encode the position or pixel regions of prompts without considering the contextual areas around them, resulting in insufficient prompt feedback, which is not conducive to performance acceleration. To tackle this problem, this paper proposes a simple yet effective Probabilistic Visual Prompt Unified Transformer (PVPUFormer) for interactive image segmentation, which allows users to flexibly input diverse visual prompts with the probabilistic prompt encoding and feature post-processing to excavate sufficient and robust prompt features for performance boosting. Specifically, we first propose a Probabilistic Prompt-unified Encoder (PPuE) to generate a unified one-dimensional vector by exploring both prompt and non-prompt contextual information, offering richer feedback cues to accelerate performance improvement. On this basis, we further present a Prompt-to-Pixel Contrastive (P2C) loss to accurately align both prompt and pixel features, bridging the representation gap between them to offer consistent feature representations for mask prediction. Moreover, our approach designs a Dual-cross Merging Attention (DMA) module to implement bidirectional feature interaction between image and prompt features, generating notable features for performance improvement. A comprehensive variety of experiments on several challenging datasets demonstrates that the proposed components achieve consistent improvements, yielding state-of-the-art interactive segmentation performance. Our code is available at https://***/XuZhang1211/PVPUFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Lin, Jiacheng Chen, Jiajun Yang, Kailun Roitberg, Alina Li, Siyu Li, Zhiyong Li, Shutao The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The College of Electrical and Information Engineering The Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Hunan University Changsha410082 China

Interactive Image Segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, but the critical issue of interaction ambiguity, notably hindering segmentation quality, has been under-researched. To address this, we introduce ADAPTIVECLICK – a click-aware transformer incorporating an adaptive focal loss that tackles annotation inconsistencies with tools for mask- and pixel-level ambiguity resolution. To the best of our knowledge, AdaptiveClick is the first transformer-based, mask-adaptive segmentation framework for IIS. The key ingredient of our method is the Click-Aware Mask-adaptive transformer Decoder (CAMD), which enhances the interaction between click and image features. Additionally, AdaptiveClick enables pixel-adaptive differentiation of hard and easy samples in the decision space, independent of their varying distributions. This is primarily achieved by optimizing a generalized Adaptive Focal Loss (AFL) with a theoretical guarantee, where two adaptive coefficients control the ratio of gradient values for hard and easy pixels. Our analysis reveals that the commonly used Focal and BCE losses can be considered special cases of the proposed AFL. With a plain ViT backbone, extensive experimental results on nine datasets demonstrate the superiority of AdaptiveClick compared to state-of-the-art methods. The source code is publicly available at https://***/lab206/AdaptiveClick. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Dynamic Hand Gesture Recognition via Electromyographic Signal Based on Convolutional Neural Network

Dynamic Hand Gesture Recognition via Electromyographic Signa...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shouan Song Lei Yang Man Wu Yanhong Liu Hongnian Yu School of Electrical Engineering Zhengzhou University China Robot Perception and Control Engineering Laboratory of Henan Province China Beijing Aerospace Control Center Beijing China Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (纸本)9781665442084

Dynamic gesture recognition is a typical human-computer interaction method owing to its great potential in practical applications. Currently, most of research work on gesture recognition has mainly focused on vision-based and surface electromyography (sEMG) methods. Compared to vision-based methods, the sequential sEMG signal can directly depict the muscle activity of different gestures which could lead to higher recognition efficiency. However, the effective feature design and selection of sEMG signal is still complicated since muscle fatigue and small electrode displacement will affect the recognition precision of sEMG signals. In this paper, a novel end-to-end dynamic gesture recognition method is developed. The raw sEMG signals are converted into an image form by using the time-frequency transformation method to obtain more comprehensive information for model training and test. And a recognition model based on Convolutional Neural Network (CNN) model is built for high-precision time-frequency image recognition. Experiments indicate that the proposed method could acquire distinguishing features from the pre-prossed images and the overall recognition accuracy on different gestures can reach up to 98.3%.

关键词： Training Time-frequency analysis Image recognition Continuous wavelet transforms Neural networks Gesture recognition Muscles

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