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Flexibility Improvement of CHP Unit for Wind Power Accommodation

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 731-742页

作者： Dongming Zhang Yong Hu Yaokui Gao School of Control and Computer Engineering North China Electric Power UniversityBeijingChina

Improving the flexibility of combined heat and power(CHP)units is an important way to solve the problem of wind power accommodation in northern China.Firstly,this paper analyzes the principle of an extraction-type CHP unit,calculates its safe operation range,and analyzes its contradiction between heating and peaking.Secondly,the safe operation ranges of the CHP unit with several flexibility modifications are further calculated,which involve two-stage bypass,low-pressure cylinder(LPC)removal,heat storage tank,and electric boiler.Finally,based on the safe operation ranges,their effects on improving the capabilities of deep peak shaving and wind power accommodation are compared,and their adaptabilities to different wind scenarios are analyzed.The results show that:①all flexibility modifications can improve the deep peak shaving capability of the CHP unit,especially for the two-stage bypass and the electric boiler;②LPC removal modification can accommodate wind power to some extent,but most of wind power is still abandoned;③heat storage tank modification is unstable in different wind scenarios,which is determined by the surplus heating capability during the daytime.

关键词： Combined heat and power(CHP) flexibility bypass low-pressure cylinder(LPC)removal heat storage tank electric boiler

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

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computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan school of computer and control engineering Yantai UniversityYantai264005China school of computer and Information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

An image compressed sensing algorithm based on adaptive nonlinear network

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Chinese Physics B 2020年第5期29卷 243-253页

作者：郭媛陈炜敬世伟 school of computer and control engineering Qiqihar UniversityQiqihar 161006China

Traditional compressed sensing algorithm is used to reconstruct images by iteratively optimizing a small number of measured values.The computation is complex and the reconstruction time is long.The deep learning-based compressed sensing algorithm can greatly shorten the reconstruction time,but the algorithm emphasis is placed on reconstructing the network part mostly.The random measurement matrix cannot measure the image features well,which leads the reconstructed image quality to be improved limitedly.Two kinds of networks are proposed for solving this problem.The first one is ReconNet’s improved network IReconNet,which replaces the traditional linear random measurement matrix with an adaptive nonlinear measurement network.The reconstruction quality and anti-noise performance are greatly improved.Because the measured values extracted by the measurement network also retain the characteristics of image spatial information,the image is reconstructed by bilinear interpolation algorithm(Bilinear)and dilate convolution.Therefore a second network USDCNN is proposed.On the BSD500 dataset,the sampling rates are 0.25,0.10,0.04,and 0.01,the average peak signal-noise ratio(PSNR)of USDCNN is 1.62 dB,1.31 dB,1.47 dB,and 1.95 dB higher than that of MSRNet.Experiments show the average reconstruction time of USDCNN is 0.2705 s,0.3671 s,0.3602 s,and 0.3929 s faster than that of ReconNet.Moreover,there is also a great advantage in anti-noise performance.

关键词： compressed sensing deep learning bilinear interpolation dilate convolution

Active Power Consensus control for Wind Turbines with Time Delays

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Chinese Journal of Electrical engineering 2022年第2期8卷 86-96页

作者： Shixian Feng Mei Yu Bo Wei Feng Xiao School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

The distributed active power control problem is explored by equating wind turbines to multi-agent systems in this paper.Both time delays and unknown topological relations are considered in the proposed model.Using the graph discovery algorithm,the algebraic connectivity of the graph is found in advance.Further,a proportional control protocol is proposed based on the adjustable margin of different wind turbines.Moreover,the proposed distributed controller not only handles the problem of supply-demand balance between the wind farm and power grid but also regulates the output power of individual wind turbines based on their capacities.Finally,simulations are performed on the wind turbines to illustrate the validity of the proposed method.

关键词： Multi-agent system active power dispatching graph discovery distributed control

Multi-Objective Task Assignment for Maximizing Social Welfare in Spatio-Temporal Crowdsourcing

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China Communications 2021年第11期18卷 11-25页

作者： Shengnan Wu Yingjie Wang Xiangrong Tong school of computer and control engineering Yantai UniversityYantai 264005China

With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network transmission has led to low data processing efficiency.Fortunately,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions faster.Therefore,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing environment.The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social welfare.The algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose.Through comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.

关键词： spatio-temporal crowdsourcing edge computing task assignment multi-objective optimization particle swarm optimization Pareto optimal solution

Viewpoint Optimization Method for Flying Robot Inspecting Transmission Towers Based on Point Cloud Model

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Chinese Journal of Electronics 2018年第5期27卷 976-984页

作者： LIU Chang’an DONG Ruifang JIN Lu WU Hua School of Control and Computer Engineering North China Electric Power University

This paper presents a method to derive optimal viewpoints for flying robot based transmission tower inspection, which applies point cloud model. A safe envelope is established according to safe transmission tower inspection rules. Essential inspection factors are proposed to evaluate the quality of candidate viewpoints, which includes visibility, dimensionality feature of point cloud as well as the distance between viewpoint and transmission power. A score function is constructed to quantify candidate viewpoints, while multiple attribute decision theory is applied to calculate the weight of each factor. Particle swarm optimization(PSO) is used to find the optimal viewpoints set. Both simulation experiment and practical observations are carried out. The results prove that optimal viewpoints have a great contribution for accurate transmission tower inspection. Final results are compared to patch-based method and proved to be feasible.

关键词： Transmission tower inspection Viewpoint optimization Point cloud model Particle swarm optimization(PSO) Flying robot

RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay

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Journal of Systems Science & Complexity 2022年第4期35卷 1262-1277页

作者： XU Juanjuan ZHANG Zhaorong school of control Science and engineering Shandong UniversityJinan250061China school of Electrical engineering and computer Science University of NewcastleNewcastleCallaghanNSW2308Australia

This paper is concerned with the multi-agent systems with both packet dropout and input delay.A novel receding horizon control(RHC)based consensus protocol is proposed by solving a distributed RHC based optimization problem.The novelty of the optimization problem lines in the involvement of the neighbours’predictor information in the cost functions.Based on the derived RHC based consensus protocol,the necessary and sufficient condition for the mean-square consensus is obtained.In addition,the authors give a specific sufficient condition to guarantee the mean-square consensus.

关键词： Consensus delay packet dropout predictor-like protocol receding horizon control

A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

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Tsinghua Science and Technology 2016年第1期21卷 71-79页

作者： Rui Wang Jinglei Zhang Suli Ren Qingjuan Li school of computer and Communication engineering University of Science and Technology Beijing school of computer and control engineering University of Chinese Academy of Sciences

In recent years, Compressed Sensing（CS） has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit（OMP） is fixed, thus limiting reconstruction precision.Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit（RIOMP） algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations.The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses.When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.

关键词： compressed sensing signal processing wireless sensor networks

A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

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Science China(Technological Sciences) 2024年第3期67卷 725-739页

作者： ZHAO YiYang LIU Xun CAO JiaBin LIU JinYu XU Yan YAN WeiXin school of Mechanical engineering Shanghai Jiao Tong UniversityShanghai 200240China College of computer and control engineering Northeast Forestry UniversityHarbin 150040China

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

关键词： industrial serial robot Model-Y harmonic calibration stiffness model online compensation