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PID2018 Benchmark Challenge: Model-based Feedforward Compensator with A Conditional Integrator

IFAC-PapersOnLine 2018年第4期51卷 888-893页

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the efficiency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. © 2018

关键词： controllers Benchmarking Disturbance rejection Electric control equipment Proportional control systems Refrigeration Three term control systems Two term control systems Conditional integrators Feed forward compensation Model based method PID controllers Refrigeration system

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Multi-objective optimization of wind turbine via controllers coordination and sensitivity analysis 8

Multi-objective optimization of wind turbine via controllers...

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8th Renewable Power Generation Conference, RPG 2019

作者： Chen, Zhenyu Lin, Zhongwei Wang, Chuanxi Han, Xiangyu Li, Gengda Hu, Feng Si, Zongming State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China Guodian New Energy Technology Research Institute Co. Ltd Beijing102209 China New Energy Co. Ltd Jinan250099 China

ISBN: (数字)9781839531248

ISBN: (纸本)9781839531255

In this paper, a sensitivity-analysis-based wind turbine optimization strategy is introduced to improve the power output stability while reducing the shaft and tower loads. Both generator torque and blade pitch are controlled coordinately to achieve the optimization. During the wind turbine control process, blade pitch and generator torque are controlled proportionally to the generator speed tracking error, which simplifies the controller structure, thus, the wind turbine performance can be evaluated with control variables sensitivities directly. The wind turbine performance is considered in output power stability, shaft bending moment and tower bending moment. Both load parameters are analysed in fatigue damage lifetime. A conflicting relationship is realized between the load-power characteristic during the optimization, and a global coordination solution is selected on the pareto optimal frontier. Essential, this work is an exploration for the quantitative analysis from the wind turbine load performance to the control variables sensitivities directly without the limitation of controller forms. Simulation results show the effectiveness of the proposed controller. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Multiobjective optimization

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Nonlinear Modeling of the Subcritical Boiler-turbine System with Stochastic Differential Equations∗

Nonlinear Modeling of the Subcritical Boiler-turbine System ...

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2019 IEEE International Conference on Power Data Science, ICPDS 2019

作者： Du, Ming Xia, Jie Qian, Yonggui Li, Xiaobin Cai, Guoyuan Liu, Rui School of Control and Computer Engineering North China Electric Power University Beijing China Huaihu Coal and Electricity Co. Ltd. Tianji Power Plant Huainan Anhui Province232000 China China Railway Beijing Bureau Group Co. Ltd. Beijing West Power Section Official Beijing China

ISBN: (纸本)9781728137759

With the large-scale renewable energy integrated into power system, the optimization of coordinated control system (CCS) is increasingly crucial. The dynamics of subcritical boiler-turbine system have been studied through kinds of modeling methods. But the ordinary differential equations (ODEs) are used in previous studies mainly, which can not reflect the uncertainties in this system. In this work, the uncertainties in subcritical boiler-turbine system had been analyzed and the dynamics had been described with stochastic differential equations (SDEs). Great improvements of boiler- turbine system model can be seen in the simulations. This model can be the platform to study stochastic model predictive control in CCS. © 2019 IEEE.

关键词： Ordinary differential equations

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Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching

arXiv

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arXiv 2019年

作者： Yang, Jiaqi Quan, Siwen Wang, Peng Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Poly-technical University Xi’an710129 China School of Electronic and Control Engineering Chang’An University Xi’an710064 China

—Local geometric descriptors remain an essential component for 3D rigid data matching and fusion. The devise of a rotational invariant local geometric descriptor usually consists of two steps: local reference frame (LRF) construction and feature representation. Existing evaluation efforts have mainly been paid on the LRF or the overall descriptor, yet the quantitative comparison of feature representations remains unexplored. This paper fills this gap by comprehensively evaluating nine state-of-the-art local geometric feature representations. Our evaluation is on the ground that ground-truth LRFs are leveraged such that the ranking of tested feature representations are more convincing as opposed to existing studies. The experiments are deployed on six standard datasets with various application scenarios (shape retrieval, point cloud registration, and object recognition) and data modalities (LiDAR, Kinect, and Space Time) as well as perturbations including Gaussian noise, shot noise, data decimation, clutter, occlusion, and limited overlap. The evaluated terms cover the major concerns for a feature representation, e.g., distinctiveness, robustness, compactness, and efficiency. The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description. A summary of evaluated methods regarding their peculiarities is also presented to guide real-world applications and new descriptor crafting. Copyright © 2019, The Authors. All rights reserved.

关键词： Shot noise

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Impact Damage Identification for Composite Material Based on Transmissibility Function and OS-ELM Algorithm

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Journal of Quantum Computing 2019年第1期1卷 1-8页

作者： Yajie Sun Yanqing Yuan Qi Wang Sai Ji Lihua Wang Shaoen Wu Jie Chen Qin Zhang Jiangsu engineering Centre of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Science Ball State UniversityMuncieUSA

A method is proposed based on the transmissibility function and the OnlineSequence Extreme Learning Machine (OS-ELM) algorithm, which is applied to theimpact damage of composite materials. First of all, the transmissibility functions of theundamaged signals and the damage signals at different points are calculated. Secondly,the difference between them is taken as the damage index. Finally, principal componentanalysis (PCA) is used to reduce the noise feature. And then, input to the online sequencelimit learning neural network classification to identify damage and confirm the damagelocation. Taking the amplitude of the transmissibility function instead of the accelerationresponse as the signal analysis for structural damage identification cannot be influencedby the excitation amplitude. The OS-ELM algorithm is based on the ELM (ExtremeLearning Machine) algorithm, in-creased training speed also increases the recognitionaccuracy. Experiment in the epoxy board shows that the method can effectively identifythe structural damage accurately.

关键词： Impact damage transmissibility function OS-ELM

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When energy trading meets blockchain in electrical power system: The state of the art

arXiv

引用

arXiv 2019年

作者： Wang, Naiyu Zhou, Xiao Lu, Xin Guan, Zhitao Wu, Longfei Du, Xiaojiang Guizani, Mohsen School of Control and Computer Engineering North China Electric Power University Beijing China Department of Mathematics and Computer Science Fayetteville State University FayettevilleNC United States Department of Computer and Information Science Temple University PhiladelphiaPA United States Electrical and Computer Engineering Department University of Idaho MoscowID United States

With the rapid growth of renewable energy resources, the energy trading began to shift from centralized to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted to design new energy trading schemes. However, there are many challenging issues for blockchain‐based energy trading, i.e., low efficiency, high transaction cost, security & privacy issues. To tackle with the above challenges, many solutions have been proposed. In this survey, the blockchain‐based energy trading in electrical power system is thoroughly investigated. Firstly, the challenges in blockchain‐based energy trading are identified. Then, the existing energy trading schemes are studied and classified into three categories based on their main focus: energy transaction, consensus mechanism, and system optimization. And each category is presented in detail. Although existing schemes can meet the specific energy trading requirements, there are still many unsolved problems. Finally, the discussion and future directions are given. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

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Nonzero-Dynamics Stealthy Attack and Its Impacts Analysis in DC Microgrids

Nonzero-Dynamics Stealthy Attack and Its Impacts Analysis in...

引用

American control Conference

作者： Mengxiang Liu Chengcheng Zhao Ruilong Deng Peng Cheng Wenhai Wang Jiming Chen State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou 310027 P. R. China School of Computer Science and Engineering Nanyang Tec

ISBN: (纸本)9781538679012;9781538679265

In this paper, we explore the potential stealthy attacks in the DC microgrid (DCmG) equipped with unknown input observer (UIO) based detectors, which are widely adopted for the detection and identification of cyber-attacks. We first prove that once the attacker knows the bounds of the initial state estimation error and the measurement noise, he/she can launch the nonzero-dynamics stealthy (NDS) attack in the DCmG, which can affect the detection residual while keep stealthy. Considering the complexity of the multi-layer control framework in the DCmG, we simplify the primary control loops as static unit gains and obtain the systematic dynamic model of the DCmG under the NDS attack. Then, we obtain the analytical expressions of the Point of Common Coupling (PCC) voltages, which are utilized to analyze the effects of the NDS attack on voltage balancing and current sharing, respectively. Moreover, we prove that under the NDS attack, the voltage and current convergence can still be achieved exponentially in the DCmG. Finally, extensive simulations are conducted in Simulink/PLECS to validate our theoretical results.

关键词： distributed control distributed power generation observers power generation control power system protection power system state estimation voltage control power system state estimation power generation control Power system protection distributed power generation diazonaphthalenedisulfonic acid decentralised control Voltage regulation Observers impact assessment voltage balance

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On the Confidentiality of Linear Anomaly Detector States

On the Confidentiality of Linear Anomaly Detector States

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American control Conference

作者： David Umsonst Ehsan Nekouei Andre Teixeira Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm 10044 Sweden Signals and Systems Group at Uppsala University Sweden

ISBN: (纸本)9781538679012;9781538679265

A malicious attacker with access to the sensor channel in a feedback control system can severely affect the physical system under control, while simultaneously being hard to detect. A properly designed anomaly detector can restrict the impact of such attacks, however. Anomaly detectors with an internal state (stateful detectors) have gained popularity because they seem to be able to mitigate these attacks more than detectors without a state (stateless detectors). In the analysis of attacks against control systems with anomaly detectors, it has been assumed that the attacker has access to the detector's internal state, or designs its attack such that it is not detected regardless of the detector's state. In this paper, we show how an attacker can realize the first case by breaking the confidentiality of a stateful detector state evolving with linear dynamics, while remaining undetected and imitating the statistics of the detector under nominal conditions. The realization of the attack is posed in a convex optimization framework using the notion of Kullback-Leibler divergence. Further, the attack is designed such that the maximum mean estimation error of the Kalman filter is maximized at each time step by exploiting dual norms. A numerical example is given to illustrate the results.

关键词： Confidentiality Feedback control systems anomaly Kalman filters Convex function Attackers

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Doping-driven electronic and lattice dynamics in the phase-change material vanadium dioxide

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Physical Review B 2020年第11期102卷 115148-115148页

作者： Kannatassen Appavoo Joyeeta Nag Bin Wang Weidong Luo Gerd Duscher E. Andrew Payzant Matthew Y. Sfeir Sokrates T. Pantelides Richard F. Haglund, Jr. Department of Physics University of Alabama at Birmingham Birmingham Alabama 35294 USA Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA School of Chemical Biological and Materials Engineering University of Oklahoma Norman Oklahoma 73019 USA Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Key Laboratory of Artificial Structures and Quantum Control School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Department of Materials Science and Engineering University of Tennessee Knoxville Tennessee 37996 USA Center for Functional Nanomaterials Brookhaven National Laboratory Upton New York 11973 USA Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee 37235 USA

Doping is generally understood as a strategy for including additional positive or negative charge carriers in a semiconductor, thereby tuning the Fermi level and changing its electronic properties in the equilibrium limit. However, because dopants also couple to all of the microscopic degrees of freedom in the host, they may also alter the nonequilibrium dynamical properties of the parent material, especially at large dopant concentrations. Here, we show how substitutional doping by tungsten at the 1 at. % level modifies the complex electronic and lattice dynamics of the phase-change material vanadium dioxide. Using femtosecond broadband spectroscopy, we compare dynamics in epitaxial thin films of pristine and tungsten-doped VO2 over the broadest wavelength and temporal ranges yet reported. We demonstrate that coupling of tungsten atoms to the host lattice modifies the early electron-phonon dynamics on a femtosecond timescale, altering in a counterintuitive way the ps-to-ns optical signatures of the phase transition. Density functional theory correctly captures the enthalpy difference between pristine and W-doped VO2 and shows how the dopant softens critical V-V phonon modes while introducing new phononic modes due to W-V bonds. While substitutional doping provides a powerful method to control the switching threshold and contrast of phase-change materials, determining how the dopant dynamically changes the broadband optical response is equally important for optoelectronics.

关键词： Electronic structure Lattice dynamics Phase transitions Phonons Photonics Functional materials Thin films Band structure methods Electron microscopy Epitaxy Femtosecond laser spectroscopy Film deposition First-principles calculations Laser techniques Monte Carlo methods Scanning electron microscopy

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Software Defect-Prone Classification using Machine Learning: A Virtual Classification Study between LibSVM & LibLinear

Software Defect-Prone Classification using Machine Learning:...

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International Conference on Mathematics, Actuarial Science, computer Science and Statistics (MACS)

作者： Salahuddin Shaikh Liu Changan Maaz Rasheed Malik Muhammad Asghar Khan School of Control & Computer Engineering North China Electric Power University Beijing China Guilin University of Electronic Technology Guilin China Institute of Business Management Karachi Pakistan

ISBN: (数字)9781728149561

ISBN: (纸本)9781728149578

The field of machine learning has been developing quickly, delivering an assortment of learning algorithms for various software applications. A definitive estimation of those algorithms is, all things considered, made a decision by their accomplishment in taking care of genuine issues. Machine Learning systems can be utilized to dissect data from alternate points of view and empower engineers to recover valuable data. Machine learning has been effectively applied to make forecasts in different datasets. Given the tremendous number of bug datasets accessible today, foreseeing the nearness of bugs also should be possible utilizing different machine learning procedures. The machine learning systems that can be utilized to identify bugs in software datasets by use of classification technique. Classification is a data mining and machine learning approach, helpful in software Defect-Prone model. It includes order of software modules into buggy, faulty or non-buggy, non-faulty that is signified by a lot of software intricacy measurements by using a classification model that is gotten from before improvement ventures data. we classifying effectiveness accuracy and efficiency software defect-prone model dependent on classification technique where we have utilized LibSVM and LibLinear classification. In our examination, we have seen that the during classification, LibSVM have increased the accuracy and efficiency special in train-set way. The TP-Rate and F-Measure positive accuracy is highly increased rather than other techniques. The area under curve is also enhanced using LibSVM in training datasets. But the correctly classified instances rate also increased in all classification. Bute with the use of % split, LibLinear an SVM are also good in few evaluation measures for their enhancement accuracy and efficiency.

关键词： Software Machine learning computer bugs Predictive models Machine learning algorithms Analytical models Classification algorithms

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