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Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control

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Frontiers of information technology & Electronic engineering 2024年第2期25卷 260-271页

作者： Mingguang ZHANG Feng LI Yang YU Qingfeng CAO School of electrical&information engineering Jiangsu University of TechnologyChangzhou213001China college of electrical Energy and Power EngineeringYangzhou UniversityYangzhou225127China

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive *** proposed Hammerstein nonlinear systems are made up of a neural fuzzy network(NFN)and a linear state`-space *** estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals,which consist of step signals and random ***,based on the characteristic that step signals do not excite static nonlinear systems,that is,the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input,the unknown intermediate variables can be replaced by inputs,solving the problem of unmeasurable intermediate variable *** the presence of step signals,the parameters of the state-space model are estimated using the recursive extended least squares(RELS)***,to effectively deal with the interference of measurement noises,a data filtering technique is introduced,and the filtering-based RELS is formulated for estimating the NFN by employing random ***,according to the structure of the Hammerstein system,the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system,and it can then be easily controlled by applying a linear *** effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

关键词： Hammerstein nonlinear systems Neural fuzzy network Data filtering Hybrid signals Industrial control

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

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Global Energy Interconnection 2024年第3期7卷 293-312页

作者： Qiaoling Yang Kai Chen Jianzhang Man Jiaheng Duan Zuoqi Jin college of electrical and information engineering Lanzhou University of TechnologyLanzhou 730050P.R.China

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm ***,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is *** precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and ***,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final *** CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)*** experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning *** mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other ***,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

关键词： Wind turbine power curve Abnormal data cleaning Community detection Louvain algorithm Mathematical morphology operation

An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of electrical and information engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of electrical engineering and Automation Henan Institute of Technology School of Electronics and information Zhongyuan University of Technology KINDI Center for Computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

Generalized load graphical forecasting method based on modal decomposition

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Global Energy Interconnection 2024年第2期7卷 166-178页

作者： Lizhen Wu Peixin Chang Wei Chen Tingting Pei college of electrical and information engineering Lanzhou University of TechnologyLanzhou 730050P.R.China School of electrical Data engineering University of Technology SydneyNSW2007Australia

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized *** the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal ***,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest ***,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B *** generated images are diversified,and an optimized DenseNet neural network was used for training and ***,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise *** on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

关键词： Load forecasting Generalized load Image processing DenseNet Modal decomposition

Transient Characteristics and Accommodative Current Limiting Strategy for Bidirectional Interlinking Converters in Hybrid AC/DC Microgrids

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1575-1588页

作者： Xia Shen Zhikang Shuai Wen Huang Chao Shen Yang Shen Z.John Shen college of electrical and information engineering Hunan UniversityChangsha 410082China Department of electrical and Computer engineering Illinois Institute of TechnologyChicagoIL 60616 USA

Bidirectional interlinking converter(BIC)is the core equipment in a hybrid AC/DC microgrid connected between AC and DC ***,the variety of control modes and flexible bidirectional power flow complicate the influence of AC faults on BIC itself and on DC sub-grid,which potentially threaten both converter safety and system *** study first investigates AC fault influence on the BIC and DC bus voltage under different BIC control modes and different pre-fault operation states,by developing a mathematical model and equivalent sequence ***,based on the analysis results,a general accommodative current limiting strategy is proposed for BIC without limitations to specific mode or operation *** amplitude is predicted and constrained according to the critical requirements to protect the BIC and relieving the AC fault influence on the DC bus *** with conventional methods,potential current limit failure and distortions under asymmetric faults can also be ***,experiments verify feasibility of the proposed method.

关键词： Bidirectional interlinking converter current limiting fault characteristic hybrid AC/DC microgrid variation/oscillation of DC bus voltage

Asymmetrical Fault Current Calculation Method and Influencing Factors Analysis of Droop-Controlled Inverters

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 562-573页

作者： Huimin Zhao Zhikang Shuai Jun Ge An Luo Weimin Wu Z.John Shen college of electrical and information engineering Hunan UniversityChangsha 410006China electrical engineering Department Shanghai Maritime UniversityShanghai 201306China Department of electrical and Computer engineering Ilinois Institute of TechnologyChicagoIL 60616 USA

Since the fault dynamic of droop-controlled inverter is different from synchronous generators (SGs), protection devices may become invalid, and the fault overcurrent may damage power electronic devices and threaten the safety of the microgrid. Therefore, it is imperative to conduct a comprehensive fault analysis of the inverter to guide the design of protection schemes. However, due to the complexity of droop control strategy, existing literatures have simplified asymmetric fault analysis of droop-controlled inverters to varying degrees. Therefore, accurate fault analysis of a droop-controlled inverter is needed. In this paper, by analyzing the control system, an accurate fault model is established. Based on this, a calculation method for instantaneous asymmetrical fault current is proposed. In addition, the current components and current characteristics are analyzed. It was determined that fault currents are affected by control loops, fault types, fault distance and nonlinear limiters. In particular, the influences of limiters on the fault model, fault current calculation and fault current characteristics were analyzed. Through detailed analysis, it was found that dynamics of the control loop cannot be ignored, the fault type and fault distance determine fault current level, and part of the limiters will totally change the fault current trend. Finally, calculation and experimental results verify the correctness of the proposed method.

关键词： Asymmetrical fault analysis droop control fault current calculation method limiter transient fault characteristics

Two simple memristive maps with adaptive energy regulation and digital signal process verification

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第5期25卷 382-394页

作者： Feifei YANG Lujie REN Jun MA Zhigang ZHU college of electrical and information engineering Lanzhou University of TechnologyLanzhou 730050China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of information Science and engineering Dalian Polytechnic UniversityDalian 116034China

Mathematical models can produce desired dynamics and statistical properties with the insertion of suitable nonlinear terms,while energy characteristics are crucial for practical application because any hardware realizations of nonlinear systems are relative to energy *** involvement of memristive terms relative to memristors enables multistability and initial-dependent property in memristive *** this study,two kinds of memristors are used to couple a capacitor or an inductor,along with a nonlinear resistor,to build different neural *** corresponding circuit equations are derived to develop two different types of memristive oscillators,which are further converted into two kinds of memristive maps after linear *** Hamilton energy function for memristive oscillators is obtained by applying the Helmholz theorem or by mapping from the field energy of the memristive *** Hamilton energy functions for both memristive maps are obtained by replacing the gains and discrete variables for the memristive oscillator with the corresponding parameters and *** two memristive maps have rich dynamic behaviors including coherence resonance under noisy excitation,and an adaptive growth law for parameters is presented to express the self-adaptive property of the memristive maps.A digital signal process(DSP)platform is used to verify these *** scheme will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map-energy calculation.

关键词： Hamilton energy Discrete memristor Self-adaptive regulation Digital signal process(DSP)implementation

Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of electrical and information engineering Zhengzhou UniversityZhengzhou 450001 School of electrical engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of electrical and information engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore college of electrical and information engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

Multi-modal visual tracking:Review and experimental comparison

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Computational Visual Media 2024年第2期10卷 193-214页

作者： Pengyu Zhang Dong Wang Huchuan Lu Faculty of Electronic information and electrical engineering Dalian University of TechnologyDalian 116024Chin

Visual object tracking has been drawing increasing attention in recent years,as a fundamental task in computer *** extend the range of tracking applications,researchers have been introducing information from multiple modalities to handle specific scenes,with promising research prospects for emerging methods and *** provide a thorough review of multi-modal tracking,different aspects of multi-modal tracking algorithms are summarized under a unified taxonomy,with specific focus on visibledepth(RGB-D)and visible-thermal(RGB-T)***,a detailed description of the related benchmarks and challenges is *** experiments were conducted to analyze the effectiveness of trackers on five datasets:PTB,VOT19-RGBD,GTOT,RGBT234,and ***,various future directions,including model design and dataset construction,are discussed from different perspectives for further research.

关键词： visual tracking object tracking multi-modal fusion RGB-T tracking RGB-D trackin