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Optimal and Robust Power System Stabilizers in a Multi Machine System

Computers, Materials & Continua 2022年第11期73卷 4139-4156页

作者： Mehrdad Ahmadi Kamarposhti Hassan Shokouhandeh Ilhami Colak Kei Eguchi Department of Electrical Engineering Jouybar BranchIslamic Azad UniversityJouybarIran Department of Electrical Engineering Semnan UniversitySemnanIran Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbulTurkey Department of Information Electronics Fukuoka Institute of TechnologyFukuokaJapan

One method for eliminating oscillations in power systems is using *** applying an appropriate control signal in the excitation system of a generator,a power system stabilizer improves the dynamic stability of power ***,the issue that is of high importance is the correct design of these *** stabilizers must be designed to have proper performance when operating conditions *** designed incorrectly,not only they do not improve the stability margin,but also increase the *** this paper,the robust design of power system stabilizers on a four-machine power system has been *** this purpose,the differential evolution algorithm has been *** studies have been repeated as three scenarios by producing different loading conditions for the *** performances of the conventional stabilizer and the optimized one have been *** results indicate that the designed robust stabilizer outperforms other stabilizers in different conditions and has damped the interarea and intra-area oscillation modes in the shortest time with minimum amplitude.

关键词： Power system dynamic stability power system stabilizers robust design evolution algorithm

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Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

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APPLIED INTELLIGENCE 2020年第6期50卷 1889-1907页

作者： Nguyen Thi My Binh Huynh Thi Thanh Binh Nguyen Van Linh Yu, Shui Hanoi Univ Ind Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Univ Technol Sydney Sydney NSW Australia

One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid evolutionary algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

关键词： Internet of things Heterogeneous wireless multimedia sensor network Directional sensing coverage model Minimal exposure path evolution algorithm Particle swarm optimization algorithm

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Design of reduced-order controllers via H_∞ and parametric optimisation:: Comparison for an active suspension system

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EUROPEAN JOURNAL OF CONTROL 2003年第1期9卷 48-60页

作者： Campos-Delgado, DU Femat, R Ruiz-Velázquez, E IPICYT Dpto Matemat Aplicdas & Sistemas Computac Mexico City 78231 DF Mexico Zona Univ Fac Ciencias San Luis Potosi SLP Mexico USALP Fac Ingn CIEP San Luis Potoi Mexico

The design of reduced-order controllers under specific performance and structure requirements is dealt in this contribution. Two controllers are designed and compared. The first one was designed using H-infinity theory whereas the latter one is designed departing from a parametric-optimisation via a two-stage algorithm. The time spent by the designer using our second approach is largely reduced. An active suspension system is selected as a case study. The performance of both controllers is tested experimentally in the active, suspension set-up. The experimental results show that the parametric-optimisation controller practically meets the desired performance specifications. Meanwhile, the H-infinity controller cannot accomplish the imposed constraints just in the low-frequency range.

关键词： active suspension evolution algorithm H-infinity control parametric optimisation

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A Novel evolutionary algorithm for Scheduling Distributed No-Wait Flow Shop Problems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第6期54卷 3694-3704页

作者： Pan, Yuxia Gao, Kaizhou Li, Zhiwu Wu, Naiqi Huzhou Vocat & Tech Coll Sch Informat Engn & Internet Things Huzhou 313000 Zhejiang Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Taipa 999078 Macao Peoples R China

This study focuses on distributed no-wait permutation flow shop scheduling problems that have many practical engineering backgrounds. The objective is to dispatch jobs optimally to multiple processing centers and ordering them for minimizing the maximum completion time (makespan). First, to solve the problems, a mathematical model is established. Second, a novel evolutionary algorithm is proposed, in which a two-dimensional (2-D) array is designed for solution representation. Based on the problem-specific knowledge, a factory assign strategy and jigsaw puzzle inspired algorithm (JPA) are employed for initializing the population of the evolutionary algorithm. Furthermore, a relative local search is used to improve the performance of the proposed algorithm. Finally, 120 instances with different scales are solved and the results are recorded. Comparisons and discussions show the proposed algorithm has computational competitiveness in solving the concerned problems with makespan criteria.

关键词： Distributed scheduling evolution algorithm flow shop scheduling makespan no wait

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Solving Ontology Meta-Matching Problem Through an evolutionary algorithm With Approximate Evaluation Indicators and Adaptive Selection Pressure

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IEEE ACCESS 2021年 9卷 3046-3064页

作者： Lv, Qing Jiang, Chengcai Li, He Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China

Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication. As an effective method, ontology matching can address the problem above, whose core technique is the similarity measure. A single similarity measure calculates the similarity value about a feature between two concepts, but none of the similarity measures can ensure their effectiveness in all context due to the diverse heterogeneous features between two ontologies. Therefore, multiple similarity measures are usually aggregated to improve the result's confidence. The problem that how to determine the optimal aggregating weights for the different similarity measures to obtain a high-quality alignment is called the meta-matching problem of ontology, which is modeled as a nonlinear problem with many local optimal solutions. evolutionary algorithm (EA) can represent an efficient methodology to address the ontology meta-matching problem, but EA-based ontology matching techniques suffer from the premature convergence and the requirement of a reference alignment to evaluate the solutions. To overcome the defects mentioned above, in this work, an improved EA-based matching approach is proposed, where two approximate evaluation indicators, i.e. pseudo-recall and pseudo-precision, are presented to evaluate the solution's quality, and an adaptive selection pressure is utilized to overcome the premature convergence. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)'s benchmark, and the experimental results will prove the effectiveness of our proposed method.

关键词： Ontologies Convergence Weight measurement Syntactics Power engineering Linguistics Licenses Ontology meta-matching evolution algorithm approximate evaluation indicator similarity measure adaptive selection pressure

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Novel numerical method to model multichannel erbium-doped fiber amplifier

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FIBER AND INTEGRATED OPTICS 2006年第5期25卷 375-385页

作者： Priye, Vishnu Singh, Dharmendra K. Arya, Subhash C. Indian Sch Mines Elect & Instrumentat Dept Dhanbad 826004 Bihar India BIT Sindri Elect & Commun Dept Dhanbad Bihar India

We propose a novel numerical method based on genetic algorithm (GA) to solve the evolution equations that are the basis of modeling multichannel erbium-doped fiber amplifiers (EDFAs). The evolution equations are first transformed from an iteration problem to an optimization problem. The unknown parameter related to backward amplified spontaneous emission (BASE) is coded into a string of bits based on their probable lower and upper limits. By defining proper random variation operators and cost function, the coupled evolution equations are optimized using GA for different values of erbium-doped fiber parameters. It is observed that GA is an efficient method for analyzing the EDFA characteristics in the entire C-band.

关键词： genetic algorithm evolution algorithm EDFA modeling optical amplifiers

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Analysis and prediction of COVID-19 epidemic in South Africa

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ISA TRANSACTIONS 2022年 124卷 182-190页

作者： Ding, Wei Wang, Qing-Guo Zhang, Jin-Xi Changshu Inst Technol Fac Elect Engn & Automat Changshu 215500 Jiangsu Peoples R China Univ Johannesburg Fac Engn & Built Environm Inst Intelligent Syst ZA-2006 Johannesburg South Africa Beijing Normal Univ Zhuhai Inst Artificial Intelligence & Future Networks Zhuhai 519000 Peoples R China BNU HKBU United Int Coll Zhuhai 519000 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively;the virus shall cease spreading on April 28, 2021;and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： COVID-19 Epidemic situation analysis Epidemic forecasting evolution algorithm South Africa

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Partial crosstalk cancellation with discrete bit-loading using genetic algorithm

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2010年第5期64卷 383-389页

作者： Wahibi, Issam Ouzzif, Meryem Le Masson, Jerome Saoudi, Samir Orange Labs F-22307 Lannion France Technopole Brest Iroise Inst TELECOM TELECOM Bretagne CNRSUMRLab STICC 3192CS 83818 F-29238 Brest 3 France Univ Europeenne Bretagne Rennes France

The reduction of far-end crosstalk in coordinated digital subscriber line (DSL) systems has been proposed to increase their performance. Joint tone-line selection (JTLS) partial crosstalk cancellation helps reduce drastically the online computational complexity of the transmission. Several algorithms were considered but did not take into account the discrete bit-loading and its effect on the selection. In this paper, the problem of JTLS partial crosstalk cancellation in the case of discrete allocation of information in the multicarrier DSL systems is formulated. A genetic based algorithm is proposed to approach the solution. Its parameters are evaluated in order to carry out the crosstalk selection. (C) 2009 Elsevier GmbH. All rights reserved.

关键词： Coordinated DSL Crosstalk selection Discrete information allocation evolution algorithm

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Multiobjective PID control for a linear brushless DC motor: an evolutionary approach

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IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS 2002年第6期149卷 397-406页

作者： Lin, CL Jan, HY Feng Chia Univ Inst Automat Control Engn Taichung 40724 Taiwan

A robust output tracking control design for a linear brushless DC motor with modelling uncertainties is presented. Frequency-domain design specifications directly related to the mixed sensitivity function and control energy consumption are imposed to ensure stability and performance robustness. A generalised two-parameter PID control framework is developed via an evolution algorithm, which searches the available solutions over a certain specified domain. The proposed design paradigm is intuitive and practical in the sense that it offers an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, provides excellent tracking performance without resorting to excessive control. Experimental and numerical studies have been presented to confirm the proposed control design.

关键词： linear motors multiobjective PID control scheme three-term control robust output tracking control design machine theory control system analysis plant perturbation control performance control simulation stability linear brushless DC motor control system synthesis frequency-domain design specifications frequency-domain synthesis machine control evolution algorithm Linear machines mixed sensitivity function control energy consumption Control system analysis and synthesis methods Control of electric power systems brushless DC motors d.c. machines performance robustness robust control Stability in control theory modelling uncertainties machine testing

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Evolving Memristive Reservoir

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 13574-13588页

作者： Shi, Xinming Minku, Leandro L. Yao, Xin Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

In light of the dynamic plasticity, nanosize, and energy efficiency of memristors, memristive reservoirs have attracted increasing attention in diverse fields of research recently. However, limited by deterministic hardware implementation, hardware reservoir adaptation is hard to realize. Existing evolutionary algorithms for evolving reservoirs are not designed for hardware implementation. They often ignore the circuit scalability and feasibility of the memristive reservoirs. In this work, based on the reconfigurable memristive units (RMUs), we first propose an evolvable memristive reservoir circuit that is capable of adaptive evolution for varying tasks, where the configuration signals of memristor are evolved directly avoiding the device variance of the memristors. Second, considering the feasibility and scalability of memristive circuits, we propose a scalable algorithm for evolving the proposed reconfigurable memristive reservoir circuit, where the reservoir circuit will not only be valid according to the circuit laws but also has the sparse topology, alleviating the scalability issue and ensuring the circuit feasibility during the evolution. Finally, we apply our proposed scalable algorithm to evolve the reconfigurable memristive reservoir circuits for a wave generation task, six prediction tasks, and one classification task. Through experiments, the feasibility and superiority of our proposed evolvable memristive reservoir circuit are demonstrated.

关键词： Reservoirs Memristors Hardware Task analysis Signal processing algorithms Scalability Optimization evolution algorithm evolvable hardware memristor neural networks reservoir computing

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