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A Three-Phase Multilevel Inverter Synthesized with 31 Levels and Optimal gating Angles Based on the ga and GWO to Supply a Three-Phase Induction Motor

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ENERGIES 2024年第5期17卷 1267页

作者： Hussein, Taha Ahmad Ishak, Dahaman Tarnini, Mohamad Northern Tech Univ Dept Elect Power Engn Mosul 41002 Iraq USM Offshore Ctr TR-34500 Istanbul Turkiye Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Malaysia Beirut Arab Univ Dept Elect & Comp Engn Beirut 11072809 Lebanon

A three-phase multilevel inverter (MLI), synthesized with 31 levels in regard to its output voltage, is used to provide the AC supply to a three-phase, squirrel cage induction motor. The gating angles required for the 30 power switches on the MLI are optimized using both the genetic algorithm (ga) and the grey wolf optimizer (GWO), in which the optimal angles are determined through solving the trigonometric equations taken from Fourier analysis to target the minimum total harmonic distortion (THD) at the MLI output. A simulation model and an experimental prototype are developed for performance analysis and validation. The results demonstrate that the MLI is effectively able to produce 31 levels of three-phase AC output voltage, with the THD not exceeding 5% when loaded with a resistive load and a three-phase induction motor. The voltage and current are measured and recorded for different loads and operating conditions, including the amount of energy consumed by the load. The results of the frequency analysis demonstrate that most of the triple harmonics, which can harm the efficiency of the inverter, are cancelled.

关键词： DC/AC (inverter) power converter ga algorithm GWO algorithm induction motor

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A novel neural network ga-optimized controller for QoS support in wireless MACs

A novel neural network GA-optimized controller for QoS suppo...

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IEEE Asia Pacific Conference on Circuits and Systems

作者： Pajouhi, Zoha Fakhraie, Sied Mehdi Univ Tehran Sch Elect & Comp Engn Tehran 14395 Iran

ISBN: (纸本)9781424403868

As the multimedia applications such as voice and video streaming across the Internet emerge, there is a growing demand for Quality of Service (QoS) over different networks. Among different Internet-based networks, 802.11 Wireless Local Area Networks (WLAN) are among the most important. Two significant factors that affect multimedia quality over a wireless network are the packet loss and delay. While the packet toss is tolerable to some extent the delay plays a major role in the performance. In this paper an effective and computationally efficient neural network controller is proposed to optimize the IEEE 802.11 Medium Access Control Layer MAC behavior by adaptively adjusting the contention parameters. The neural network architecture and the weights in the network are optimized using genetic algorithm (ga) for high load situations. After this optimization, elaborate simulations are performed to show the effectiveness and efficiency of the method proposed.

关键词： WLAN ga algorithm QoS MAC

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Application of Nonlinear Model Predictive Control Method Based on ga for Northeastern Greenhouse in China 35

Application of Nonlinear Model Predictive Control Method Bas...

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35th Chinese Control and Decision Conference (CCDC)

作者： Wang, Yonggang Wang, Qi Chen, Ziqi Zhang, Ning Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Peoples R China

ISBN: (纸本)9798350334722

The purpose of the greenhouse system is to build a suitable microclimate environment for crop growth in northeastern China. The precise control of environmental factors is the key factor to ensure the high quality of crop growth. However, the actual greenhouse system is described as a complex dynamic characteristic with strong nonlinear, strong coupling, multiple disturbances. Therefore, the traditional PID control method is difficult to solve the above problems. In order to solve the above problems, this paper develops a microclimate dynamic model suitable for the northeastern greenhouse. Subsequently, the Nonlinear Model Predictive Control (NMPC) algorithm combines with ga algorithm is applied to the northeastern greenhouse. This scheme is applied to the control of temperature, humidity and carbon dioxide concentration in the greenhouse compared with the traditional PID control. The simulation results show that the nonlinear model predictive control based on ga algorithm has better control performance and stability. Furthermore, the proposed method can meet the actual demand.

关键词： Greenhouse temperature control humidity control Carbon dioxide concentration control ga algorithm Nonlinear model predictive control

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Optimization Design of Elastic Coupling in Swashplate Engine Based on BP-ga

Optimization Design of Elastic Coupling in Swashplate Engine...

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International Conference on Applied Mechnics and Mechanical Engineering

作者： Kong, Yanfeng Zhang, Zhenshan Cheng, Guangtao Naval Univ Engn Dept Weaponry Engn Wuhan 430033 Peoples R China

ISBN: (纸本)9780878492459

Aiming at the fact that the current method of designing elastic coupling in swashplate engine is deficient, and the vibration damping effect is inadequate, a new method for optimization design of coupling is put forward based on back propagation artificial neural network and genetic algorithm(BP-ga). Firstly the dynamics model of swashplate engine shafting is set up, and the samples are gained by numerical simulation, then the non-linear mapping relationship of elastic coupling designed parameters of the objective function is established with BP neural network, finally the trained network is called back by ga to make global optimization. The optimization results show that the global optimal resolution can be searched rapidly and correctly with the method, besides, the method is precise, convenient and applicable.

关键词： swashplate engine elastic coupling BP neural network ga algorithm

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Improving virtualization and migration in combinatorial dynamic mapping for cloud services

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第2期26卷 1511-1533页

作者： Mehlabani, Ehsan Gorjian Zhang, Chongqi Guangzhou Univ Dept Probabil & Stat Guangzhou 510006 Peoples R China

Cloud computing is a new approach to provide computing resources from infrastructure to software as a service which have many challenges. One of the most important challenges is pricing and resource allocation issues in the cloud environment. Static pricing schemes do not meet supply and demand, and therefore prompted researchers to look for a dynamic, low-overhead approach that could manage such situations. Usually two views are applied which are based on providing profit and benefit to the user and the provider. In cloud computing, resource availability and workload are dynamically changing, this behavior poses a challenge between users and providers, so resource allocation should be optimized. They are more biased towards providers. In this study, a meta-heuristic method named genetic algorithm is used for win-win allocation of virtual machine to the user of the cloud environment in a double-sided combinatorial auction. The purpose of this study is to provide a method for dynamic mapping of cloud services by combining multi-unit auction algorithm and genetic algorithm that tries to increase the profit of service providers and greater customer welfare, so that it can maximize the profit of service providers by reducing costs and on the other hand increase the level of customer satisfaction by providing some solutions.

关键词： ga algorithm Particle swarm optimization Virtual machine Virtual machine positioning Service providers gaMultiUnit

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Integration of evolutionary computation algorithms and new AUTO-TLBO technique in the speaker clustering stage for speaker diarization of broadcast news

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2017年第1期2017卷 1页

作者： Dabbabi, Karim Hajji, Salah Cherif, Adnen Univ Tunis El Manar Res Unity Anal & Proc Elect & Energet Syst Fac Sci Tunis Tunis 2092 Tunisia Univ Tunis El Manar Natl Sch Engn Tunis Res Lab Informat & Image Proc Tunis 2092 Tunisia

The task of speaker diarization is to answer the question "who spoke when?" In this paper, we present different clustering approaches which consist of Evolutionary Computation algorithms (ECAs) such as Genetic algorithm (ga), Particle Swarm Optimization (PSO) algorithm, and Differential Evolution (DE) algorithm as well as Teaching-Learning-Based Optimization (TLBO) technique as a new optimization technique at the aim to optimize the number of clusters in the speaker clustering stage which remains a challenging problem. Clustering validity indexes, such as Within-Class Distance (WCD) index, Davies and Bouldin (DB) index, and Contemporary Document (CD) index, is also used in order to make a correction for each possible grouping of speakers' segments. The proposed algorithms are evaluated on News Broadcast database (NDTV), and their performance comparisons are made between each another as well as with some well-known clustering algorithms. Results show the superiority of the new AUTO-TLBO technique in terms of comparative results obtained on NDTV, RT-04F, and ESTER datasets of News Broadcast.

关键词： Speaker diarization PSO algorithm ga algorithm DE algorithm TLBO technique EA algorithms Clustering validity index DER

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Construction of Polar Codes in Rayleigh Fading Channel

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IEEE COMMUNICATIONS LETTERS 2019年第3期23卷 402-405页

作者： Zhou, Dekun Niu, Kai Dong, Chao Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing 100876 Peoples R China

In this letter, we investigate the effective construction method of the polar codes in the Rayleigh fading channel (RFC) based on the gaussian approximation (ga) algorithm. For the RFC, two algorithms are proposed to find the equivalent binary input AWGN channel (BIAWGNC). The first one transforms the given RFC to an equivalent BIAWGNC with the same average mutual information, and then ga algorithm is performed to construct the polar codes. For the second one, the given RFC is handled as an equivalent BIAWGNC with an analytical expression in terms of the Kullback-Leibler divergence so as to further reduce the complexity of the first algorithm. Finally, the simulation results show the effectiveness of the proposed methods.

关键词： RFC ga algorithm average mutual information Kullback-Leibler divergence

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Optical Burst Routing by Balanced Wavelength Allocation Under Multi-objective Quality Metrics

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WIRELESS PERSONAL COMMUNICATIONS 2019年第2期107卷 1093-1114页

作者： Kumar, Veparala Kishen Ajay Reddy, Katam Suresh Prasad, Mahendra Giri JNTUA G Pulla Reddy Engn Coll Autonomous Dept Elect & Commun Engn Kurnool India JNT Univ Anantapur Dept Elect & Commun Engn Anantapuram India

The optical burst switching is promising optical network technique that intends to transmit data in the form of bursts with the ability of dynamic switching between sub-wave lengths. One among the crucial issues of the OBS networks is burst loss due to suboptimal and often impediment burst scheduling and wavelength allocation strategies. Hence, the major contributions of the contemporary research in regard to OBS networks entails to portray the optimal burst routing strategies. The contribution of this manuscript is balanced wavelength allocation using differential evolution method in short referred as BWADE, which selects an optimum multi-hop route that intends to uses multi-objective QoS aspects as a fitness scale for differential evolution algorithm. The experimental study concluding that the proposed BWADE outperforming the other contemporary routing approaches through simulation outcomes.

关键词： BWADE OBS network WDM-OBS transmission system JAVOBS ga algorithm DE approach

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GWO-Based Tuning of LQR-PID Controller for a 3-DOF Parallel Manipulator

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IETE JOURNAL OF RESEARCH 2023年第7期69卷 4378-4393页

作者： Choubey, Chandan Ohri, Jyoti Natl Inst Technol Elect Dept Kurukshetra Haryana India

This paper presents the mathematical modeling and optimal trajectory tracking control of a 3-degree-of-freedom parallel manipulator, commonly known as Maryland manipulator. Three unlike sequential trajectories are considered, and the trajectory-tracking control of a manipulator is performed by the LQR-PID controller. The tuning of LQR-PID is done by using the Grey Wolf Optimizer (GWO), a meta-heuristic method. The two other traditional benchmarking algorithms are Particle Swarm Optimizer (PSO) and Genetic algorithm (ga). The main goal of this work is to calculate the optimum values of LQR gain parameters i.e. Q and R matrices and PID controller's gains i.e. K-p, K-i and K-d using three different optimization algorithms, and their performance comparisons are highlighted in simulation results and discussion section. According to the obtained simulation results, the proposed GWO methodology is more efficient and more accurate in trajectory tracking control and generates optimal torques to the input links of the manipulator when compared with other evolutionary optimization algorithms such as PSO and ga.

关键词： ga algorithm GWO algorithm parallel manipulator PSO algorithm trajectory control tuning LQR-PID torques

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Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data

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APPLIED ENERGY 2016年 169卷 309-323页

作者： Ji, Ying Xu, Peng Duan, Pengfei Lu, Xing Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

One major obstacle in Heating, Ventilation and Air Conditioning (HVAC) system Fault Detection and Diagnostics (FDD), retrofitting and energy performance evaluation is the lack of detailed hourly cooling load data. Cooling load measurement in commercial buildings is expensive and sometimes very difficult to implement. Detailed building simulation models, such as EnergyPlus, are too complicated to build and also must be calibrated. In this paper, an hourly cooling load prediction model, called the "RC-S" model, is proposed. This new cooling load calculation approach consists of a simplified thermal network model of the building envelope, a thermal network model for the building internal mass and the internal cooling load model from the submetering system. One existing RC model is introduced as reference model and three types of "RC-S" models are set up in this study. Genetic algorithm (ga) is selected to optimize the parameters in those models. Measurement data collected from a real commercial building and simulation data obtained from EnergyPlus model of the same commercial building are used to train and test the four models. The results prove that the proposed "RC-S" cooling load calculation method is more accurate than the existing RC model and much simpler than whole building simulation models. It can provide reasonable estimations of cooling loads for HVAC FDD and other performance evaluations. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cooling load estimation Thermal network model Electricity submetering data ga algorithm Parameter optimization

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