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2014 International Conference on Advanced Control, Automation and Robotics (ACAR2014)

作者： R.Irakoze K.Yakoub A.Kaddouri GRETER Research Group Department of Electrical EngineeringUniversity of Moncton

ISBN: (纸本)9781784660468

This paper deals with the identification of a piezoelectric actuator model parameters considering Coleman–Hodgdon modeling. The identification is based on two artificial intelligence techniques: the particle swarm optimization and the genetic algorithm techniques. The identification results for both techniques are presented and discussed. A comparison between the two techniques is performed using high-precision PI actuator.

关键词： Coleman–Hodgdon model systems identification particle swarm optimization technique genetic algorithm piezoelectric actuator

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Selective Harmonic Elimination In 5-Level MLI Using particle swarm optimization 4

Selective Harmonic Elimination In 5-Level MLI Using Particle...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Verma, Vikas Kumar Meena, Hemant Kumar Sandeep, N. Mnit Jaipur Electrical Engineering Jaipur India

ISBN: (纸本)9798350343670

Multilevel inverters are highly capable to obtaining higher power ratings and higher output voltage waveforms by their modular multilevel structure. Currently, multilevel inverter applications is increasing in the various industries and renewable energy sources. High performance, modular design, and low harmonic distortion in voltage waveform are the most remarkable characteristics of multilevel inverters. The paper proposes a PSO algorithm for selective harmonic elimination in 5-level multilevel inverters. The algorithm consists of particle swarm optimization (PSO) to obtain optimized firing angles, resulting Elimination of 3rd harmonics from the system witch is shown by FFT analysis, it also provides lower total harmonic distortion (THD). © 2024 IEEE.

关键词： Cascade H-Bridge Multilevel inverters particle swarm optimization technique Selective Harmonic Elimination Total Harmonic Distortion

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Hybrid optimization technique for Enhancing the Stability of Inverted Pendulum System

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INTERNATIONAL JOURNAL OF swarm INTELLIGENCE RESEARCH 2021年第1期12卷 77-97页

作者： Mousa, M. E. Ebrahim, M. A. Zaky, Magdy M. Saied, E. M. Kotb, S. A. Egyptian Atom Energy Author Cairo Egypt Benha Univ Fac Engn Shoubra Cairo Egypt Higher Technol Inst HTI 10th Of Ramadan Egypt

The inverted pendulum system (IPS) is considered the milestone of many robotic-based industries. In this paper, a new variant of variable structure adaptive fuzzy (VSAF) is used with new reduced linear quadratic regulator (RLQR) and feedforward gain for enhancing the stability of IPS. The optimal determining of VSAF parameters as well as Q and R matrices of RLQR are obtained by using a modified grey wolf optimizer with adaptive constants property via particle swarm optimization technique (GWO/PSO-AC). A comparison between the hybrid GWO/PSO-AC and classical GWO/PSO based on multi-objective function is provided to justify the superiority of the proposed technique. The IPS equipped with the hybrid GWO/PSO-AC-based controllers has minimum settling time, rise time, undershoot, and overshoot results for the two system outputs (cart position and pendulum angle). The system is subjected to robustness tests to ensure that the system can cope with small as well as significant disturbances.

关键词： Grey Wolf Optimizer Inverted Pendulum System particle swarm optimization technique Reduced Linear Quadratic Regulator Variable Structure Adaptive Fuzzy

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A Comprehensive Study on Adaptive MPPT Control techniques for Efficient Power Generation

A Comprehensive Study on Adaptive MPPT Control Techniques fo...

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International Conference on Advancement in Materials, Manufacturing and Energy Engineering, ICAMME 2021

作者： Dangi, Pushpendra Ojha, Amit Singh, Shiv Pratap Gawre, Suresh Kr. Meshram, Sangharsh Mittal, Arvind Depertment of Electrical Engineering MANIT Bhopal India Energy Centre MANIT Bhopal India

ISBN: (纸本)9789811653704

The variable conditions like partial shading, changing irradiance, irregular load lead to reduction of efficiency of solar PV systems and because of this lower proficiency to convert and expensiveness of solar photovoltaic, it is requisite to extract the power at maximum power point, and hence, this paper presents a study on various adaptive control techniques used for impedance matching and tracking of power generation from solar PV so to reach and hold at maximum power point, minimize the effects of nonlinearities and performance improvement. The aim of the study is to address the advances made in this field for the future research purposes. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization technique Artificial neural network technique Maximum power point tracking (MPPT) particle swarm optimization technique Perturbation and observation (P&O) Renewable energy Solar photovoltaic (PV) array

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An EPQ model with stock and selling price dependent demand and variable production rate in interval environment

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2020年第2期11卷 385-399页

作者： Ruidas, Subhendu Seikh, Mijanur Rahaman Nayak, Prasun Kumar Kazi Nazrul Univ Dept Math Asansol 713340 W Bengal India Midnapore Coll Autonomous Dept Math Midnapore 721101 India

The motto of this paper is to develop a production inventory model in real life situation. In reality, demand of a certain product becomes highly effected by on-hand stock level in stores and the selling price of the product. In the proposed model, demand of the product is dependent on these two vital deceive factors. In real life production systems, always there are some defective products which requires reworking in order to make them useful. The possibility of producing some defective items in regular production process and their reworking has been taken into account in the model. In case of inventories of highly demandable products, it is observed that production rate is proportional to demand. The situation also arises in case of launching a new product or in a multi-stage production system. In this model, production rate is a variable and it varies with the demand rate. Shortages are allowed and it is backlogged fully. Based on these assumptions, several researchers worked on but they considered that the associated inventory cost parameters are fixed real numbers. However, these are not fixed in reality and may vary time to time depending upon some scenario. Main objective of the paper is to develop a production inventory model under those assumptions considering various cost parameters as interval numbers. As a result the corresponding optimization problem is also interval valued. Quantum behaved particle swarm optimization technique has been applied to find the maximum profit in a single cycle. Numerical example and sensitivity analysis are given to illustrate the proposed inventory model.

关键词： Production inventory model Stock dependent demand Interval number Ranking of interval numbers particle swarm optimization technique

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Optimal generation scheduling for a deep-water semi-submersible drilling platform with uncertain renewable power generation and loads

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ENERGY 2019年第0期181卷 897-907页

作者： Huang, Yuqing Lan, Hai Hong, Ying-Yi Wen, Shuli Yin, He Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Chung Yuan Christian Univ Dept Elect Engn Taoyuan 320 Taiwan

Owing to the depletion of oil reserves and the low efficiency of traditional deep-water semi-submersible drilling platforms, the application of photovoltaic generation and energy storage systems in marine power systems has been increasingly attracting attention. However, the intermittent and uncertain nature of solar energy brings crucial challenges in maintaining stable and sustainable operations. Furthermore, unlike land-based power systems, the deep-water semi-submersible drilling platform depends on a dynamic positioning system to be fixed in an area of deep water for producing oil, but irregular and stochastic waves make the outputs of a dynamic positioning system uncertain, increasing the difficulty and complexity of energy management. This paper develops a novel hybrid day-ahead probabilistic scheduling method to reduce the total fuel cost of the whole system and improve the energy efficiency;the method combines Taguchi's orthogonal algorithm with particle swarm optimization and a probabilistic load flow method. A detailed model of the deep-water semi-submersible drilling platform is established to validate the proposed method, which is compared with various classical methods. With the help of the proposed method, the total operation cost and greenhouse gas emissions are reduced from $7562.52 to $7333.68, and from 18.95 tons to 16.81 tons, respectively. Furthermore, compared to the two-point estimation method, the experimental times drops by half, which minimizes the computational burden. The simulation results clearly demonstrate the necessity for day-ahead energy scheduling, and the advantages of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Deep-water semi-submersible drilling platform Photovoltaic generation Day-ahead scheduling Taguchi orthogonal algorithm particle swarm optimization technique

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Rock Slope Analysis with Nonlinear Hoek-Brown Criterion Incorporating Equivalent Mohr-Coulomb Parameters

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2019年第6期37卷 4741-4757页

作者： Kumar, Vinay Himanshu, Navneet Burman, Avijit NIT Patna Dept Civil Engn Geotech Engn Patna Bihar India

In the present work, the results of rock slope analyses incorporating nonlinear Hoek-Brown yield criterion with equivalent Mohr-Coulomb strength parameters to simulate rock mass behaviour are presented. The factor of safety (FOS) of rock slopes are calculated using Morgenstern-Price and Spencer's method. A simplified method of generating non-circular segmented failure surface is proposed. The search of minimum FOS and associated critical non-circular failure mass for rock slopes is performed by particle swarm optimization (PSO) technique. A MATLAB code is developed incorporating nonlinear Hoek-Brown criteria with equivalent Mohr-Coulomb strength parameters and coupled with PSO to analyze stability of rock slopes. The efficiency and accuracy of the developed MATLAB code is established by comparing the obtained results with those from existing literatures. Furthermore, the analyses of slopes are performed with varying height and slope angle for an intact rock slope with varying material parameters such as Hoek-Brown constant mi and uniaxial compressive strength sigma ci for rock material. It is found that the FOS value follows a decreasing pattern if height and the slope angles are increased.

关键词： Nonlinear Hoek-Brown yield criterion Linear equivalent Mohr-Coulomb strength parameters Geological strength index Morgenstern-Price method Spencer's method particle swarm optimization technique

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Integer-PSO: a discrete PSO algorithm for task scheduling in cloud computing systems

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EVOLUTIONARY INTELLIGENCE 2019年第2期12卷 227-239页

作者： Beegom, A. S. Ajeena Rajasree, M. S. Coll Engn Dept Comp Sci & Engn Trivandrum Kerala India Govt Engn Coll Barton Hill Trivandrum Kerala India

Cloud computing is an emerging technology that changes the computing world through its power to serve the need of any user who requires better computing power over the Internet. For this environment the end user may want to have a better Quality of Service at low cost and cloud service providers have a different goal of achieving maximum profit and minimal management overhead. Task scheduling is a challenging task in this scenario to meet the requirements of both the ends. This work proposes a discrete version of the particle swarm optimization (PSO) algorithm, namely Integer-PSO, for task scheduling in the cloud computing environment which can be used for optimizing a single objective function and multiple objective functions as well. Experimental studies on different types of task set characterising normal traffic and bursty traffic in the cloud computing environment shows that this approach is better, have good convergence and load balancing.

关键词： Cloud computing Task scheduling particle swarm optimization technique Integer-PSO

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Uniaxial and biaxial ratcheting behavior of pressurized AISI 316L pipe under cyclic loading: Experiment and simulation

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2020年 179卷 105693-105693页

作者： Moslemi, N. Mozafari, F. Abdi, B. Gohery, Scott Redzuan, N. Burvill, Colin Ayob, A. Univ Teknol Malaysia UTM Sch Mech Engn Skudai 81310 Malaysia Univ Kebangsaan Malaysia Dept Mech & Mat Engn Bangi 43600 Malaysia Ulster Univ Shore Rd Belfast BT37 0QB Antrim North Ireland Univ Melbourne Dept Mech Engn Parkville Vic 3010 Australia

Ratcheting defined as the progressive accumulation of plastic strain occurring during cyclic loading in the presence of the mean stress is one of the most prevalent failure modes in engineering structures. Experimental studies were conducted to characterize the uniaxial and biaxial ratcheting responses of an AISI 316L pipe. Experimental results show that an obvious cyclic hardening occurs in the AISI 316L pipe under uniaxial strain loading. Uniaxial ratcheting rate obtained from the axial cyclic experiment reaches a quasi-steady rate after a certain number of loading cycles. Moreover, using a designed four-point bending experimental setup, different axial stress amplitudes in the presence of the constant hoop stress were considered to characterize the biaxial ratcheting response of the material. The ratcheting strain and ratcheting strain rate in the hoop direction increase with increasing axial stress amplitudes under the constant hoop stress. To simulate the uniaxial and biaxial ratcheting behavior of the AISI 316L pipe, the Chaboche nonlinear kinematic hardening model was used. Using the particle swarm optimization (PSO) technique, parameters of the Chaboche model were identified efficiently from the monotonic response of a cold-worked sample. The elastic limit of the material was measured using a very careful quasi-static cyclic compression experiment. The procedure to calibrate the material parameters for the Chaboche model and finite element simulation results were validated well with experimental data for the AISI 316L pipe. It is shown that the Chaboche model with the suitably-calibrated parameters from the experimental study can be applied to rigorously predict the ratcheting behavior of the AISI 316L pipe under cyclic uniaxial and biaxial loading conditions.

关键词： Ratcheting Chaboche parameters particle swarm optimization technique Finite element analysis Experimental investigation Cyclic loading

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A Clustering Approach using PSO optimization technique for Data Mining 6

A Clustering Approach using PSO Optimization Technique for D...

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6th International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Dagde, Rashmi Radke, Dipeeka Lokhande, Ashwini Priyadarshini Bhagwati Coll Engn Dept Comp Sci & Engn Nagpur Maharashtra India Priyadarshini Bhagwati Coll Engn Dept Informat Technol & Engn Nagpur Maharashtra India

ISBN: (纸本)9789380544342

The cluster analysis is incredibly huge conception of information mining several researchers square live offer attention on cluster downside. The cluster is member of unattended learning technique during that teacher is absent. throughout this analysis primarily based paper we've got developed the efficient hybrid data processing algorithmic rule. The obtained algorithmic rule known as as BKPSO that contain the Bisecting K-mean algorithmic rule and particle swarm optimization algorithmic rule. By victimization this 2 algorithmic rule the hybrid model can get. This hybrid model will increase the accuracy of clustering. The Bisecting K-mean algorithmic rule is use for the clustering. it'll kind the cluster suggests that organizing the article into cluster according their price that is analogous in how. The bisecting K-mean algorithmic rule is modification of the fundamental K-mean algorithmic rule. {it can vertical bar it'll} cut the most cluster into 2 sub clusters till the K-cluster will kind. It reduced the computation time and additionally reduces the complexness of the information cluster. it's a drawback like initializing the beginning cluster center. to beat this downside and realize the optimum path the particle swarm optimization rule is used throughout this analysis based mostly project the information sets square measure transfer from the UCI Repository. it's one among the general public analysis based mostly University of Golden State in Irvine.

关键词： UCI Repository data(Car machine Learning Data set Iris Plants Database) K-mean Bisecting K-mean algorithm particle swarm optimization technique

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