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General Meeting of the IEEE-Power-and-Energy-Society

作者： Al-Hinai, A. Ai-Badi, A. Sultan Qaboos Univ Dept Elect & Comp Engn Muscat 123 Oman

ISBN: (纸本)9781424419050

Axial-flux, Permanent-magnet machines are used in different applications where the power and torque density requirements are very high. These machines have simple structures, relatively high efficiencies and low cost. The parameters of these machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of particle swarm optimization (PSO) technique to predict the self and mutual inductances of the 11-phase Torus machine automatically.

关键词： Torus machine Parameter Identification particle swarm optimization technique

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Identification of Colman–Hodgdon hysteresis model of a piezoelectric actuator using particle swarm optimization technique and genetic algorithm

Identification of Colman–Hodgdon hysteresis model of a piez...

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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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PS trained ANN-based differential protection scheme for power transformers

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NEUROCOMPUTING 2008年第4-6期71卷 904-918页

作者： Geethanjali, M. Slochanal, S. Mary Raja Bhavani, R. Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India

In this paper, a novel scheme is proposed which monitors and discriminates the different operating conditions (normal, magnetizing inrush current. over excitation of the core, internal faults and CT saturation due to external faults) of power transformers. The particle swarm optimization (PSO) technique is used to train the multi-layered feed forward neural networks to discriminate the different operating conditions. The proposed scheme has been realized through two different Artificial Neural Network (ANN) architectures. One is used as an internal fault detector and the other one detects and discriminates the other operating conditions like normal, inrush, over excitation, and CT saturation due to external faults. These two ANNs were trained using Back Propagation Neural Network Algorithm (BPN) and PSO technique and the simulated results are compared. Simulations are performed for the practical power transformer ratings obtained from Tamilnadu Electricity Board (TNEB), India. Comparing the simulated results of the above two cases, training the neural network by PSO technique gives more accurate (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) results than BPN. The PSO trained ANN-based differential protection scheme provides faster, accurate, more secured and dependable results for power transformers. (c) 2007 Elsevier B.V. All rights reserved.

关键词： power transformer differential protection artificial neural network (ANN) particle swarm optimization technique

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Nonlinear multi-variable optimization of layered composites with nontraditional interfaces

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2015年第5期52卷 991-1000页

作者： Shabana, Yasser M. Elsawaf, A. E JUST Sch Innovat Design Engn Alexandria 21934 Egypt Helwan Univ Fac Engn Mech Design Dept Cairo Egypt

This article deals with a nonlinear multi-variable optimization problem to minimize the critical thermal stress induced in a layered composite plate. The numerical model is solved by using the finite element analysis while the optimization is done by applying the particle swarm optimization technique. The plate contains nontraditional interfaces between the layers while their profiles follow a power law. Different parameters are optimized either individually or in combination by applying single and multi-variable optimizations so as to minimize the critical induced thermal stress in the structure. These optimized parameters include the interface profile parameter, the height of the nontraditional interface on the right surface relative to the mid-surface, and the thicknesses of the layers of the plate. It is found that the critical stress can be minimized greatly when tailoring the geometrical and interface parameters by using their optimum values.

关键词： Stress reduction Layer composites Nontraditional interfaces particle swarm optimization technique Finite element analysis

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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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Optimal setting of FACTS devices using particle swarm optimization for ATC enhancement in deregulated power system

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IFAC-PapersOnLine 2016年第1期49卷 450-455页

作者： Bavithra, K. Raja, S. Charles Venkatesh, P. Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai Tamilnadu625015 India

In electricity supply industries, the word deregulation is disassembly of original structure and reassembly into another form for better efficiency, performance and to draw private sector investment. The major concerns in deregulated power market are transmission pricing, congestion management and Available Transfer Capability (ATC) that need to be dogged. Among these intriguing challenges, one that needs to be interrogated to provide assured open access transmission service is ATC. The way of improving ATC is either adding transmission facilities or using various FACTS devices. In this paper, our concern is incorporation of FACTS, a semiconductor based control devices to enhance the ATC of transmission system. Various FACTS devices like STATCOM, SSSC and UPFC are modeled by power flow equations and optimally set the FACTS parameters by particle swarm optimization (PSO) technique in such a way so as to enhance ATC in the deregulated electricity environment. This replaces the audacious job of erecting new transmission system or expanding the existing one. The non iterative, simple and fast Power Transfer Distribution Factors (PTDF) based sensitivity method is adopted to determine and enhance ATC on sample six bus system to prove the feasibility of ATC enhancement for bilateral and multilateral transactions. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Flexible AC transmission systems Deregulation Economics Electric control equipment Electric load flow Energy transfer Iterative methods particle swarm optimization (PSO) Power control Semiconductor devices Ac power Available transfer capabilities (ATC) Deregulated power systems Facts modeling particle swarm optimization technique Power transfer distribution factors Private sector investments Sensitivity slope

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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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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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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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