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Evaluation and improvement of material removal rate with good surface quality in TC4 blisk blade polishing process

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2018年第4期12卷

作者： Chen, Zhen Shi, Yaoyao Lin, Xiaojun Northwestern Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China

As a kind of efficient and rapid developed surface processing technique, polishing technology is widely used to improve the surface quality in the precision manufacturing of aero-engine blisk blades. Since blisk blade polishing is a metal removal process which aims to produce smooth surfaces with very low surface roughness, it is difficult to achieve the optimum balance between high material removal rate and low surface roughness. To solve this problem, this study investigates the process parameter optimization to improve the material removal rate with high surface quality in the belt flapwheel polishing of TC4 aero-engine blisk blades. The experiment was designed by Box-Behnken design (BBD) theory of response surface methodology. Then the prediction model of material removal rate was built based on the linear regression analysis of experimental data. To improve material removal rate on the condition that surface roughness meets technical requirements, the single-objective constrained optimization problem was solved utilizing the particle swarm optimization (PSO) technique. Finally, the feasibility of this method was experimentally verified. This study provides a theoretical and experimental reference for the evaluation and improvement of material removal rate with good surface quality in TC4 blisk blade polishing process.

关键词： Aero-engine blisk blade Belt flapwheel polishing Material removal rate Surface quality particle swarm optimization technique Parameter optimization

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particle swarm optimization trained recurrent neural network for voltage instability prediction

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Journal of Electrical Systems and Information Technology 2018年第2期5卷 216-228页

作者： Amr M. Ibrahim Noha H. El-Amary Electrical Power and Machines Engineering Dept. Ain Shams University Cairo Egypt Electrical Power and Control Engineering Dept. Arab Academy for Science and Technology Egypt

Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performance of recurrent neural network (RNN) in voltage instability prediction. A recurrent neural network trained with particle swarm optimization (PSO) is proposed in this paper. The proposed method is examined on 14-bus and 30-bus IEEE standard systems. These systems are simulated using MATLAB/Power System Toolbox program. Also, a detailed comparison between PSO algorithm and Backpropagation (BP) algorithm is discussed. The results proved the effectiveness of the proposed method.

关键词： Backpropagation algorithm particle swarm optimization technique Recurrent neural network Voltage instability predictor Voltage stability

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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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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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Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and particle swarm optimization

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2015年第5期23卷 667-683页

作者： Ahmed, Troudi Mohamed, Bouzbida Abdelkader, Chaari Higher Sch Sci & Tech Tunis ESSTT Res Unit C3S Tunis Tunisia

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi-Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and particle swarm optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

关键词： Nonlinear systems fuzzy identification fuzzy clustering particle swarm optimization technique stochastic environment

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Parameters Identification of 11-Phase Torus Generator Using particle swarm optimization technique

Parameters Identification of 11-Phase Torus Generator Using ...

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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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On Tracking Control for a Class of Network-Based Linear Systems

On Tracking Control for a Class of Network-Based Linear Syst...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Zhang, Dawei Han, Qing-Long Cent Queensland Univ Ctr Intelligent & Networked Syst Rockhampton Qld 4702 Australia Cent Queensland Univ Sch Informat & Commun Technol Rockhampton Qld 4702 Australia

ISBN: (纸本)9781424452262

This paper investigates the problem of tracking control for a class of linear networked control systems (NCSs). A linear system with interval time-varying delay is employed to describe the NCSs in consideration of both network-induced delay and packet dropout. An LMI-based sufficient condition on the existence of a network-based controller which guarantees an H-infinity-tracking performance is derived by a new discretized Lyapnov functional. Based on the stability condition and the particle swarm optimization technique, an algorithm is presented to design the tracking controller. Two examples show the effectiveness of the proposed methods.

关键词： Hâ?tracking performance LMI Lyapunov methods control engineering computing delays discretized Lyapnov functional interval time varying delay linear matrix inequalities linear networked control systems linear systems network induced delay networked control systems packet dropout particle swarm optimisation particle swarm optimization technique time-varying systems tracking control

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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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Modified particle swarm optimization models for production cost minimization in deregulated power system

Modified particle swarm optimization models for production c...

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42nd International Universities Power Engineering Conference

作者： El Ela, A. A. Abou El-Sehiemy, R. A. Minoufiya Univ Dept Elect Engn Fac Engn Shibin Al Kawm Minoufiya Egypt

ISBN: (纸本)9781905593361

This paper presents different modified particle swarm optimization (PSO) models for optimal market payment problem. The suggested PSO models use more particles' information to control the global optimal solution search. Ail adaptive strategy is proposed for choosing PSO parameters to assure the convergence of the proposed models. Also, the proposed models attempt to generate initial population that are scattered uniformly over the feasible solution space that is leads to the capable for obtaining higher quality solutions. The simulation results show that the proposed models prove practical for modelling the impact of different emergency conditions on the expected generation payment under the competitive environment.

关键词： particle swarm optimization technique sensitivity analysis production cost deregulated power systems

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Different transmission loss allocation schemes in deregulated power markets

Different transmission loss allocation schemes in deregulate...

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42nd International Universities Power Engineering Conference

作者： El Ela, A. A. Abou El-Sehiemy, R. A. Minoufiya Univ Fac Engn Dept Elect Engn Shibin Al Kawm Minoufiya Egypt Osman Ahmed Osman & Co Shibin Al Kawm Minoufiya Egypt

ISBN: (纸本)9781905593361

This paper suggests different Transmission Loss Allocation (TLA) schemes. The impact of different network users in deregulated power markets is considered. The market payment model considers the TLA as a constraint added to other system constraints. An optimal TLA scheme is proposed, beside the suggested TLA schemes, using particle swarm optimization technique. The TLA at different market conditions for different test systems are presented to show the capability of the suggested schemes. The proposed methodology is compatible with Independent System Operator (ISO) evaluation for obtaining responsible economic signals. Then, ISO can achieve an early detection of market equilibrium through the proposed scheme.

关键词： Transmission Loss Allocation Electricity Market Payment particle swarm optimization technique

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