Simulation and Optimization of Asynchronous AC Motor Control by Particle Swarm Optimization (PSO) and Emperor Algorithm

2011 ◽  
Author(s):  
Masoud Nabipour Afrozi ◽  
Masoud Hassanpour Aghdam ◽  
Ahmad Naebi ◽  
Saeed Hassanpour Aghdam
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Control ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Simulation And Optimization ◽  
Ac Motor

Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Majid Siavashi ◽  
Mohammad Rasoul Tehrani ◽  
Ali Nakhaee
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Oil Recovery ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Full Time ◽  
Well Placement ◽  
Swarm Optimization ◽  
Simulation And Optimization ◽  
Optimal Well Placement

One of the main reservoir development plans is to find optimal locations for drilling new wells in order to optimize cumulative oil recovery. Reservoir simulation is a necessary tool to study different configurations of well locations to investigate the future of the reservoir and determine the optimal places for well drilling. Conventional well-known numerical methods require modern hardware for the simulation and optimization of large reservoirs. Simulation of such heterogeneous reservoirs with complex geological structures with the streamline-based simulation method is more efficient than the common simulation techniques. Also, this method by calculation of a new parameter called “time-of-flight” (TOF) offers a very useful tool to engineers. In the present study, TOF and distribution of streamlines are used to define a novel function which can be used as the objective function in an optimization problem to determine the optimal locations of injectors and producers in waterflooding projects. This new function which is called “well location assessment based on TOF” (WATOF) has this advantage that can be computed without full time simulation, in contrast with the cumulative oil production (COP) function. WATOF is employed for optimal well placement using the particle swarm optimization (PSO) approach, and its results are compared with those of the same problem with COP function, which leads to satisfactory outcomes. Then, WATOF function is used in a hybrid approach to initialize PSO algorithm to maximize COP in order to find optimal locations of water injectors and oil producers. This method is tested and validated in different 2D problems, and finally, the 3D heterogeneous SPE-10 reservoir model is considered to search locations of wells. By using the new objective function and employing the hybrid method with the streamline simulator, optimal well placement projects can be improved remarkably.

scholarly journals Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 159
Author(s):  
Guoyong Su ◽  
Pengyu Wang ◽  
Yongcun Guo ◽  
Gang Cheng ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Control ◽  
Simulated Annealing ◽  
Adaptive System ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Reference ◽  
Model Reference Adaptive System ◽  
Model Reference Adaptive

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

Simulation and Optimization of an Actuator

2015 ◽  
Vol 1099 ◽  
pp. 110-119
Author(s):  
Norelislam El Hami ◽  
Mhamed Itmi ◽  
A. El Hami
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Numerical Models ◽  
Limit State ◽  
Particle Swarm ◽  
Rectangular Plates ◽  
Swarm Optimization ◽  
Simulation And Optimization ◽  
Reliability Methods ◽  
State Functions

This paper presents a new methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing. The reliability analysis procedure couple traditional and modified first and second order reliability methods, in rectangular plates modelled by an Assumed Modes approach. Both reliability methods are applicable to the implicit limit state functions through numerical models, like those based on the Assumed Mode Method. In modified approaches, the algorithms are based on heuristic optimization methods such as Particle Swarm Optimization and Simulated Annealing Optimization. Numerical applications in static, dynamic and stability problems are used to illustrate the applicability and effectiveness of proposed methodology. The results of example show that the predicted reliability levels are accurate to evaluate simultaneously various implicit limit state functions with respect to static, dynamic and stability criterions.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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