Study on Optimal-Power Ratio Optimization of Automatic Transmission

2014 ◽  
Vol 986-987 ◽  
pp. 1151-1154
Author(s):  
Feng Ping Cao
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Numerical Models ◽  
Automatic Transmission ◽  
Power Ratio ◽  
Car Model ◽  
Optimal Power ◽  
Output Torque ◽  
Dynamic Target ◽  
Ratio Optimization

Regards the compound auto transmission as the object of research, an optimal-power ratio optimization algorithm that takes into account the efficiency of CVT was presented in the paper. Firstly, the numerical models of drivetrain were established, which included the engine output torque model and the efficiency model of CVT. Then, the driving power was chosen as the dynamic target, and the optimized objective function was built. According to the requirement of shift gear, the ratio of shuttle transmission components was determined. Based on the Simulation X, the simulation car model with the compound auto transmission was design to confirm the validity and effectiveness of the proposed method.

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

Wide-Band Modeling of the IGBT Module Based on the Physical Structure

2014 ◽  
Vol 986-987 ◽  
pp. 1954-1957
Author(s):  
Hai Feng Sun ◽  
Xiao Ming Wu ◽  
Chen Da Zheng ◽  
Xiao Qian Liu
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Physical Meaning ◽  
Wide Band ◽  
Physical Structure ◽  
High Accuracy ◽  
Suitable Model ◽  
Igbt Module ◽  
Distribution Parameters ◽  
Iterative Optimization Algorithm

This paper presents a new modeling method, based on the physical structure of the IGBT module, considering the distribution parameters of high frequency.This method is simple and the wideband model has physical meaning, by choosing suitable model initial parameters as well as the objective function which use the iterative optimization algorithm to solve the model parameters.Comparing the wideband model with the measured results ,the wideband model maintain high accuracy in the range of 100KHz ~ 20MHz.

Geometric Optimization of Concentrating Solar Collectors using Monte Carlo Simulation

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
A. J. Marston ◽  
K. J. Daun ◽  
M. R. Collins
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stochastic Programming ◽  
Objective Function ◽  
Sample Size ◽  
Optimization Algorithm ◽  
Geometric Optimization ◽  
Trial And Error ◽  
Solar Collectors ◽  
Step Size

This paper presents an optimization algorithm for designing linear concentrating solar collectors using stochastic programming. A Monte Carlo technique is used to quantify the performance of the collector design in terms of an objective function, which is then minimized using a modified Kiefer–Wolfowitz algorithm that uses sample size and step size controls. This process is more efficient than traditional “trial-and-error” methods and can be applied more generally than techniques based on geometric optics. The method is validated through application to the design of three different configurations of linear concentrating collector.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

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