A regression analysis based heuristic method to the power transformer design optimization problem

2011 ◽  
Author(s):  
Zhen Zhang ◽  
Zaiping Pan ◽  
Xiaohong Pan
Keyword(s):  
Regression Analysis ◽  
Design Optimization ◽  
Optimization Problem ◽  
Power Transformer ◽  
Heuristic Method ◽  
Transformer Design

scholarly journals A Simulated Annealing Approach for the Train Design Optimization Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Federico Alonso-Pecina ◽  
David Romero
Keyword(s):  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Ad Hoc ◽  
Heuristic Method ◽  
Combinatorial Problem ◽  
Second Step ◽  
Railway Network ◽  
Optimal Decisions ◽  
Initial Feasible Solution

The Train Design Optimization Problem regards making optimal decisions on the number and movement of locomotives and crews through a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In a mathematical programming formulation, the objective function to minimize is composed of the costs associated with the movement of locomotives and cars, the loading/unloading operations, the number of locomotives, and the crews’ return to their departure stations. The constraints include upper bounds for number of car blocks per locomotive, number of car block swaps, and number of locomotives passing through railroad segments. We propose here a heuristic method to solve this highly combinatorial problem in two steps. The first one finds an initial, feasible solution by means of an ad hoc algorithm. The second step uses the simulated annealing concept to improve the initial solution, followed by a procedure aiming to further reduce the number of needed locomotives. We show that our results are competitive with those found in the literature.

Implementation of tree pruning method for power transformer design optimization

2018 ◽  
Vol 29 (1) ◽  
pp. e2659 ◽  
Author(s):  
Ali Soldoozy ◽  
Abdolreza Esmaeli ◽  
Hamidreza Akbari ◽  
Seyed Ziya Mazloom
Keyword(s):  
Design Optimization ◽  
Power Transformer ◽  
Pruning Method ◽  
Transformer Design ◽  
Tree Pruning

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

scholarly journals FEM Based Preliminary Design Optimization in Case of Large Power Transformers

2019 ◽  
Author(s):  
Tamás Orosz ◽  
David Pánek ◽  
Pavel Karban
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Optimization Problems ◽  
Optimization Method ◽  
Power Transformers ◽  
Design Stage ◽  
Preliminary Design ◽  
Large Power ◽  
Custom Made ◽  
Transformer Design

Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm.

scholarly journals Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

2021 ◽  
Author(s):  
Yoshihiro Kanno
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Structural Reliability ◽  
Reliability Based Design Optimization ◽  
Input Distribution ◽  
Reliability Based Design ◽  
Optimal Value ◽  
Reliability Constraint ◽  
Distributionally Robust ◽  
Robust Reliability

AbstractThis study considers structural optimization under a reliability constraint, in which the input distribution is only partially known. Specifically, when it is only known that the expected value vector and the variance-covariance matrix of the input distribution belong to a given convex set, it is required that the failure probability of a structure should be no greater than a specified target value for any realization of the input distribution. We demonstrate that this distributionally-robust reliability constraint can be reduced equivalently to deterministic constraints. By using this reduction, we can handle a reliability-based design optimization problem under the distributionally-robust reliability constraint within the framework of deterministic optimization; in particular, nonlinear semidefinite programming. Two numerical examples are solved to demonstrate the relation between the optimal value and either the target reliability or the uncertainty magnitude.

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