Robust Global Optimization of Electromagnetic Devices With Uncertain Design Parameters: Comparison of the Worst Case Optimization Methods and Multiobjective Optimization Approach Using Gradient Index

2013 ◽  
Vol 49 (2) ◽  
pp. 851-859 ◽  
Author(s):  
Ziyan Ren ◽  
Minh-Trien Pham ◽  
Chang Seop Koh
Keyword(s):  
Global Optimization ◽  
Multiobjective Optimization ◽  
Optimization Methods ◽  
Design Parameters ◽  
Optimization Approach ◽  
Gradient Index ◽  
Worst Case ◽  
Electromagnetic Devices

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

Life Augmentation of Turbine Exhaust System Compensators Through Integrated MADM Optimization Approach of Stress Based Fatigue Cycles

2021 ◽  
Author(s):  
Nitin D. Pagar ◽  
Amit R. Patil
Keyword(s):  
Exhaust System ◽  
Optimization Methods ◽  
Fractional Factorial ◽  
Design Parameters ◽  
Optimization Approach ◽  
Auxiliary Equipment ◽  
Maximum Expansion ◽  
Optimal Configurations ◽  
Selection Of ◽  
Turbine Exhaust

Abstract Exhaust expansion joints, also known as compensators, are found in a variety of applications such as gas turbine exhaust pipes, generators, marine propulsion systems, OEM engines, power units, and auxiliary equipment. The motion compensators employed must have accomplished the maximum expansion-contraction cycle life while imposing the least amount of stress. Discrepancies in the selecting of bellows expansion joint design parameters are corrected by evaluating stress-based fatigue life, which is challenging owing to the complicated form of convolutions. Meridional and circumferential convolution stress equations that influencing fatigue cycles are evaluated and verified with FEA. Fractional factorial Taguchi L25 matrix is used for finding the optimal configurations. The discrete design parameters for the selection of the suitable configuration of the compensators are analysed with the help of the MADM decision making techniques. The multi-response optimization methods GRA, AHP, and TOPSIS are used to determine the parametric selection on a priority basis. It is seen that weighing distribution among the responses plays an important role in these methods and GRA method integrated with principal components shows best optimal configurations. Multiple regression technique applied to these methods also shows that PCA-GRA gives better alternate solutions for the designer unlike the AHP and TOPSIS method. However, higher ranked Taguchi run obtained in these methods may enhance the suitable selection of different design configurations. Obtained PCA-GRG values by Taguchi, Regression and DOE are well matched and verified for the all alternate solutions. Further, it also shows that stress based fatigue cycles obtained in this analysis for the L25 run indicates the range varying from 1.13 × 104 cycles to 9.08 × 105 cycles, which is within 106 cycles. This work will assist the design engineer for selecting the discrete parameters of stiff compensators utilized in power plant thermal appliances.

Identification of the Optimal Product Configuration and Parameters for Improving Manufacturability

1996 ◽  
Author(s):  
Deyi Xue
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Product Configuration ◽  
Design System ◽  
Optimization Approach ◽  
Continuous Variables ◽  
Integer Variables ◽  
System A ◽  
Global Optimization Methods

Abstract A global optimization approach for identifying the optimal product configuration and parameters is proposed to improve manufacturability measures including feasibility, cost, and time of production. Different product configurations, including alternative design candidates and production processes, are represented by an AND/OR graph. Product parameters are described by variables including continuous variables, integer variables, Boolean variables, and discrete variables. Two global optimization methods, genetic algorithm and simulated annealing, are employed for identifying the optimal product configuration and parameters. The introduced approach serves as a key component in an integrated concurrent design system. A case study example is given to show how the proposed method is used for solving the engineering problems.

Multiobjective approach developed for optimizing the dynamic behavior of incremental linear actuators

2014 ◽  
Vol 33 (3) ◽  
pp. 953-964 ◽  
Author(s):  
Imen Amdouni ◽  
Lilia El Amraoui ◽  
Frédéric Gillon ◽  
Mohamed Benrejeb ◽  
Pascal Brochet
Keyword(s):  
Multiobjective Optimization ◽  
Dynamic Model ◽  
Dynamic Behavior ◽  
Pareto Front ◽  
Computing Time ◽  
Design Parameters ◽  
Parallel Optimization ◽  
Optimization Approach ◽  
Content Type ◽  
Linear Actuators

Purpose – The purpose of this paper is to develop an optimal approach for optimizing the dynamic behavior of incremental linear actuators. Design/methodology/approach – First, a parameterized design model is built. Second, a dynamic model is implemented. This model takes into account the thrust force computed from a finite element model. Finally, the multiobjective optimization approach is applied to the dynamic model to optimize control as well as design parameters. Findings – The Pareto front resulting from the optimization approach (or the parallel optimization approach,) is better than the Pareto, which is obtained from the only application of MultiObjective Genetic Algorithm (MOGA) method (or parallel MOGA with the same number of optimization approach objective function evaluations). The only use of MOGA can reach the region near an optimal Pareto front, but it consumes more computing time than the multiobjective optimization approach. At each flowchart stage, parallelization leads to a significant reduction of computing time which is halved when using two-core machine. Originality/value – In order to solve the multiobjective problem, a hybrid algorithm based on MOGA is developed.

Integration of an Automatic Optimizer Functionality Into the Design Process of Industrial Steam Turbines

2012 ◽  
Author(s):  
Dimitri Drapkin ◽  
Franz Kores ◽  
Thomas Polklas
Keyword(s):  
Design Process ◽  
Optimization Methods ◽  
Simultaneous Optimization ◽  
Design Parameters ◽  
Optimization Approach ◽  
Steam Turbines ◽  
Particle Swarm Optimizer ◽  
Optimization Task ◽  
Wide Range ◽  
Optimization Parameters

Industrial steam turbines are mostly tailor made machinery, varying in a wide range of specifications. This feature introduces high requirements on the design process which has to be flexible, efficient and fast at the same time. Given live steam and design parameters as input, the geometry corresponding to the valid design scheme can be calculated together with the required thermodynamic, aerodynamic and mechanical characteristics. By variation of design parameters a design may be achieved which optimizes both, efficiency and cost. The optimization task is formulated mathematically, e.g. crucial optimization parameters, criteria for evaluation of different designs and other required constraints are selected. The structure of the resulting optimization problem is analyzed. Based on this analysis a modular optimization system design is proposed. The choice of Genetic Algorithms and Adaptive Particle Swarm Optimizer as optimization methods is discussed, recommendations for their proper use are given. A bicriterial optimization approach for a simultaneous optimization of efficiency and cost is developed.

Using the Pareto Set Pursuing Multiobjective Optimization Approach for Hybridization of a Plug-In Hybrid Electric Vehicle

2012 ◽  
Vol 134 (9) ◽  
Author(s):  
Shashi K. Shahi ◽  
G. Gary Wang ◽  
Liqiang An ◽  
Eric Bibeau ◽  
Zhila Pirmoradi
Keyword(s):  
Multiobjective Optimization ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Operating Cost ◽  
Pareto Set ◽  
Computational Time ◽  
Design Parameters ◽  
Optimization Approach ◽  
Hybrid Electric

A plug-in hybrid electric vehicle (PHEV) can improve fuel economy and emission reduction significantly compared to hybrid electric vehicles and conventional internal combustion engine (ICE) vehicles. Currently there lacks an efficient and effective approach to identify the optimal combination of the battery pack size, electric motor, and engine for PHEVs in the presence of multiple design objectives such as fuel economy, operating cost, and emission. This work proposes a design approach for optimal PHEV hybridization. Through integrating the Pareto set pursuing (PSP) multiobjective optimization algorithm and powertrain system analysis toolkit (PSAT) simulator on a Toyota Prius PHEV platform, 4480 possible combinations of design parameters (20 batteries, 14 motors, and 16 engines) were explored for PHEV20 and PHEV40 powertrain configurations. The proposed approach yielded the optimal solution in a small fraction of computational time, as compared to an exhaustive search. This confirms the efficiency and applicability of PSP to problems with discrete variables. In the design context we have found that battery, motor, and engine collectively define the optimal hybridization scheme, which also varies with the drive cycle and all electric range (AER). The proposed method and software platform could be applied to optimize other powertrain designs.

Fault Sensitivity Analysis of Data Feedback Control Design Based on Multiobjective Optimization

2016 ◽  
Author(s):  
Young-Man Kim
Keyword(s):  
Feedback Control ◽  
Multiobjective Optimization ◽  
Estimation Error ◽  
Optimization Technique ◽  
Control Synthesis ◽  
Optimization Approach ◽  
Worst Case ◽  
Mean Square Stability ◽  
Fault Sensitivity ◽  
Data Feedback

This paper analyzes the fault sensitivity of data feedback control which is synthesized with H∞/H_ optimization technique. With I/O data, a closed-loop output predictor is parameterized by stochastically uncertain Markov parameters which are estimated by least squares. The estimation error due to bias and noise are rejected over infinite horizon and guarantees mean square stability in the sense of worst case. The measured I/O data is setup as state which makes the stability analysis and data feedback control synthesis possible. In order to improve fault sensitivity, the H_ index method is applied. Then, the controller design problem based on multiobjective optimization approach is solved in a numerically efficient way such as Linear Matrix Inequality (LMI). The fault sensitivity is analyzed in the full frequency range and its effect on the pre-defined performance is described with a simulation example.

Sign in / Sign up

Export Citation Format

Share Document