• Earthquakes and Structures
• /
• v.19 no.5
• /
• pp.347-359
• /
• 2020

The Mega-Sub Controlled Structure System (MSCSS), an innovative vibration passive control system for building structures, is improved by adding lead rubber bearings (LRBs) on top of the substructure. For the new system, a genetic algorithm is used to optimize the dynamic parameters and distributions of dampers and LRBs. The program uses various seismic performance indicators as optimization objectives, and corresponding results are compared. It is found that the optimization procedure for maximizing the energy dissipation ratio yields the best solutions, and optimized models have consistent seismic performances under different earthquakes. Seismic performances of optimized MSCSS models with and without LRBs, as well as the traditional Mega-Sub Structure model, are evaluated and compared under El Centro wave, Taft wave and 20 other artificial waves. In both elastic and plastic analysis, the model with LRBs shows significantly smaller story drift and horizontal acceleration than those of the other two models, and fewer plastic hinges are developed during severe earthquakes. Energy analysis also shows that LRBs installed in proper locations increase the deformation and energy dissipation of dampers, thereby significantly reduce the kinetic, potential, and hysteretic energy in the structure. However, LRBs do not have to be mounted on all the additional columns. It is also demonstrated that LRBs at unfavorable locations can decrease the energy dissipation for dampers. After LRBs are installed, the optimal damping coefficient and the optimal damping exponent of dampers are reduced to produce the best damping effect.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.6
• /
• pp.2236-2244
• /
• 2018

We make optimal consecutive offer curves for an energy storage system (ESS) integrated wind power producer (WPP) in the co-optimized day-ahead energy and regulation markets. We build the offer curves by solving multi-stage stochastic optimization (MSSO) problems based on the scenarios of pairs consisting of real-time price and wind power forecasts through the progressive hedging method (PHM). We also use the rolling horizon method (RHM) to build the consecutive offer curves for several hours in chronological order. We test the profitability of the offer curves by using the data sampled from the Iberian Peninsula. We show that the offer curves obtained by solving MSSO problems with the PHM and RHM have a higher profitability than offer curves obtained by solving deterministic problems.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.4
• /
• pp.547-554
• /
• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.

• International Journal of Fluid Machinery and Systems
• /
• v.4 no.1
• /
• pp.14-24
• /
• 2011

In this paper, design optimization for mixed-flow pump impellers and diffusers has been studied using a commercial computational fluid dynamics (CFD) code and DOE (design of experiments). We also discussed how to improve the performance of the mixed-flow pump by designing the impeller and diffuser. Geometric design variables were defined by the vane plane development, which indicates the blade-angle distributions and length of the impeller and diffusers. The vane plane development was controlled using the blade-angle in a fixed meridional shape. First, the design optimization of the defined impeller geometric variables was achieved, and then the flow characteristics were analyzed in the point of incidence angle at the diffuser leading edge for the optimized impeller. Next, design optimizations of the defined diffuser shape variables were performed. The importance of the geometric design variables was analyzed using $2^k$ factorial designs, and the design optimization of the geometric variables was determined using the response surface method (RSM). The objective functions were defined as the total head and the total efficiency at the design flow rate. Based on the comparison of CFD results between the optimized pump and base design models, the reason for the performance improvement was discussed.

• Nuclear Engineering and Technology
• /
• v.56 no.5
• /
• pp.1654-1666
• /
• 2024

The goal of this research is to propose a way to maximize small modular reactor (SMR) utilization to gain better market feasibility in support of carbon neutrality. For that purpose, a comprehensive tool was developed, combining off-design thermohydraulic models, economic objective models (levelized cost of electricity, annual profit), non-economic models (saved CO2), a parameter input sampling method (Latin hypercube sampling, LHS), and a multi-objective evolutionary algorithm (Non-dominated Sorting Algorithm-2, NSGA2 method) for optimizing a SMR-combined heat and power cycle (CHP) system design. Considering multiple objectives, it was shown that NSGA2+LHS method can find better optimal solution sets with similar computational costs compared to a conventional weighted sum (WS) method. Out of multiple multi-objective optimal design configurations for a 105 MWe design generation rating, a chosen reference SMR-CHP system resulted in its levelized cost of electricity (LCOE) below $60/MWh for various heat prices, showing economic competitiveness for energy market conditions similar to South Korea. Examined economic feasibility may vary significantly based on CHP heat prices, and extensive consideration of the regional heat market may be required for SMR-CHP regional optimization. Nonetheless, with reasonable heat market prices (e.g. district heating prices comparable to those in Europe and Korea), SMR can still become highly competitive in the energy market if coupled with a CHP system.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.5
• /
• pp.210-218
• /
• 2003

MDO (multidisciplinary design optimization) technology has been proposed and applied to solve large and complex optimization problems where multiple disciplinaries are involved. In this research. an MDO problem is defined for automobile design which has crashworthiness analyses. Crash model which are consisted of airbag, belt integrated seat (BIS), energy absorbing steering system .and safety belt is selected as a practical example for MDO application to vehicle system. Through disciplinary analysis, vehicle system is decomposed into structure subspace and occupant subspace, and coupling variables are identified. Before subspace optimization, values of coupling variables at given design point must be determined with system analysis. The system analysis in MDO is very important in that the coupling between disciplines can be temporary disconnected through the system analysis. As a result of system analysis, subspace optimizations are independently conducted. However, in vehicle crash, system analysis methods such as Newton method and fixed-point iteration can not be applied to one. Therefore, new system analysis algorithm is developed to apply to crashworthiness. It is conducted for system analysis to determine values of coupling variables. MDO algorithm which is applied to vehicle crash is MDOIS (Multidisciplinary Design Optimization Based on Independent Subspaces). Then, structure and occupant subspaces are independently optimized by using MDOIS.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.830-839
• /
• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.937-940
• /
• 1988

The problem of minimization of energy, consumed by the robot manipulator, is important, notably for larger manipulators, higher working speeds, and loads. Obviously, the stated problem requires the application of optimal control theory, which is being successfully applied for linear system and certain classes of nonlinear systems. However, the application of optimal control theory(in energy or time optimization) leads to substantial practical difficulties, so that significant simplifications are usually performed, either in model complexity or by neglecting the existing constraints. In this paper the problem of obtaining such an optimization method. which would take into account the complete system dynamics and all the constraints is considered. The only method found to be suitable for such a complex optimization should be based on dynamic programming.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1099-1109
• /
• 2018

For practical consideration, economic dispatch (ED) problems in power system have non-smooth cost functions with equality and inequality constraints that makes the problems complex constrained nonlinear optimization problems. This paper proposes a new constraint handling method for equality and inequality constraints which is employed to solve ED problems, where the incremental rate is employed to enhance the modification process. In order to prove the applicability of the proposed method, the study cases are tested based on the classical particle swarm optimization (PSO) and differential evolution (DE) algorithm. The proposed method is evaluated for ED problems using six different test systems: 6-, 15-, 20-, 38-, 110- and 140-generators system. Simulation results show that it can always find the satisfactory solutions while satisfying the constraints.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4680-4697
• /
• 2017

In future, since the user experience plays a more and more important role in the development of today's communication systems, quality of experience (QoE) becomes a widely used metric, which reflects the subjective experience of end users for wireless service. In addition, the energy efficiency is an increasingly important problem with the explosive growth in the amount of wireless terminals and nodes. Hence, a QoE-aware energy efficiency maximization based joint user access selection and power allocation approach is proposed to solve the problem. We transform the joint allocation process to an optimization of energy efficiency by establishing an energy efficiency model, and then the optimization problem is solved by chaotic clone immune algorithm (CCIA). Numerical simulation results indicate that the proposed algorithm can efficiently and reliably improve the QoE and ensure high energy efficiency of networks.