• Journal of Information Processing Systems
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• v.18 no.3
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• 한국연소학회:학술대회논문집
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• 2013.06a
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• pp.15-17
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• 2013

In the design of combined cycle power plants, the design parameters considered mainly could be changed and added for performance evaluation with change on the design objective and method. Therefore, the design criteria considering the different objectives and type of power plant were needed. Thermodynamic and economic analyses of various types of gas turbine combined cycle power plants with demand on generation of power and heat and carbon capture system from high pressure flue gas have been performed to establish criteria for optimization of power plants.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.461-466
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• 2005

This paper addresses the bidding strategies of a hydro generator in an electricity market, and their effect on the electricity market in accordance with some parameters: the water volume, the demand elasticity, and the hydro unit performance. The competition of a hydro generator is formulated as a hi-level optimization problem, and the solving scheme for the equilibrium condition is proposed as a set of nonlinear simultaneous equations. The equilibrium of the oligopolistic model is evaluated by comparison with that of a perfect competition model from the viewpoint of a market power. Simulation results show some parameters have an influence on the market power of an electricity market including a hydro generator.

• Solar Energy
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• v.4 no.2
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• pp.3-12
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• 1984

In this paper, how to design the wind power generation system is presented. It is shown that the wind system optimization can be achieved by consideration of the four factors; wind statistics, efficiency of conversion of wind energy to electrical energy, average annual energy extracted and load factor. The wind is characterized by a weibull probability function. The Weibull parameter is calculated for the characterizing wind and the primary design specification of ten different sites. Some graphs are presented, which can be used to design a wind system for maximum output of a specified load factor at given site. Two different systems, $V_c=0.4V_R$ and $V_c=0.5V_R$ are discussed, as samples, for investigation of the effects on the system through the variation of cut-in speed.

• Journal of Ocean Engineering and Technology
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• v.25 no.4
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• pp.42-47
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• 2011

In this paper, wake equalizing duct (WED) form optimization was carried out using computational fluid dynamics (CFD) techniques. A WED is a ring-shaped flow vane with a foil-type cross-section fitted to a hull in front of the upper propeller area. The main advantage of a WED is the power savings resulting from the uniformity of the velocity distribution on the propeller plane, a reduction in the flow separation at the aft-body, and lift generation with a forward force component on the foil section. This paper intends to evaluate these functions and find an optimized WED form for minimizing the viscous resistance and equalizing the wake distribution. In the optimization process, the study uses four WED parameters: the angle of the section, longitudinal location, and angles of the axes for the half rings against the longitudinal and transverse planes of the ship. KRISO 300K VLCC2 (KVLCC2) is chosen as an example ship to demonstrate the WED optimization. The optimization procedure uses genetic algorithms (GAs), a gradient-based optimizer for the refinement of the solution, and Non-dominated Sorting GA-II(NSGA-II) for Multiobjective Optimization. The results show that the optimized WED can reduce the viscous resistance at the expense of the uniformity of the wake distribution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.918-922
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• 2009

The power expansion planning is large and capital intensive capacity planning. In the past, the expansion planning was established with the proper supply reliability in order to minimize social cost. However, the planning cannot use cost minimizing objective function in the power markets with many market participants. This paper proposed the power expansion planning process in the power markets. This system is composed of Regulator and GENCO's model. Regulator model used multi-criteria decision making rule. GENCO model is very complex problem. Thus, this system transacted the part by several scenario assuming GENCO model.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2146-2156
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• 2017

When a failure occurs in active distribution system, it will be isolated through the action of circuit breakers and sectionalizing switches. As a result, the network might be divided into several connected components, in which distributed generations could supply power for customers. Aimed at decreasing customer interruption cost, this paper proposes a theoretically optimal island partition model for such connected components, and a simplified but more practical model is also derived. The model aims to calculate a dynamic island partition schedule during the failure recovery time period, instead of a static islanding status. Fluctuation and stochastic characteristics of the renewable distributed generations and loads are considered, and the interruption cost functions of the loads are fitted. To solve the optimization model, a heuristic search algorithm based on the hill climbing method is proposed. The effectiveness of the proposed model and algorithm is evaluated by comparing with an existing static island partitioning model and intelligent algorithms, respectively.

• Journal of Power Electronics
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• v.4 no.3
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• pp.188-196
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• 2004

The dielectric barrier discharge is recognized as one of the efficient methods of ultraviolet light generation and ozone production. As well, it is widely utilized for gaseous wastes neutralization and other technological processes in industry. This electrochemical reaction is electrically equivalent to a nonlinear capacitive load that represents some difficulties for designing the power supply. Therefore, a conventional power supply is designed for a drastically simplified model of the load and generally is not optimal. This paper presents a fast simulation approach for the nonlinear capacitive model representation of the dielectric barrier discharge load lamp. The main idea of the proposed method is to use analytical solutions of the differential state equations for the load and find the unknown initial conditions for the steady state by an optimization method. The derived expressions for the analytical solutions are rather complicated, however they greatly reduce the calculation time, which make sense when a deeper analysis is performed. This paper introduces the proposed simulation method and gives some examples of its application such as estimation of the load equivalent parameters and load matching conditions.

• ETRI Journal
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• v.30 no.1
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• pp.129-140
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• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.