June-Ho Park

Contribution of an Energy Storage System for Stabilizing a Microgrid during Islanded Operation

In this paper, the cooperative control scheme of micro sources and an ESS (Energy Storage System) during islanded operation is presented and evaluated by a simulation study. The ESS handles the frequency and voltage as a primary control. Then, the secondary regulation control returns the cur- rent power output of ESS into a pre-planned value. The simulations results show that the proposed co- operative control scheme can regulate the frequency and voltage and reduce the consumption of the stored energy of ESS.

New formulas for predicting audible noise from overhead HVAC lines using evolutionary computations

Audible noise (AN) produced by corona discharges from high voltage transmission lines is one of the more important considerations in line design. Therefore, line designers must predetermine the AN using prediction formulas. This paper presents the results of applying evolutionary computation techniques using AN data from lines throughout the world to develop new highly accurate formulas for predicting the A-weighted AN during heavy rain and stable rain from overhead AC lines. Calculated ANs using these new formulas and existing formulas are compared with measured data.

A Modified Particle Swarm Optimization for Optimal Power Flow

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 30-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

Design of fuzzy power system stabilizer using adaptive evolutionary algorithm

This paper presents a design methodology of fuzzy system stabilizer (FPSS) using an adaptive evolution algorithm (AEA). The AEA consists of a genetic algorithm for a global search and evolution strategy for a local search in an adaptive manner when the present generation evolves into the next generation. The AEA is used to optimize the membership functions and scaling factors of FPSS. A single machine infinite system is applied to evaluate the usefulness of the FPSS. The results show that the proposed FPSS has a better control performance than the conventional power system stabilizer (CPSS) in the case of a three-phase fault under heavy load. To show the robustness of FPSS, it is applied to the system with disturbances such as change of mechanical torque and three-phase fault under the normal and light load. The results of the FPSS show a better robustness than that of the CPSS.

Development of a New Personal Magnetic Field Exposure Estimation Method for Use in Epidemiological EMF Surveys among Children under 17 Years of Age

A number of scientific researches are currently being conducted on the potential health hazards of power frequency electric and magnetic field (EMF). There exists a non-objective and psychological belief that they are harmful, although no scientific and objective proof of such exists. This possible health risk from ELF magnetic field (MF) exposure, especially for children under 17 years of age, is currently one of Koreas most highly contested social issues. Therefore, to assess the magnetic field exposure levels of those children in their general living environments, the personal MF exposure levels of 436 subjects were measured for about 6 years using government funding. Using the measured database, estimation formulas were developed to predict personal MF exposure levels. These formulas can serve as valuable tools in estimating 24-hour personal MF exposure levels without directly measuring the exposure. Three types of estimation formulas were developed by applying evolutionary computation methods such as genetic algorithm (GA) and genetic programming (GP). After tuning the database, the final three formulas with the smallest estimation error were selected, where the target estimation error was approximately 0.03 μT. The seven parameters of each of these three formulas are gender (G), age (A), house type (H), house size (HS), distance between the subjects residence and a power line (RD), power line voltage class (KV), and the usage conditions of electric appliances (RULE).

Maximum Power Point Tracking using Double Fuzzy Logic Controller for Grid-connected Photovoltaic System

This paper proposes a method of maximum power point tracking (MPPT) using fuzzy logic control for grid-connected photovoltaic systems (PV). First, for the purpose of comparison, because of its proven and good performances, the incremental conductance (IncCond) technique is briefly introduced. A double fuzzy logic controller (DFLC) based MPPT is then proposed which has shown better performances compared to the IncCond MPPT based approach. Modeling and Simulation in grid-connected PV system results are provided for both controllers under same atmospheric condition based PSCAD/EMTDC. The double fuzzy logic MPPT controller is then simulated and evaluated, which has shown better performances.

Implementation of Fuzzy Logic Controller for HVDC Using Genetic Algorithm

Abstract This paper presents an optimal tuning method for Fuzzy Logic Controller (FLC) of current controller for HVDC using Genetic Algorithm (GA). GA is probabilistic search method based on genetics and evolution theory. The scaling factors of FLC are tuned by using real-time GA. The proposed tuning method is applied to the scaled-down HVDC simulator at Korea Electrotechnology Research Institute(KERI). Experimental result shows that disturbances are well-damped and the dynamic performances of FLC have the better responses than those of PI controller for small and large disturbances such as ULTC tap change, reference DC current change and DC ground fault.

Discussion of "New formulas for predicting audible noise from overhead HVAC lines using evolutionary computations" and closure

K. Tanabe and K. Miyajima comment on the paper by K.-H. Yang et al. (see ibid., vol.15, no.4, p.1243-51, 2000) and ask three questions of the authors. The original authors reply to the comments.