• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.9-20
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• 2018

When a large-scale photovoltaic (PV) system is introduced into a distribution system, the customer's voltage may exceed the allowable limit ($220V{\pm}6%$) due to voltage variations and reverse power flow in the PV system. In order to solve this problem, we propose a method for adjusting the customer voltage using the existing step voltage regulator (SVR) installed in the primary feeder. However, due to the characteristics of a mechanically operating SVR, the customer voltage during the tap changing time of the SVR is likely to deviate from the allowable limit. In this paper, an energy storage system (ESS) with optimal operation strategies, and an appropriate capacity calculation algorithm are proposed, and the parallel driving scheme between the SVR and the ESS is also proposed to solve the customer voltage problem that may occur during the tap changing time of the SVR. The simulation results show that the allowable limit of the customer voltage is verified by the proposed methods during the tap changing time of the SVR when the large-scale PV system is connected to the distribution system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1036-1043
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• 2014

The installation of PV system to the power distribution system is being increased as one of solutions for environmental pollution and energy crisis. Because the output efficiency of PV system is getting decreased because of the aging phenomenon and several operation obstacles, the technology development of output prediction and state diagnosis of PV modules are required in order to improve operation performance of PV modules. The conventional methods for output prediction by considering various parameters and standard test condition values of PV modules may have difficult and complex computation procedure and also their prediction values may produce large error. To overcome these problems, this paper proposes an optimal prediction algorithm and state diagnosis algorithm of PV modules by using least square methods of linear regression analysis. In addition, this paper presents a state diagnosis evaluation system of PV modules based on the proposed optimal algorithms of PV modules. From the simulation results of proposed evaluation system, it is confirmed that the proposed algorithms is a practical tool for state diagnosis of PV modules.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.799-821
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• 2016

Unmanned Aerial Vehicles (UAVs) are essential systems for Intelligence, Surveillance, and Reconnaissance (ISR) operations in current battlespace. And its importance will be getting extended because of complexity and uncertainty of battlespace. In this study, we forecast the advancement of 96 UAVs during the period of 32 years from 1982 to 2014 using TFDEA. TFDEA is a quantitative technology forecasting method which is characterized as non-parametric and non-statistical mathematical programming. Inman et al. (2006) showed that TFDEA is more accurate in forecasting compared with classical econometrics (e.g. regression). This study got 4.06% point of annual technological rate of change (RoC) for UAVs by applying TFDEA. And most UAVs in the period are inefficient according to the global SOA frontiers. That is because the countries which develop UAVs are in the middle class of technological level, so more than 60% of world UAVs markets are shared by North America and Europe which are advanced countries in terms of technological maturity level. This study could give some insights for UAVs development and its advancement. And also can be used for evaluating the adequacy of Required Operational Capability (ROC) of suggested future systems and managing the progress of Research and Development (R&D).