• 대한전기학회논문지:전력기술부문A
• /
• 제50권6호
• /
• pp.279-286
• /
• 2001

A load modeling, micro method, is performed by component load modeling, load composition rate estimation and aggregation of component load model, etc. The load model obtained from this process must be applied to actual load bus to verify it and to get reliable load model. But it is difficult to apply every load bus due to al lot of load buses and complex experiment. This paper proposed the field test method in load bus to verify the load modeling. For appropriate field test, representative load buses are selected by the proposed algorithm considering the composition rate of user category in all load buses. The field tests were performed at selected load buses to obtain load characteristics of bus by time and seasonal without blackout. The results of measurement and analysis are presented in detail.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 A
• /
• pp.304-306
• /
• 2000

Load modeling, micro method, needs field test to identify the validity of methodology applied to modeling. This paper presents seasonal field test method and measurement results on serveral substations. Seasonal load characteristics were analyzed by the developed substation load model and correlation coefficients of seasonal load of substation under base, peak and average load time.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2011년도 제42회 하계학술대회
• /
• pp.199-200
• /
• 2011

Load forecasting is very important for power system analysis and planning. This paper suggests yearly load forecasting of considering weekly normalization and seasonal load characteristics. Each weekly peak load is normalized and the average value is calculated. The new hourly peak load is seasonally collected. This method was used for yearly load forecasting. The results of the actual data and forecast data were calculated error rate by comparing.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 추계학술대회 논문집 전력기술부문
• /
• pp.450-453
• /
• 2003

This paper presents the estimation on Load Characteristic Factor(k) which is considered to load pattern and seasonal characteristic of consumer. We can calculate the loss of distribution networks through the equation composing of Load Factor(LF), Loss Load Factor(LLF) and load characteristic factor(k). This equation is similar to the method of Regulator-General Victoria, Australia. Generally, the conventional method for calculating the distribution losses uses k with a constant value from 0.1 to 0.3. However, the k which is a relationship between LF and LLF can be varied by load pattern and seasonal characteristics. It is necessary to estimate the k according to load characteristics. This paper shows the result for recalculating k using the KEPCO's SOMAS data measured in distribution networks.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
• /
• pp.242-244
• /
• 1999

This paper proposes a novel wavelet transform based algorithm for the seasonal load forecasting. In this paper, Daubechies DB2, DB4 and DB10 wavelet transforms are adopted to predict the seasonal loads and the numerical results reveal that certain wavelet components can effectively be used to identify the load characteristics in electric power systems. The wavelet coefficients associated with certain frequency and time localization are adjusted using the conventional multiple regression method and then reconstructed. In order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the wavelet transform approach can be used as an attractive and effective means of the seasonal load forecasting.

• 전기학회논문지
• /
• 제64권2호
• /
• pp.315-323
• /
• 2015

Nowadays, it is expected that mobility loads such as electric railways and electric vehicles will be penetrated gradually and affect on the power system stability by their load characteristics. Various researches have been carried out about electric vehicles for the recent decade though the load of electric railway could be forecasted because of the specified path and timetable, is a field with a long historic background. Some precise 5th polynomial equations are required to analyze the power system stability considering mobility load to be increased in the immediate future while the electric railway dispatching simulator uses load models with constant power and constant impedance for the system analysis. In this paper, seasonal urban railway load models are established as the form of 5th polynomial equations and substation load modeling methods are proposed merging railway station load models and general load models. Additionally, load management effects by the load modeling are confirmed through the case studies, in which seasonal load models are developed for Seoul Subway Line No. 2, Gyeongui Line and Airport Railroad and the substation load change is analyzed according to the railway load change.

• 설비공학논문집
• /
• 제29권3호
• /
• pp.111-118
• /
• 2017

Performance factors such as the EER(Energy Efficiency Ratio) and the COP (Coefficient of Performance) are being replaced by seasonal energy efficiency factors, like the SEER (Seasonal EER) and the SCOP (Seasonal COP) to evaluate the performance of a heat pump by the time of the year. Seasonal performance factors, such as the CSPF (Cooling Seasonal Performance Factor) and the HSPF (Heating Seasonal Performance Factor) are used to describe the heat pump's performance during the cool and hot seasons. In this study, the optimization of all heat pump's operating parameters was experimentally conducted to enhance the SEER based on the EU standard (EN 14825). Moreover, the SEER was improved by the compressor frequency, as well as indoor and outdoor fan speeds. In addition, the performance characteristics of the heat pump were studied under partial load conditions. As a result, the SEER was enhanced by 17% when the compressor frequency was optimized. An additional 2% improvement was achievable with the optimization of indoor and outdoor fan speeds.

• 한국물환경학회지
• /
• 제28권1호
• /
• pp.94-101
• /
• 2012

The performance data for eight years from a free-surface-flow constructed wetland system receiving agricultural tailwater were used to analyze denitrification rate and nitrogen treatment characteristics according to season and wetland design. Seasonal difference between growing season (March~November) and winter season (December~February) was shown in the concentration of all nitrogen species. Seasonal nitrogen treatment has similar trend with temperature and measured denitrification rate. The highest denitrification rate was measured in July, but treatment efficiency was most higher in May and June. Nitrogen absorption of vegetation could affect to these wetland performances, therefore dense population of wetland vegetation might be helpful. According to design of wetland, at least 25~50 m of wetland length was needed to decrease effluent T-N concentration to background concentration in growing season. In winter season, wetland needed much longer distance to reduce T-N concentration. Mass removal rate was continuously high through whole year because runoff coefficient was low in winter season. Applicability of constructed wetland was observed for the total maximum daily load that control T-N load.

• 한국해양공학회:학술대회논문집
• /
• 한국해양공학회 2002년도 추계학술대회 논문집
• /
• pp.100-107
• /
• 2002

This study investigates the seasonal variation and spatial distribution characteristics of pollutant load, as executing the quantity valuation of pollutant load inflowing into Yeong-Il bay from on-land including the Hyeong-san river. Annual total pollutant generating rate from Yeong-Il bay region are 202ton to BOD, 620ton to SS, 42ton to T-N, 16ton to T-P respectively, if expressly point out, pollutant generating rate from the Hyeong-san river is the greatest, which BOD ratio is 78.2%, SS 88.5%, T-N 62.5%, T-P 73.1%. As calculating Tank model with input value of daily precipitation and evaporation of 2001 year in drainage basin of the Hyeong-san river, Estimated result of the annual total river discharge effluencing from this river is $830{\times}106m^3$. As result to estimating annual total effluence rate outflowing at the rivers from each drainage basins, annual total inflow pollutant rate are BOD 10,633ton, SS 19,302ton, T-N 15,369ton, T-P 305ton. The III basin which is population congestion region of the Pohang-city drain away a good many pollutant load than the V basin including the Neang-Chun with wide drainage area. Especially, a great many T-N than T-P inflow into Yeong-Il bay. The accumulation of pollutant load effluenced from on-land will happen on at the inner coast region of Yeong-Il bay, finally we would make a prediction that the water quality will take a bad turn.

• 농촌계획
• /
• 제24권4호
• /
• pp.99-119
• /
• 2018

Unit load factor, which is used for the quantification of non-point pollution in watersheds, has the limitation that it does not reflect spatial characteristics of soil, topography and temporal change due to the interannual or seasonal variability of precipitation. Therefore, we developed the method to estimate a watershed-scale non-point pollutant load using seasonal forecast data that forecast changes of precipitation up to 6 months from present time for watershed-scale water quality management. To establish a preemptive countermeasure against non-point pollution sources, it is possible to consider the unstructured management plan which is possible over several months timescale. Notably, it is possible to apply various management methods such as control of sowing and irrigation timing, control of irrigation through water management, and control of fertilizer through fertilization management. In this study, APEX-Paddy model, which can consider the farming method in field scale, was applied to evaluate the applicability of seasonal forecast data. It was confirmed that the rainfall amount during the growing season is an essential factor in the non-point pollution pollutant load. The APEX-Paddy model for quantifying non-point pollution according to various farming methods in paddy fields simulated similarly the annual variation tendency of TN and TP pollutant loads in rice paddies but showed a tendency to underestimate load quantitatively.