• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.54-61
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• 2015

This paper presents short-term wind farm power forecasting method using multivariate analysis and time series. Based on factor analysis, the proposed method makes new independent variables which newly composed by raw independent variables such as wind speed, ramp rate, wind power. Newly created variables are used in the time series model for forecasting wind farm power. To demonstrate the improved accuracy, the proposed method is compared with persistence model commonly used as reference in wind power forecasting using data from Jeju Island. The results of case studies are presented to show the effectiveness of the proposed forecasting method.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.57-58
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• 2003

산업자원부에서 $\ulcorner$대체에너지 개발 보급 3개년 계획(2002년 4월)$\lrcorner$ $\ulcorner$대체에너지이용 발전전력의 기준 가격 지침(2002년 5월)$\lrcorner$을 시행하여 대체에너지 생산 전력을 보상.지원함에 따라 국내에서도 본격적인 대체에너지 시대가 시작되었다. 특히 최근에는 지방자치단체를 중심으로 풍력발전사업이 활발하게 진행되고 있다. 일반적으로 바람은 지형조건과 기후특성에 영향을 받는데, 특히 우리나라와 같이 국토의 70%가 산지이며 사계절이 분명하고 반도 기상특성으로 인해 계절풍과 해륙풍이 존재하는 조건에서의 국지 바람장은 예측하기 힘든 매우 복잡한 양상을 보인다. (중략)

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1998.10a
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• pp.39-39
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• 1998

본 연구는 500KW급 수평축 풍력발전기용으로 개발된 회전날개의 시제품 제작에 앞서 축소모델에 대한, 이론적으로 예측된 공력성능과 신축에 의한 공력성능을 비교 검토함으로서, 설계결과를 검증하고, 필요한 경우 설계를 보완하여 개발위험도를 최소화하기 위해 수행되었다. 시험모델의 크기는 실제의 5%로서 직경이 2.1m이며 날개의 시위길이는 0.2r/R에서 0.101m, 날개끝에서 0.043m 이고, 날개단면형 상온 FX-S-03-182이다. 블레이드의 재질은 Glass/Epoxy 복합재료로 제작되었으며, 실제 풍황을 모사하기 위해 자연풍 상태에서 실험하였다. 실험장치의 구성은 15m 높이의 타워에 회전날개와 전자브레이크 및 각종 센서를 장착하였고, 날개가 회전하기 시작하면 제동장치에 의해 부하를 주면서 토크, 회전수, 풍속 등을 각각의 센서로부터 자료획득장치를 통해 자료처리를 할 수 있도록 하였다. 실험하는 동안 풍속은 4m/s-13m/s 정도로서 시동 풍속인 4m/s와 정격풍속인 12m/s를 포함하여 회전날개의 전체적인 특성을 파악하기 용이하였고, 이론적인 예측성능과 측정된 성능을 비교 검토한 결과 비슷한 결과를 얻어 공력설계 및 해석 방법을 검증하였다.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1355-1357
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• 2002

한국형 풍력발전기의 감시제어 시스템은 순시적인 발전 상황과 풍향, 풍속 등을 모니터링하는 기능뿐만 아니라, 인버터 시스템과 전력 변환 지령값을 전달하고, 인버터의 현재 상태를 감시하고 최적의 피치각라을 유지시켜주는 피치제어 시스템 등으로 구성되어 있다. 이러한 감시제어 시스템은 풍력발전기의 효율을 증가시킬뿐만 아니라 각종 예측이 불가능한 사고를 예방하는데 있어서 필수적이라 할 수 있다.

• KIEAE Journal
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• v.12 no.1
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• pp.127-132
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• 2012

The energy consumption in the world is growing rapidly. And the environmental issues of climate become a important task. The interest in renewable energy like wind and solar is increasing now. Especially, by reducing power transmission loss, a small wind power is getting attention at the residential areas and campus of university. In this study, we attempted to estimate and compare the wind energy density using wind data of AWS (Automatic Weather Station) of H University. In this case of a campus, the weibull distribution parameter C is 2.27, and K is 0.88. According to the data, the energy density of the small wind power is 12.7 W/m2. We did CFD(Computational Fluid Dynamics) simulations at H University campus by 7 wind directions(ENE, ESE, SE, NW, WNW, W, WSW). In the results, we suggest 4 small wind powers. The small wind power generating system can produce 4,514kWh annually.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.5
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• pp.615-621
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• 2007

Wind power generation is one of the promising gateways that will solve the energy crisis in the future. The wind power generator studied so far is limited to static interpretation in the areas related to tower. This study broadly sets the form of tower as tubular and jacket, identifies the characteristics of each and aims to find and apply their trend to in actual design and manufacturing process. This paper identified the resonance frequency of tower at each mode and studied their features. Furthermore, this study identified the characteristics of the load that occurs in operation and the effect of additional mass incurring when installed in sea, and it compared the two types of tower and was able to predict their trend.

• Journal of the Korean Solar Energy Society
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• v.35 no.2
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• pp.33-41
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• 2015

A feasibility study to estimate annual energy production of an offshore wind farm was performed using MERRA reanalysis data. Two well known commercial codes commonly used to wind farm design and power prediction were used. Three years of MERRA data were used to predict annual energy predictions of the offshore wind farm close to Copenhagen from 2011 to 2013. The availability of the wind farm was calculated from the power output data available online. It was found from the study that the MERRA reanalysis data with commercial codes could be used to fairly accurately predict the annual energy production from offshore wind farms when a meteorological mast is not available.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.25-28
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• 2014

에너지 자립섬은 외부 전력의 유입이 어려운 상황에서 풍력/태양광 발전과 같은 재생 에너지를 주요 발전원으로 운영하는 섬이다. 에너지 자립섬의 운영을 위해서는 전력 수요와 공급량을 예측하여 발전기, 송배전 시스템, ESS 등의 운영 계획 수립이 필요한데 수요 및 공급의 예측은 기상 상황 및 시간 등의 다양한 요소에 영향을 받으므로 예측이 어렵다. 이러한 특성을 감안하여 효율적인 전력망 운영을 위해 기계 학습을 기반으로 한 스마트 그리드 운영 프레임워크의 활용을 통해 이 문제를 해결하고자 한다. 본 논문에서는 자립섬 운영 계획 수립에 필요한 구성 요소를 파악하고 요소들 간의 연계 관계를 분석하여 운영 시스템의 프레임워크 설계안을 제시한다.