• Journal of the Korea Society of Computer and Information
• /
• v.17 no.1
• /
• pp.211-218
• /
• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.12
• /
• pp.2202-2211
• /
• 2010

The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.1
• /
• pp.22-28
• /
• 2014

This paper proposes the fuzzy modeling and robust stability analysis of wind farm based on prediction model for wind speed. Owing to the sensitivity of wind speed, it is necessary to study the dynamic equation of the variable speed wind turbine. In this paper, based on the least-square method, the wind speed prediction model which is varied by the surrounding environment is proposed so that it is possible to evaluate the practicability of our model. And, we propose the composition of intelligent wind farm and use the fuzzy model which is suitable for the design of fuzzy controller. Finally, simulation results for wind farm which is modeled mathematically are demonstrated to visualize the feasibility of the proposed method.

• 한국태양에너지학회:학술대회논문집
• /
• 2008.11a
• /
• pp.300-305
• /
• 2008

Using WindPRO that was software for windfarm design developed by EMD from Denmark, wind resources for the western Jeju island were analyzed, and the performance of WindPRO prediction was evaluated in detail. The Hansu site and the Yongdang site that were located in coastal region were selected, and wind data for one year at the two sites were analyzed using WindPRO. As a result, the relative error of the Prediction for annual energy Production and capacity factor was about ${\pm}20%$. For evaluating wind energy more accurately, it is necessary to obtain lots of wind data and real electric power production data from real windfarm.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2011.11a
• /
• pp.677-681
• /
• 2011

Characteristics of vertical and horizontal axis wind turbines are explained. The speed and direction of wind on the blade of the Darrieus type turbine changes very severely. Therefore dynamic stall happens periodically and the wake from the front blade deteriorates the performance of rear blades. Blade element momentum theory(BEMT) is widely utilized for aerodynamic design and performace prediction of horizontal axis wind turbine(HAWT). Computation analysis and wind tunnel test are also performed for the performance prediction.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.26 no.1
• /
• pp.49-54
• /
• 2016

The wind power plants were installed in many places because of the low climate changing effects since 2000. Generally, the wind power plants located in the seaside and the mountainous area and the heights of the windmills are about 40 m~140 m above the ground level. So the noises emitted from the wind power plants propagate far away compared with other environment noise sources like trains and cars noise. Because of these reasons, the noise emitted from the wind power plant is easy to cause the additional social problems like as noise complaints. Under the situation, the ministry of environment has established the guideline to evaluate the environmental effects for the wind power plant. According to the guideline, the noise of the wind power plant has to meet 55 dB(A) at daytime and 45 dB(A) at night in the residential area, which is regulated in the noise and vibration management law. But, it is difficult to estimate the noise emitted from the wind power plant because of the absence of the prediction model of the wind power plant noise. Therefore, the noise prediction model for wind power plants using the regression analysis method is developed in this study. For the development of the model, the sound pressure levels of the wind power plants in Jeju island are measured and the correlations between the sound pressure levels are analyzed. Finally, the prediction equation of the wind power plant noise using by regression analysis method derived. The prediction equation for the wind power plant noise proposed in this study can be useful to evaluate the environmental effects in any wind power plant development district.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.2
• /
• pp.319-328
• /
• 2021

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.3
• /
• pp.19-27
• /
• 2015

Technologies of wind power generation for development of alternative energy technology have been accumulated over the past 20 years. Wind power generation is environmentally friendly and economical because it uses the wind blowing in nature as energy resource. In order to operate wind power generation efficiently, it is necessary to accurately predict wind speed changing every moment in nature. It is important not only averagely how well to predict wind speed but also to minimize the largest absolute error between real value and prediction value of wind speed. In terms of generation operating plan, minimizing the largest absolute error plays an important role for building flexible generation operating plan because the difference between predicting power and real power causes economic loss. In this paper, we propose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecast model made to analyze the wind speed forecast given by the Meteorological Administration and pattern value for considering seasonal property of wind speed as well as changing trend of past wind speed. The wind speed forecast given by the Meteorological Administration is the forecast in respect to comparatively wide area including wind generation farm. But it contributes considerably to make accuracy of wind speed prediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in more detail, the greater accuracy will be obtained.

• Wind and Structures
• /
• v.9 no.5
• /
• pp.387-396
• /
• 2006

This paper presents the wind characteristics of Typhoon Dujuan as measured at a 50 m guyed mast in Hong Kong. The basic wind speed, wind direction and turbulent intensity are studied at two measurement levels of the structure. The power spectral density of the typhoon is compared with the von Karman prediction, and the coherence between wind speeds at the two measurement levels is found to This paper presents the wind characteristics of Typhoon Dujuan as measured at a 50 m guyed mast in Hong Kong. The basic wind speed, wind direction and turbulent intensity are studied at two measurement levels of the structure. The power spectral density of the typhoon is compared with the von Karman prediction, and the coherence between wind speeds at the two measurement levels is found to compare with Davenport's prediction. The effect of typhoon Dujuan on the response of the structure will be discussed in a companion paper (Law, et al. 2006).with Davenport's prediction. The effect of typhoon Dujuan on the response of the structure will be discussed in a companion paper (Law, et al. 2006).

• Wind and Structures
• /
• v.18 no.6
• /
• pp.619-631
• /
• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.