scholarly journals On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

2018 ◽  
Vol 10 (11) ◽  
pp. 1701 ◽  
Author(s):  
Laura Valldecabres ◽  
Nicolai Nygaard ◽  
Luis Vera-Tudela ◽  
Lueder von Bremen ◽  
Martin Kühn
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Electricity Market ◽  
Doppler Radar ◽  
Short Term ◽  
Radar Observations ◽  
Spatial Coverage ◽  
Probabilistic Forecasts ◽  
Dual Doppler Radar

Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper.

scholarly journals A Reliability Based Model for Wind Turbine Selection

2013 ◽  
Vol 2 (2) ◽  
pp. 69-74 ◽  
Author(s):  
A.K. Rajeevan ◽  
P.V. Shouri ◽  
Usha Nair
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Point Of View ◽  
Cube Root ◽  
Mathematical Relationship ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
A Site

A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

scholarly journals Design and Contruction of Vertical Axis Wind Turbine Triple-Stage Savonius Type as the Alternative Wind Power Plant

KnE Energy ◽  
2015 ◽  
Vol 2 (2) ◽  
pp. 172
Author(s):  
Tedy Harsanto ◽  
Haryo Dwi Prananto ◽  
Esmar Budi ◽  
Hadi Nasbey
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Wind Turbine ◽  
Wind Power ◽  
Output Power ◽  
Vertical Axis ◽  
Wind Power Plant ◽  
Vertical Axis Wind Turbine ◽  
Low Wind Speed ◽  
Simple Materials

<p>A vertical axis wind turbine triple-stage savonius type has been created by using simple materials to generate electricity for the alternative wind power plant. The objective of this research is to design a simple wind turbine which can operate with low wind speed. The turbine was designed by making three savonius rotors and then varied the structure of angle on the three rotors, 0˚, 90˚ and 120˚. The dimension of the three rotors are created equal with each rotor diameter 35 cm and each rotor height 19 cm. The turbine was tested by using blower as the wind sources. Through the measurements obtained the comparisons of output power, rotation of turbine, and the level of efficiency generated by the three variations. The result showed that the turbine with angle of 120˚ operate most optimally because it is able to produce the highest output power and highest rotation of turbine which is 0.346 Watt and 222.7 RPM. </p><p><strong>Keywords</strong>: Output power; savonius turbine; triple-stage; the structure of angle</p>

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

scholarly journals A Controller for Optimum Electrical Power Extraction from a Small Grid-Interconnected Wind Turbine

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5809
Author(s):  
Tania García-Sánchez ◽  
Arbinda Kumar Mishra ◽  
Elías Hurtado-Pérez ◽  
Rubén Puché-Panadero ◽  
Ana Fernández-Guillamón
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Conversion ◽  
Wind Turbine ◽  
Wind Power ◽  
Maximum Power ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
Conversion System ◽  
Energy Conversion System

Currently, wind power is the fastest-growing means of electricity generation in the world. To obtain the maximum efficiency from the wind energy conversion system, it is important that the control strategy design is carried out in the best possible way. In fact, besides regulating the frequency and output voltage of the electrical signal, these strategies should also extract energy from wind power at the maximum level of efficiency. With advances in micro-controllers and electronic components, the design and implementation of efficient controllers are steadily improving. This paper presents a maximum power point tracking controller scheme for a small wind energy conversion system with a variable speed permanent magnet synchronous generator. With the controller, the system extracts optimum possible power from the wind speed reaching the wind turbine and feeds it to the grid at constant voltage and frequency based on the AC–DC–AC conversion system. A MATLAB/SimPowerSystems environment was used to carry out the simulations of the system. Simulation results were analyzed under variable wind speed and load conditions, exhibiting the performance of the proposed controller. It was observed that the controllers can extract maximum power and regulate the voltage and frequency under such variable conditions. Extensive results are included in the paper.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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