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.

Discrete wavelet transforms based hybrid approach to forecast windspeed time series

2021 ◽  
pp. 0309524X2199826
Author(s):  
Anil Kumar Kushwah ◽  
Rajesh Wadhvani
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Statistical Models ◽  
Wavelet Transforms ◽  
Hybrid Approach ◽  
Hybrid Models ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Power Calculation

The wind resources have been estimated by using physical models, statistical models, and artificial intelligence models. Wind power calculation helps us measure the annual energy that will sustain the balance between electricity generation and electricity consumption. Wind speed plays a significant role in calculating wind power, due to which here we focus on wind speed prediction. In this paper, hybrid models for wind speed forecasting have been proposed. The hybrid models are formed by combining the time series decomposition technique, that is, discrete wavelet transform (DWT), with statistical models, that is, autoregressive integrated moving average (ARIMA) and generalized autoregressive score (GAS), respectively. These hybrid models are referred to as DWT-ARIMA and DWT-GAS. DWT decomposes the original series into sub-series. After that, statistical models are applied to each sub-series for prediction. In the end, aggregate the prediction results of each sub-series to get the final forecasted series. For experimentation purposes, statistical and hybrid models are applied to various datasets that are taken from the NREL repository. In our studies, the hybrid version demonstrates better results in terms of accuracy and complexity, which indicates superior performance in most cases compared to the existing statistical models.

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 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>

Optimal Operation Planning of Wind Farm Installed BESS Using Wind Power Forecast Data of Wind Turbine Generators Considering Forecast Error

2013 ◽  
Vol 14 (3) ◽  
pp. 207-218 ◽  
Author(s):  
Kazuki Ogimi ◽  
Shota Kamiyama ◽  
Michael Palmer ◽  
Atsushi Yona ◽  
Tomonobu Senju ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Forecast Error ◽  
Energy Resource ◽  
Optimal Operation ◽  
Turbine Generators ◽  
Wind Turbine Generators ◽  
Wind Power Forecast ◽  
Forecast Data

Abstract In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.

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 Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 523 ◽  
Author(s):  
Bian Ma ◽  
Jing Teng ◽  
Huixian Zhu ◽  
Rong Zhou ◽  
Yun Ju ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Sensor Array ◽  
Three Dimensional ◽  
Ultrasonic Sensor ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Wind Turbine Control ◽  
3D Space

The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.

A Probabilistic Model for Simulating Long-Term Wind-Power Output

2003 ◽  
Vol 27 (3) ◽  
pp. 167-181 ◽  
Author(s):  
Scott Kennedy ◽  
Peter Rogers
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Power Output ◽  
Energy Resource ◽  
Autoregressive Process ◽  
Resource Planning ◽  
Offshore Wind ◽  
Arbitrary Length

This paper describes a chronological wind-plant simulation model for use in long-term energy resource planning. The model generates wind-power time series of arbitrary length that accurately reproduce short-term (hourly) to long-term (yearly) statistical behaviour. The modelling objective and methodology differ from forecasting models, which focus on minimizing prediction error. In the present analysis, periodic cycles are isolated from historical wind-speed data from a known local site and combined with a first-order autoregressive process to produce a wind-speed time series model. Corrections for negative wind-speed values and spatial smoothing for geographically disperse wind turbines are discussed. The resulting model is used to simulate the output from a hypothetical offshore wind-plant south of Long Island, New York. Modelled differences of power output between individual turbines result from wind speed variability; wake effects are not considered in this analysis.

A case study of climate variability effects on wind resources in South Africa

2014 ◽  
Vol 25 (3) ◽  
pp. 2-10 ◽  
Author(s):  
Lynette Herbst ◽  
Jörg Lalk
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Power ◽  
Energy Production ◽  
Energy Sector ◽  
Speed Increase ◽  
Wind Resources ◽  
Wind Speed Increase

The wind energy sector is one of the most prominent sectors of the renewable energy industry. However, its dependence on meteorological factors subjects it to climate change. Studies analysing the impact of climate change on wind resources usually only model changes in wind speed. Two elements that have to be calculated in addition to wind speed changes are Annual Energy Production (AEP) and Power Density (PD). This is not only because of the inherent variability between wind speed and wind power generated, but also because of the relative magnitudes of change in energy potentially generated at different areas under varied wind climates. In this study, it was assumed that two separate locations would experience a 10% wind speed increase after McInnes et al. (2010). Given the two locations’ different wind speed distributions, a wind speed increase equal in magnitude is not equivalent to similar magnitudes of change in potential energy production in these areas. This paper demonstrates this fact for each of the case studies. It is of general interest to the energy field and is of value since very little literature exists in the Southern African context on climate change- or variability-effects on the (wind) energy sector. Energy output is therefore dependent not only on wind speed, but also wind turbine characteristics. The importance of including wind power curves and wind turbine generator capacity in wind resource analysis is emphasised.

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