3,008 research outputs found
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems
The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon
Quantifying the Influences on Probabilistic Wind Power Forecasts
In recent years, probabilistic forecasts techniques were proposed in research
as well as in applications to integrate volatile renewable energy resources
into the electrical grid. These techniques allow decision makers to take the
uncertainty of the prediction into account and, therefore, to devise optimal
decisions, e.g., related to costs and risks in the electrical grid. However, it
was yet not studied how the input, such as numerical weather predictions,
affects the model output of forecasting models in detail. Therefore, we examine
the potential influences with techniques from the field of sensitivity analysis
on three different black-box models to obtain insights into differences and
similarities of these probabilistic models. The analysis shows a considerable
number of potential influences in those models depending on, e.g., the
predicted probability and the type of model. These effects motivate the need to
take various influences into account when models are tested, analyzed, or
compared. Nevertheless, results of the sensitivity analysis will allow us to
select a model with advantages in the practical application.Comment: 5 pages; 1 table; 3 figures; This work has been submitted to the IEEE
for possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
A Review on the Young History of the Wind Power Short-term Prediction
This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out
State of the Art in the Optimisation of Wind Turbine Performance Using CFD
Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained
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