E3S Web Conf.
Volume 22, 2017International Conference on Advances in Energy Systems and Environmental Engineering (ASEE17)
|Number of page(s)||8|
|Published online||07 November 2017|
Comparison of power curve monitoring methods
École de Technologie Supérieure, 1100 Notre-Dame O, Montréal, QC, Canada
2 Arista Renewables Energies, 2648 Desjardins, Montréal, QC, Canada
* Corresponding author: firstname.lastname@example.org
Performance monitoring is an important aspect of operating wind farms. This can be done through the power curve monitoring (PCM) of wind turbines (WT). In the past years, important work has been conducted on PCM. Various methodologies have been proposed, each one with interesting results. However, it is difficult to compare these methods because they have been developed using their respective data sets. The objective of this actual work is to compare some of the proposed PCM methods using common data sets. The metric used to compare the PCM methods is the time needed to detect a change in the power curve. Two power curve models will be covered to establish the effect the model type has on the monitoring outcomes. Each model was tested with two control charts. Other methodologies and metrics proposed in the literature for power curve monitoring such as areas under the power curve and the use of statistical copulas have also been covered. Results demonstrate that model-based PCM methods are more reliable at the detecting a performance change than other methodologies and that the effectiveness of the control chart depends on the types of shift observed.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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