Development of an advanced power curve for performance monitoring of wind turbines

Abstract

Maintenance is an important step that must be performed as soon as possible, to minimize the impact of failures. In this paper we discuss the concept of power curve fitting and deviation calculation to detect failures that cause the wind turbines to misperform (the so-called Type I failures). As mathematics and software applications evolution for predictive maintenance is growing fast, simple considerations will be made to enable the development of an advanced individual power curve, so the real time turbine performance analysis may be as accurate as possible. Past environmental and production data is subjected to outlier filtration and statistical analysis, prior to creation of a best fit that would allow for future failures to be detected early. The innovation relies on segregating a power curve for each directional sector and on the correction of the influence of the temperature on the power production, achieving an advanced power curve that really mimics as close as possible the real operational output.

Keywords

• predictive maintenance
• wind power curve fitting
• failure detection