Phase I analysis of hidden operating status for wind turbine

TL;DR

This paper presents a phase I analysis method for wind turbine SCADA data, using power curves and control charts to identify abnormal operating segments, thereby enhancing turbine monitoring and maintenance.

Contribution

It introduces a distribution-free control chart approach combined with power curve modeling for better phase I analysis of wind turbine data.

Findings

Identified out-of-control segments in real field data.

Improved understanding of turbines' operating status.

Enhanced reliability and maintenance strategies.

Abstract

Data-driven methods based on Supervisory Control and Data Acquisition (SCADA) become a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbines' operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distribution-free control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbines' performance. Informative out-of-control segments have been revealed in real field case studies. This phase I analysis can help improve wind turbine's monitoring, reliability, and maintenance for a smarter wind energy system.