A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data

TL;DR

This paper introduces a machine learning-based predictive maintenance system for wind turbines that effectively detects component failures up to two months in advance using SCADA data, enhancing maintenance planning.

Contribution

The paper presents a novel scalable predictive maintenance model combining machine learning and statistical tools applied to SCADA data for wind turbines, validated through extensive offline and real-time testing.

Findings

Predicts anomalies up to 2 months before failure

Successfully detects multiple anomalies in real-time

Enables proactive maintenance scheduling

Abstract

In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines. The proposed model is based on machine learning and statistical process control tools applied to SCADA (Supervisory Control And Data Acquisition) data of critical components. The test campaign was divided into two stages: a first two years long offline test, and a second one year long real-time test. The offline test used historical faults from six wind farms located in Italy and Romania, corresponding to a total of 150 wind turbines and an overall installed nominal power of 283 MW. The results demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Furthermore, the real-time 12-months test confirms the ability of the proposed system to detect several anomalies, therefore allowing the operators to identify the root causes, and to schedule maintenance actions before reaching a catastrophic stage.