Wind Turbine Gearbox Condition Based Monitoring

TL;DR

This paper proposes a novel method for wind turbine gearbox health monitoring using operational SCADA data, focusing on rotational speed ratios and clustering to identify deterioration without costly sensors.

Contribution

It introduces a new approach leveraging existing operational data and clustering techniques to monitor gearbox health over time, avoiding additional sensor installation.

Findings

Rotational speed ratios can indicate gearbox deterioration.

Clustering ensures consistent operational conditions for monitoring.

Method reduces need for costly sensor data collection.

Abstract

The main objective of this paper is finding effective gearbox condition monitoring methods by using continuously recorded monitoring SCADA (Supervisory Control and Data Accusation) data points. Typically for wind turbine gearbox condition monitoring; temperature readings, high frequency sounds and vibrations in addition to lubricant condition monitoring have been used. However, collection of such data, require shutting down equipment for installation of costly sensors and measuring lubricant quality. Meanwhile, operational data usually collected every 10 minutes, comprised of wind speed, power generated, pitch angle and similar performance parameters can be used for monitoring health of wind turbine components such as blades, gearbox and generator. This paper uses gear rotational speed for monitoring health of gearbox teeth; since gearbox teeth deterioration can be measured by monitoring rotor to generator rotation ratios over extended period of time. As nature of wind is turbulent with rapid fluctuations, a wind turbine may operate in variety of modes within relatively short period of time. Monitoring rotational speed ratio over time, requires consistent operational conditions such as wind speed and torques within the gearbox. This paper also introduces the concept of clustering such as Normal Mixture algorithm for dividing operating datasets into consistent subgroups, which are used for long term monitoring.