Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model

Abstract

Vigilant fault diagnosis and preventive maintenance has the potential to significantly decrease costs associated with wind generators. As wind energy continues the upward growth in technology and continued worldwide adoption and implementation, the application of fault diagnosis techniques will become more imperative. Fault diagnosis and preventive maintenance techniques for wind turbine generators are still at an early stage compared to matured strategies used for generators in conventional power plants. The cost of wind energy can be further reduced if failures are predicted in advance of a major structural failure, which leads to less unplanned maintenance. High maintenance cost of wind turbines means that predictive strategies like fault diagnosis and preventive maintenance techniques are necessary to manage life cycle costs of critical components. Squirrel-Cage Induction Generators (SCIG) are the prevailing generator type and are more robust and cheaper to manufacturer compared to other generator types used in wind turbines. A statistical model was developed using SCADA data to estimate the relationships between winding temperatures and other variables. Predicting faults in stator windings are challenging because the unhealthy condition rapidly evolves into a functional failure.

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

“…Onshore wind energy is a near-zero marginal cost generator, which means that (almost) all of the cost is attributed to the construction of the asset, and 70e80% [190] of the costs are made up by the turbine itself. IRENA states the total installed costs (i.e.…”

Rüzgar Türbinlerinde İşletme ve Bakım

“…Bakım, üretim işlemlerinde en düşük maliyetle optimum kullanılabilirlik, güvenilirlik ve işletilebilirlik seviyelerinin sağlanmasına yardımcı olan bir aktivitedir. Bakım stratejileri genel olarak arıza bakımı, önleyici bakım ve düzeltici bakım olmak üzere üç ana stratejiye ayrılabilir [9].…”

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

“…Swana and Doorsamy [2] investigates a short-circuit fault involving the inter-turn windings of both the stator and rotor. Additional studies on stator faults are presented in [3], [4], while rotor fault research is documented in studies [5], [6]. The majority of fault detection techniques utilize machine learning methods, including wavelet analysis [5], [7], [8], fast Fourier transform (FFT) [9], current signature analysis [4], [10],…”