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With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie—Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTPs) that result from such surface contamination or damage. To complement the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.
Passive Thermografie als Inspektionsinstrument für Rotorblätter von Windkraftanlagen im Betrieb
(2024)
Der „European Green Deal“ und der globale Kampf gegen den Klimawandel erfordern mehr und größere Windkraftanlagen. Um dem steigenden Bedarf an Wartung und Inspektion zu bedienen, werden neben der „klassischen“ Inspektion durch Industriekletterer zunehmend auch halb autonome Methoden der Ferninspektion entwickelt.
Die BAM arbeitet in mehreren Projekten an der Detektion sowohl inneren als auch oberflächlichen Schäden mittels bodenbasierter passiver Thermografie. Passive Thermografie hat den Vorteil, dass die Sonne als Wärmequelle genutzt wird und so sehr große Objekte ohne direkten Zugriff untersucht werden können. Dies erlaubt eine Inspektion von Rotorblättern von Windenergieanlagen im laufenden Betrieb. Das verhindert Ausfallzeiten und Umsatzeinbußen im Vergleich zu herkömmlichen Blattinspektionen.
Neben der Automatisierung der Aufnahmetechnik stehen zwei wissenschaftliche Fragestellungen im Fokus der aktuellen Arbeiten. Auf der einen Seite sind Inspektionen mit passiver Thermografie und deren Ergebnisse stark wetterabhängig. Deswegen bedarf es der einem guten Verständnis dieser Abhängigkeit und einer Kopplung von Simulationen und experimentellen Daten. So kann gezeigt werden, welche Defekte in Rotorblätter passive Thermografie bei welchen Wetterbedingungen sichtbar macht. Auf der anderen Seite sind die Auswertung und Interpretation der schnell erfassten Inspektionsergebnisse zeitaufwendig und verlangen gute Kenntnisse der Thermografie. Um hier praxistauglicher zu werden, arbeitet die BAM an der Erstellung von Algorithmen, die eine Vorauswahl und -interpretation der Inspektionsdaten mit künstlicher Intelligenz ermöglichen.
In dem Beitrag wird der messtechnische Aufbau zur bodenbasierten passiven Thermografie an Rotorblättern, der Vergleich von Simulationsdaten und Feldmessungen sowie die Arbeiten bezüglich der KI-basierten Auswertung von thermografischen Aufnahmen gezeigt.
The European Green Deal and the global fight against climate change call for more and larger wind turbines. To meet the increasing demand for maintenance and inspection, semi-autonomous methods of remote inspection are increasingly being developed in addition to industrial climbers performing the inspection [1, 2].
Rotor blades are exposed to extreme weather conditions throughout their lifetime causing leading edge erosion which changed the aerodynamic features of blades and can cause structural damages. The estimated annual energy production (AEP) losses caused by erosion damages are between 0.5% and 2% per year. The classification of the severity of the damage and the quantification of the resulting AEP losses are subject of scientific research. With such information, cost efficient repairs and maintenance efforts can improve the power production of wind turbines.
Passive thermography presents a viable method for inspecting rotor blades, capable of identifying internal damage [3, 4] and surface erosion [5, 6]. An advantage of this method is that it not only detects rain erosion damage but also reveals temperature variations caused by resulting turbulence on the blade's surface. These turbulent effects reduce rotor blade efficiency, leading to losses in AEP. The inspection process takes approximately 10 minutes per turbine and is carried out while the turbine is fully operational, eliminating downtime and revenue losses typically associated with traditional blade inspections. This efficient inspection procedure is further enhanced by an automatic data analysis system, resulting in a substantial number of wind turbines being inspected within a specified timeframe. The automated evaluation of thermal images is executed using a fully convolutional network (FCN).
The initial phase involved training an FCN using over 1500 thermal images. The primary objective was to accurately identify the thermal patterns associated with erosion damage at the leading edge. In the ongoing second phase, the emphasis is on integrating thermographic and visual images to assess the extent of the damage using an FCN. Additionally, this phase involves leveraging human factors in the FCN training process and providing a precise estimation of Annual Energy Production losses attributed to the identified rain erosion damages.
The rapid growth of wind energy infrastructure over the past two to three decades has led to an urgent need for advanced non-destructive testing (NDT) methods—both for newly installed wind turbine blades (WTBs) and for ageing components nearing the end of their service life. Among emerging techniques, passive infrared thermography (IRT) offers a promising solution by enabling contactless, time-efficient inspection based on naturally occurring thermal variations. The effectiveness of passive IRT depends on the presence of sufficient thermal contrast to distinguish surface features, subsurface structures, and defects. To better understand the possibility of obtaining such contrast in composite structures such as WTBs, a controlled study was carried out on a blade section exposed to programmed temperature transients in a climate chamber. Infrared measurements were recorded, and the thermal behaviour of the specimen was simulated using finite element models (FEM) in COMSOL Multiphysics 6.3. Although direct validation is limited by measurement uncertainties and transient effects, the comparison provides insight into the capabilities and limitations of FEM in replicating real-world thermal behaviour. This paper focuses specifically on the challenges related to the modelling approach.
The rapid expansion of wind energy infrastructure over the past 20–30 years has led up to a situation where advanced non‐destructive testing (NDT) technologies are the need‐of‐the‐hour, not only for new wind turbine blades (WTBs) that are being installed, but also for older infrastructure which is reaching their designed lifetime. NDT technologies that improve both the quality as well as reduce the time required for the inspection are sought after, and one such example is passive infrared thermography (IRT). For passive IRT to provide significant information/insight into the integrity of the WTB, there needs to exist certain thermal contrast to both visualize and distinguish between features in WTB. These features could be surface features, subsurface structure or defects. The temperature variations due to air temperature fluctuations and the sun assist (passively) to obtain the necessary thermal contrast. To better understand the thermal response of composite structures such as WTBs, a validation study was conducted using a WTB section subjected to controlled temperature transients within a climate chamber, without external irradiation. Infrared measurements were recorded using a thermographic camera, and the same specimen was modeled using finite element methods (FEM) in COMSOL Multiphysics. While a direct validation of the simulation is limited due to transient and unmeasured variables in the experimental data, qualitative comparison provides valuable insight into the applicability of FEM for predicting thermal behavior in passive IRT scenarios. This article represents the first part of a two‐part study, focusing on the FEM modeling approach and associated challenges. The second part will address the experimental investigation in more detail, with an emphasis on thermal contrast behavior under varied transient conditions.
