Jan Helsen

Towards an evolvable data management system for wind turbines

There is a trend towards large wind farms clustering a significant amount of turbines, aiming at continuously optimizing design and maintenance costs and thus reducing the overall cost of energy. Advanced control algorithms and maintenance optimizations may affect the lifetime of the turbines. Therefore it is necessary to actively monitor turbine behavior and condition. In order to fulfill this requirement, clean and accurate data sets need to be available. This data can be diverse in type and might evolve in a progressive innovative environment. In this paper, we present a system which can store and manage such data. Because of non-functional requirements as evolvability, maintainability, scalability and diversity, the Normalized Systems Theory was taken as theoretical foundation for the development of this system. Since the theorems of this theory are not always easy to grasp for practitioners, we combined our development with the derivation of a set of rules tailored to the application domain of our technology stack. HighlightsWe illustrate that structural health monitoring of wind turbines requires evolvable data management systems.A theoretical foundation on how to design evolvable systems is given.We present 7 derived rules to facilitate implementation.We present an application of an evolvable data management system.

Long-Term Automatic Tracking of the Modal Parameters of an Offshore Wind Turbine Drivetrain System in Standstill Condition

Modal behavior of a wind turbine is an important design aspect for tackling noise, vibration, and harshness (NVH) issues and validating complex simulation models. This paper focusses long-term modal analysis on an offshore wind turbine (OWT) in stand still conditions. It presents the results of an automated procedure to track the variation of the modal parameters of the drivetrain system of the OWT. The tracking focuses on the continuous monitoring of the resonant frequencies and damping values of the most dominant modes of the drivetrain unit during more than half a day of stand still. The long-term tracking of the natural frequencies and modal damping of the drivetrain vibration modes under distinct ambient conditions allows to better understand the dynamics of the drivetrain by gaining confidence in modal parameters estimated over multiple measurement blocks and helps in gaining understanding in the dynamics of the OWT. The used automatic tracking procedure is based on the well-known parametric operational modal analysis algorithm, pLSCF estimator. The experimental data used in this paper has been obtained during a long-term measurement campaign lasting 6 months on an offshore wind turbine with instrumentation directly mounted on the drivetrain. Both eigenfrequencies and damping values are of particular interest.

Influence of the Harmonics on the Modal Behavior of Wind Turbine Drivetrains

In the last decades, noise, vibration and harshness (NVH) problems became critical issues to be tackled by the wind industry. They have been caused by the upscaling trend that has imposed bigger (not quasi-static) loads on turbine subcomponents: the dynamic loads are significantly influencing the fatigue life of the wind turbine components and the tonalities generated. To validate complex simulation models, it is of high interest to continuously track the modal parameters of the fundamental modes of a wind turbine during operating conditions. At this purpose, operational modal analysis (OMA) represents a powerful tool.

Robust Power-Electronic-Converter Fault Detection and Isolation Technique for DFIG Wind Turbines

This paper proposes a robust fault detection and isolation (FDI) technique for the power electronic converter (PEC) of doubly-fed induction generator (DFIG) wind turbines (WTs), and in particular for open-circuit faults herein. It combines fault indicators based on the processing of the Clarke transformation of the converter currents and a statistical change detection algorithm, namely a cumulative sum (CUSUM) algorithm that detects significant changes in the variance of the reactive power. This allows for a reduction of the false alarm rate compared to an approach relying exclusively on the current analysis. The proposed FDI technique is validated by means of both simulation and experimental results.

Modal Parameters Estimation of an Offshore Wind Turbine Using Measured Acceleration Signals from the Drive Train

Offshore Wind Turbine (OWT) is complex structure that consists of different parts (e.g. foundation, tower, drivetrain, blades, …). The last decade there is a continuous trend towards larger machines with the goal of cost reduction. Modal behavior is an important design aspect. For tackling NVH issues and validating complex simulation models it is of high interest to continuously track the vibration levels and the evolution of the modal parameters (resonance frequencies, damping ratios, mode shapes) of the fundamental modes of the turbine. Wind turbines are multi-physical machines with significant interaction between their subcomponents. This paper will exploit this and present the possibility of identifying and tracking consistently the structural vibration modes of the drivetrain of the instrumented offshore wind turbine by using signals (e.g. acceleration responses) measured on the drivetrain system. The experimental data has been obtained during a measurement campaign on an offshore wind turbine in the Belgian North Sea where the OWT was in standstill condition. The drivetrain, more specifically the gearbox, is instrumented with a dedicated measurement set-up consisting of 17 sensor channels with the aim to continuously track the vibration modes. The consistency of modal estimates made at consequent 10-min intervals is validated, and the dominant drivetrain modal behavior is identified.

Pattern Mining for Learning Typical Turbine Response During Dynamic Wind Turbine Events

Maintenance costs are a main cost driver for offshore wind energy. Prediction of failure and particularly failure understanding can help to bring these costs down significantly. Since the wind turbine is subjected to a large number of dynamic events it is important to fully understand the turbine response to these events. Pattern mining has been used successfully for different applications. We believe it to have large potential for understanding turbine behavior based on turbine status logs. These logs record all turbine actions and can be used as input for pattern mining algorithms. This paper proposes the use of a multilevel pattern mining approach in order to minimize the number of uninteresting patterns and facilitate response understanding. The paper mainly focuses on the extraction of patterns and association rules linked to certain alarms and how they can be annotated for further use in the multi-level pattern mining approach. Several years of wind turbine data is used. The use of the approach is illustrated by detecting the characteristic pattern linked to turbine response to an Extremely High Wind Speed Alert.