A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems

Monitoring the variation of the loading blades is fundamental due to its importance in the behavior of the wind turbine system. Blade performance can be affected by different loads that alter energy conversion efficiency and cause potential safety hazards. An example of this is icing on the blades. Therefore, the main objective of this work is to propose a proportional digital controller capable of detecting load variations in wind turbine blades together with a fault detection method. An experimental platform is then built to experimentally validate the main contribution of the article. This platform employs an automotive throttle device as a blade system emulator of a wind turbine pitch system. In addition, a statistical fault detection algorithm is established based on the point change methodology. Experimental data support our approach.

Analysis and Detection of Erosion in Wind Turbine Blades

This paper studies erosion at the tip of wind turbine blades by considering aerodynamic analysis, modal analysis and predictive machine learning modeling. Erosion can be caused by several factors and can affect different parts of the blade, reducing its dynamic performance and useful life. The ability to detect and quantify erosion on a blade is an important predictive maintenance task for wind turbines that can have broad repercussions in terms of avoiding serious damage, improving power efficiency and reducing downtimes. This study considers both sides of the leading edge of the blade (top and bottom), evaluating the mechanical imbalance caused by the material loss that induces variations of the power coefficient resulting in a loss in efficiency. The QBlade software is used in our analysis and load calculations are preformed by using blade element momentum theory. Numerical results show the performance of a blade based on the relationship between mechanical damage and aerodynamic behavior, which are then validated on a physical model. Moreover, two machine learning (ML) problems are posed to automatically detect the location of erosion (top of the edge, bottom or both) and to determine erosion levels (from 8% to 18%) present in the blade. The first problem is solved using classification models, while the second is solved using ML regression, achieving accurate results. ML pipelines are automatically designed by using an AutoML system with little human intervention, achieving highly accurate results. This work makes several contributions by developing ML models to both detect the presence and location of erosion on a blade, estimating its level and applying AutoML for the first time in this domain.

Numerical and Experimental Investigations on a Bio-Inspired Design of Darrieus Vertical Axis Wind Turbine Blades With Leading Edge Tubercles

Various improvements can be made to Darrieus vertical axis wind turbines (VAWT) for maximum performance in an urban environment. One such improvement is the inclusion of bio-inspired leading-edge tubercles to increase the aerodynamic performance. These structures, found on the flippers of humpback whales, are believed to aid the mammal in quick maneuvering. The objective of the chapter is to investigate and compare the performance of a Darrieus type VAWT with the inclusion of leading edge tubercles. The performance of the turbine with leading-edge tubercles on the blades is compared with the turbine with normal blade, computationally (with computational fluid dynamics using transition SST turbulence model) and experimentally. The focus lies on building an experimental setup to compare the performance of leading-edge tubercles with the baseline turbine.