Abstract
Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer. The statistical, histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.
Highlights
As in the request on energy creation from renewable sources continually rises, modern advancements in wind turbine design are imposed by improved requirements imitated in wind turbine size increment for yielding more energy from available wind source, advanced in blade design, equipped control system advancement, and enhanced structural health and condition monitoring (SCHM)
The main achievement of a wind energy development is depending on the dependability of a wind turbine structure
In J48 decision tree algorithm, the minimum number of instances per leaf and the number of data used for reduced-error pruning was kept at 50
Summary
As in the request on energy creation from renewable sources continually rises, modern advancements in wind turbine design are imposed by improved requirements imitated in wind turbine size increment for yielding more energy from available wind source, advanced in blade design, equipped control system advancement, and enhanced structural health and condition monitoring (SCHM). Apart from previously mentioned necessities, the expenditures of energy production must be at least similar with the expenditures of energy generation from regular sources to create wind turbine structure a mechanically satisfactory. The wind turbine machinery has benefits among other uses of renewable energy machineries due to its technological development, superior structure and comparative cost affordability [Manwell, McGowan and Rogers (2010)]. The main achievement of a wind energy development is depending on the dependability of a wind turbine structure. Inadequate dependability will right away result in the raise of operation and maintenance cost and fall in the lifetime of the wind turbine structure
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