Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm

doi:10.23940/ijpe.17.03.p1.247258

Abstract

Abstract:

Wind energy has turned into a massive contender of traditional fossil fuel energy. Due to environmental conditions and over-time run conditions, wind turbine blades are prompt to different vibrations which cause damage to the blades. This paper presents an algorithmic classification of various blade fault conditions like blade bend, blade cracks, blade erosion, hub-blade loose connection and pitch angle twist using vibration signals. Initially histogram features were extracted from the vibration data and classified using machine learning algorithms like hyperpipes (HP) and voting feature intervals (VFI) algorithm. The performance of these algorithms were compared with respect to classification accuracy and better algorithm was suggested for fault prediction on wind turbine blades.

Submitted on January 1, 2017; Revised on March 28, 2017; Accepted on April 12, 2017
References: 26

Joshuva. A.* Sugumaran. V.. Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm [J]. Int J Performability Eng, 2017, 13(3): 247-258.

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