Title: Improvement in wind energy production through condition monitoring of wind turbine blades using vibration signatures and ARMA features: a data-driven approach

Authors: A. Joshuva; V. Sugumaran

Addresses: Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, Tamil Nadu 603103, India ' School of Mechanical and Building Sciences (SMBS), Vellore Institute of Technology, Chennai Campus, Vandalur-Kelambakkam Road, Chennai 600127, India

Abstract: The main objective of this study is to improve the wind energy productivity by implementing the condition monitoring technique for wind turbine blades through vibration source. The fault detection and the isolation of the fault which affects the wind energy productivity were carried using machine learning algorithms. In this study, a three bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults which affect the turbine blade. Initially, vibration sources were collected from the wind turbine using piezoelectric accelerometer and from that vibration source; needed features are extracted using ARMA through MATLAB. From the extracted feature, the dominating feature is selected using J48 decision tree algorithm and with the selected features, fault classification has been carried out. The fault classifications were carried out using Bayesian, function and lazy classifiers.

Keywords: wind turbine blade; ARMA features; machine learning; vibration signals; condition monitoring.

DOI: 10.1504/PIE.2019.100889

Progress in Industrial Ecology, An International Journal, 2019 Vol.13 No.3, pp.207 - 231

Received: 27 Feb 2018
Accepted: 12 Jul 2018
Published online: 19 Jul 2019 *

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