Integrating Auto-Associative Neural Networks with Hotelling T2 Control Charts for Wind Turbine Fault Detection

Hsu-Hao Yang,Shih-Wei Yang,Mei-Ling Huang

doi:10.3390/en81012100

Hsu-Hao Yang, Shih-Wei Yang + Show 1 more

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https://doi.org/10.3390/en81012100

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Abstract

This paper presents a novel methodology to detect a set of more suitable attributes that may potentially contribute to emerging faults of a wind turbine. The set of attributes were selected from one-year historical data for analysis. The methodology uses the k-means clustering method to process outlier data and verifies the clustering results by comparing quartiles of boxplots, and applies the auto-associative neural networks to implement the residual approach that transforms the data to be approximately normally distributed. Hotelling T2 multivariate quality control charts are constructed for monitoring the turbine’s performance and relative contribution of each attribute is calculated for the data points out of upper limits to determine the set of potential attributes. A case using the historical data and the alarm log is given and illustrates that our methodology has the advantage of detecting a set of susceptible attributes at the same time compared with only one independent attribute is monitored.

Highlights

Wind energy has become one of major sources of renewable energy because of growing environmental concerns
This section discusses the results of processing outliers, training the associative neural networks (AANN) model, and constructing the Hotelling T2 control charts for detecting the potential attributes
This study proposes a three-phase methodology for detecting a set of attributes of the wind turbine using the supervisory control and data acquisition (SCADA) data

Summary

Introduction

Wind energy has become one of major sources of renewable energy because of growing environmental concerns. A wind turbine extracts energy from the wind and the amount of energy extracted depends largely on the wind speed. The power generated by a turbine at various wind speeds is described by a power curve that resembles a sigmoid function. Due to the stochastic nature of wind, main components of wind turbines like blades and generators are susceptible to various types of faults. The frequency and severity of the faults affect operations and maintenance costs, and unscheduled shutdowns are costly. Condition and performance monitoring methodologies have been developed to detect early faults and reduce unscheduled shutdowns; reviews of the proposed methodologies and future research trends are provided [1,2,3]

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