Simplified automatic fault detection in wind turbine induction generators

Katharine Brigham,Donatella Zappalá,Christopher Donaghy‐Spargo,Christopher J Crabtree

doi:10.1002/we.2478

Abstract

AbstractThis paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.

Highlights

Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimizing wind turbine (WT) operation and maintenance (O&M) costs for competitive development of offshore wind energy.[1]
Numerical measures of the system performance were taken to be the system accuracy and the false positive rate (FPR), which is computed as the number of ‘‘healthy’’ samples incorrectly categorized as ‘‘faulty’’ over the total number of samples that was determined to be ‘‘faulty.’’ The False Positive Rate (FPR) is reported in addition to the system accuracy, as it is an important measure in health monitoring,since declaring a fault when the system is healthy would likely result in unnecessary expenditure of time and money to investigate a non-issue, and the FPR should be extremely low
This paper proposes an automated WT induction generator fault detector that requires very little training data, is robust against variable speeds, and achieves high accuracy at very little computational cost

Summary

Introduction

Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimizing wind turbine (WT) operation and maintenance (O&M) costs for competitive development of offshore wind energy.[1]. Attention at the incipient fault stage is required to avoid fault escalation leading to breakdown

Methods

Results

Conclusion