Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Mingzhu Tang,Qi Zhao,Kang Zhang,Bin Huang,Wen Long,Huawei Wu,Lida Liao,Wei Chen

doi:10.3390/en12173396

Abstract

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

Highlights

With environmental concerns of fossil-fuel-based energy consumption, renewable energy has been playing a predominant role in clean and cost-efficient energy production, contributing significantly to the sustainability of human society
Diagram indicated that the results8 detected byemployed the Support vector vector machines machines (SVM) model areerrors desirable when the gearbox is working value variations
This study presents an support vector regression (SVR) based method which is of the ability in troubleshooting for large-scale wind turbines

Summary

Introduction

With environmental concerns of fossil-fuel-based energy consumption, renewable energy has been playing a predominant role in clean and cost-efficient energy production, contributing significantly to the sustainability of human society. One of these renewable energy sources is the large-scale wind turbines with a doubly-fed induction generator (DFIG), which have been widely installed attributing to their low lifecycle costs. These faults take the responsibility of unexpected system breakdown and extra expenditures on turbine maintenance [1]. The literature review indicates that most of those fault diagnosis methods are based on vibration and acoustic signals, vibration signals monitored with additional vibration sensors and equipment will result in extra investment on maintenance, as well as the increase of computational load and difficulty in signal processing

Methods

Results

Conclusion