Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

Self Cite

Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM approach for wind turbines based on the online sequential extreme learning machine (OS-ELM) algorithm. A physical kinetic energy correction model is employed to normalize the temperature change to the value at the rated power output to eliminate the effect of variable speed operation of the turbines. The residual signal, obtained by comparing the predicted values and practical measurements, is processed by the physical correction model and then assessed with a Bonferroni interval method for fault diagnosis. Models have been validated using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains various types of temperature data of the gearbox. The results show that the proposed method can detect more efficiently both the long-term aging characteristics and the short-term faults of the gearbox.

“…Therefore, the output weight matrix β (1) for the 1st chunk of training data N 1 is updated. Suppose…”

Section: Input Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Health Condition of the Wind Turbine Drivetrain System

Qian

Zhang

2017

Self Cite

“…This includes (a) a front interface, the portion of a device that directly interacts with the incident waves, (b) a power take-off (PTO) system used to transform the front-end energy into other forms of energy, like mechanical energy, and (c) an electrical energy generation system that takes the responsibility to do the final conversion. In the wind energy industry, the supervisory control and data acquisition (SCADA) system, which records hundreds of variables related to operational parameters, is installed in most modern wind farms [10]. Compared with wind turbines, the data available from WECs are not as abundant in quantity because of the presently immature ocean wave technologies.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

2018

Self Cite

Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

“…The current need for data gathering and analysis is highlighted in [8] proposing "new conceptual expert systems that automate data processing and provide analysis to enhance decision-making". Efforts have been made with such goal by implementing SCADA-data-driven methods [9] that also look into optimizing the number of sensors while reducing the detection time [10,11].…”

Section: Introductionmentioning

confidence: 99%

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Artigao

Koukoura

Carroll³

et al. 2018

Abstract:The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.