scholarly journals Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 201 ◽  
Author(s):  
Kevin Leahy ◽  
Colm Gallagher ◽  
Peter O’Donovan ◽  
Dominic T. J. O’Sullivan
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Maintenance Strategies ◽  
Scada System ◽  
System Data ◽  
The Common ◽  
Common Problems ◽  
Original Equipment Manufacturers ◽  
Historical Periods ◽  
Operational Data

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting.

scholarly journals Deep Learning Method for Fault Detection of Wind Turbine Converter

2021 ◽  
Vol 11 (3) ◽  
pp. 1280 ◽  
Author(s):  
Cheng Xiao ◽  
Zuojun Liu ◽  
Tieling Zhang ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Network Models ◽  
Detection Accuracy ◽  
Neural Network Models ◽  
Scada System ◽  
Indicator Variables ◽  
System Data

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.

scholarly journals Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6601
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
Charlie Plumley
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Supervisory Control ◽  
Data Acquisition System ◽  
Novel Technique ◽  
Machine Learning Model ◽  
System Data ◽  
Pitch System ◽  
Run Up ◽  
Isolation Forest

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

scholarly journals A Survey of Deep Learning Approaches on Wind Turbine Condition Monitoring

2021 ◽  
Vol 2132 (1) ◽  
pp. 012015
Author(s):  
Sijia Li
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Control Charts ◽  
Original Data ◽  
Learning Approaches ◽  
Monitoring Methods ◽  
Effectiveness And Efficiency ◽  
Operational Data

Abstract Current physics-based wind turbine monitoring methods often need extra sensors installed on wind turbines, thus increasing the operation and maintenance (O&M) cost. Besides, physical methods are only effective under some constraints. The real effectiveness needs to be further checked in real conditions. Recent advances in data acquisition systems allow collection of large volumes of operational data of wind turbines. Learning knowledge from the data allows us to do monitoring in another direction. In this paper, a survey of deep learning algorithms applied to wind turbine condition monitoring is given. Compared with original data, more meaning features were extracted through feature extraction of deep learning. Monitoring these new signals, outliers were detected by applying suitable control charts. Several industrial cases confirmed the effectiveness and efficiency of these frameworks.

Application of FastICA Algorithm in Wind Turbine Condition Monitoring

2012 ◽  
Vol 217-219 ◽  
pp. 2750-2753
Author(s):  
Guang Kun Shan ◽  
Hai Long Zhang ◽  
Xiao Dong Wang ◽  
Ying Ming Liu
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Processing Method ◽  
Vibration Signals ◽  
Signal Processing Method ◽  
Ideal Position ◽  
Fastica Algorithm ◽  
The Common ◽  
Common Signal ◽  
The Ideal

In wind turbine condition monitoring, the sensors often can not be installed to the ideal position. Compare the common signal processing method comprehensively and give the advantage of the fastICA algorithm in the wind turbine condition monitoring. Give the basic principle and mathematical model of the fastICA algorithm, while monitor and analysis the wind turbine state data based on the fastICA algorithm. The results show that this algorithm can separate the vibration characteristics of the tested compenent of the wind turbine from the vibration signals quickly and accurately.

Applying Possessive pronoun in Writing Sentences: Exploring Students’ Ability and Problems in a Mixed Method Study

2019 ◽  
Vol 1 (3) ◽  
pp. 79-83
Author(s):  
Eka Utami Putri ◽  
Syahdan Syahdan
Keyword(s):  
Mixed Method ◽  
Simple Random Sampling ◽  
Average Score ◽  
Mixed Method Study ◽  
Z Score ◽  
Possessive Pronoun ◽  
Efl Students ◽  
The Common ◽  
Common Problems ◽  
Essay Test

The purpose of this research was to find out the students' ability in applying Possessive pronoun in writing sentences and the problems encounter it.  This mixed method study employs an explanatory design to reveals it. 53 students out of 105 students from1st semester EFL students from one reputable University in Pekanbaru, Indonesia, were invited to this study. These 53 students were selected using simple random sampling and enrolled for an essay test and interview to see the students' ability and explaining the problems. The data analysis using SPSS showed that the average score of students was 52.98. Meanwhile for the median is 48, the mode is 20. The score of Standard Deviation is 27.93, Variance is 780.25, and Range is 84.  Z-Score was found 41.5%, which is means higher than average and 58.5% while, students' ability was indicated below the average. It showed that the students were low ability in applying possessive pronoun in writing sentences. The study also found the common problems, i.e., (1) students still mixed up between possessive pronoun and possessive adjectives. (2) students used the wrong pattern in using a possessive pronoun. (3) students did not understand clearly about a possessive pronoun, (4) experiencing difficulties in learning possessive pronoun. 

scholarly journals Common issues in the hetero-epitaxial seeding on SiC substrates in the sublimation growth of AlN crystals

2021 ◽  
Vol 127 (8) ◽  
Author(s):  
R. Radhakrishnan Sumathi
Keyword(s):  
Power Electronic ◽  
Etch Pit ◽  
Micro Hole ◽  
Wet Chemical ◽  
The Common ◽  
Common Problems ◽  
Hole Defects ◽  
Sublimation Growth ◽  
Impurity Elements ◽  
Very High

AbstractAluminium nitride (AlN) is a futuristic material for efficient next-generation high-power electronic and optoelectronic applications. Sublimation growth of AlN single crystals with hetero-epitaxial approach using silicon carbide substrates is one of the two prominent approaches emerged, since the pioneering crystal growth work from 1970s. Many groups working on this hetero-epitaxial seeding have abandoned AlN growth altogether due to lot of persistently encountered problems. In this article, we focus on most of the common problems encountered in this process such as macro- and micro-hole defects, cracks, 3D-nucleation, high dislocation density, and incorporation of unintentional impurity elements due to chemical decomposition of the substrate at very high temperatures. Possible ways to successfully solve some of these issues have been discussed. Other few remaining challenges, namely low-angle grain boundaries and deep UV optical absorption, are also presented in the later part of this work. Particular attention has been devoted in this work on the coloration of the crystals with respect to chemical composition. Wet chemical etching gives etch pit density (EPD) values in the order of 105 cm-2 for yellow-coloured samples, while greenish coloration deteriorates the structural properties with EPD values of at least one order more.

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