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Open Journal of Applied Sciences, 2018, 8, 211-225

http://www.scirp.org/journal/ojapps

ISSN Online: 2165-3925

ISSN Print: 2165-3917

Automatic Fault Prediction of Wind Turbine

Main Bearing Based on SCADA Data and

Artificial Neural Network

Zhenyou Zhang

Renewables & Utilities, Kongsberg Digital AS, Trondheim, Norway

Abstract

As the demand for wind energy continues to grow at exponential rate, redu

ing operation and maintenance (O & M) costs and improving

reliability have

become top priorities in wind turbine maintenance strategies. Prediction of

wind turbine failures before they reach a catastrophic stage is critical to reduce

the O & M cost due to unnecessary scheduled maintenance. A SCADA-

data

based condition monitoring system, which

takes advantage of data already

collected at the wind turbine controller, is a cost-

effective way to monitor

wind turbines for early warning of failures. This article proposes a methodo

ogy of fault prediction and automatical

ly generating warning and alarm for

wind turbine main bearings based on stored SCADA data using Artificial

Neural Network (ANN). The ANN model of turbine main bearing

normal

behavior is established and then the deviation between estimated and actual

values of the parameter is calculated. Furthermore, a method has been deve

oped to generate

early warning and alarm and avoid false warnings and

alarms based on the deviation. In this way, wind farm operators are able to

have enough time to plan maintenance, and

thus, unanticipated downtime

can be avoided and O & M costs can be reduced.

Keywords

Artificial Neural Network, SCADA Data, Wind Turbine, Automatic Fault

Prediction

1. Introduction

Power production from renewable sources becomes more and more important

globally to meet the increasing demand of power and reduce the effect of the

energy production on the environments, such as water and air, ecological system

How to cite this paper:

Zhang, Z.Y. (2018

)

Automatic Fault Prediction of Wind Tu

bine Main Bearing Based on SCADA Data

and Artificial Neu

ral Network.

Open Jou

nal of Applied Sciences

, 211-225.

https://doi.org/10.4236/ojapps.2018.86018

Received:

May 2, 2018

Accepted:

June 25, 2018

Published:

June 28, 2018

8 by author and

Scientific

Research Publishing Inc.

This work is licensed under the Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

DOI: 10.4236/ojapps.2018.86018 Jun. 28, 2018 211 Open Journal of Applied Sciences

Z. Y. Zhang

and climate change. Wind power is one of the most developed renewable energy

technologies worldwide with 432.491 GW installed capacity at the end of 2015

[1]. It has overtaken hydro as the third largest source of power generation in the

EU with a 15.6% share of total power capacity as seen in

Figure 1 which has

been increased remarkably from just tiny 2.4% in 2000 [2]. If the trend remains,

it is no doubt that the target of 20% wind energy share of energy production in

Europe will be met in 2020 [3] [4]. However, to achieve this target, the Levelised

cost of wind energy still should be reduced further. Today, large wind turbines

(2 - 10 MW) for both onshore and offshore from some wind equipment OEMs,

such as Siemens and Vestas, are designed, tested and manufactured as economi-

cally viable alternatives to traditional fossil-fueled power generation. At the same

time, the cost of wind energy has decreased sharply in last two decades which

makes both onshore and offshore wind power more competitive than conven-

tional power generation sources, such as coal and gas, and other renewable

energy [5]. Even though, the cost of wind power production, especially for off-

shore wind power, need to be reduced further to make it more competitive to

other resources. Operations and Maintenance (O & M) has been estimated at up

to 20% - 30% of overall lifetime costs for energy [6] [7]. It is really making sense

to reduce the O & M cost by reducing unanticipated downtime through an au-

tomatic condition monitoring system.

The best way to reduce the O & M cost of wind energy is to reduce downtime

through online condition monitoring to enable the operators to plan mainten-

ance action when and only when it needed. Wind turbines often operate in se-

vere and remote environments which make the limitation of accessibility when

the maintenance actions are needed. Therefore, unscheduled maintenance due

to unexpected failures, even just small issues, can be very costly, not only for

maintenance actions but also for lost production time. From the operators’ view, it

Figure 1. Comparison of EU Power Mix in 2000 and 2015 [2].

DOI: 10.4236/ojapps.2018.86018 212 Open Journal of Applied Sciences

Z. Y. Zhang

is, therefore, worth increasing effort spent to monitor the wind turbine condition

in order to reduce unanticipated downtime and thus reduce the maintenance

cost and production time loss. Therefore, prediction of faults of wind turbines as

early as possible becomes the key part of condition monitoring. In the other

hand, false critical alarms may cause even more cost due to lots of resources

maybe used for the maintenance that is not needed.

SCADA system, which is installed with almost all wind turbines, mainly logs

the performance parameters of wind turbines. It would be beneficial for the

wind farm owners if the SCADA data can be analysed and interpreted auto-

matically to predict faults early enough to help them to do the maintenance ac-

tions just when it is needed.

There are mainly two methodologies for fault prediction of wind turbines:

model-based or data-based. The model-based methods require a comprehensive

physical or mathematical model which is usually unavailable. Data-based methods

are based on the history data and data mining technologies are used to find the

relations among variables in the data. Wind turbine mathematical/physical

models are normally unavailable but the data has been collected and stored via

SCADA system, and thus data-based technologies are preferred [8]. There are

some works in literature to use SCADA data analysis to detect faults of wind

turbine components with model-based or data-driven methodologies [9] [10]

[11] [12] [13], however, none of them consider to design a mechanism to avoid

false warning and false alarms due to sudden changes of wind turbine running

condition which the model may be not fast enough to capture these changes.

This paper describes the methodology to automatically predict incipient faults

of wind turbine main bearings by analyzing SCADA data based on Artificial

Neural Network (ANN), which has been implemented in EmPower® developed

by Kongsberg Digital AS. The ANN model of turbine main bearing normal be-

haviour is established firstly and then the deviation between estimated and ac-

tual values of the parameter is calculated.The deviation is used to be an indicator

of abnormal behavior of main bearing. To avoid the false warning/alarm, a time

interval (

i.e.

a week) is used to calculate the percentage of time that the deviation

is above warning/alarm threshold. In this way, incipient faults are detected in

advance and false warning/alarm is avoided, and thus, only actual warn-

ings/alarms are sent to the operator.

The remaining sections of this paper are organized as follows. Section 2 de-

scribes the principle of Artificial Neural Network (ANN). Section 3 proposes the

procedure of fault prediction and warning/alarm generation. Section 4 describes

how the normal behavior ANN model is established based on SCADA data. Sec-

tion 5 proposes how the warning and alarm can be generated automatically

based on established normal behavior ANN model. Section 6 describes how the

proposed procedure to be applied in online monitoring for wind turbines and

some cases are shown. Section 7 gives the conclusions and indicates some future

works.

DOI: 10.4236/ojapps.2018.86018 213 Open Journal of Applied Sciences

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