”Big Data” Initiative as an IT Solution for Improved Operation and Maintenance of Wind Turbines

Zsolt János Viharos, Csaba István Sidló, András A. Benczúr, János Csempesz, Krisztián Balázs Kis, István Petrás, András Garzó

Computer and Automation Research Institute of the Hungarian Academy of Sciences (MTA SZTAKI)

Laboratory on Engineering and Management Intelligence and Informatics Laboratory

Big Data Business Intelligence Research Group

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Mo

de

l e

stim

ati

on

err

or

(%)

[lim

it:

+/

-17

%]

Tem

pe

ratu

res

Time - a year

Non-conform situation detection - estimation of the gearbox bearing temperature by a neural network modell

(Model validity: ambient temperature between 4 and 10 C)

Values_for_Model_INPUT_2 Values_for_Model_INPUT_1

Gearbox bearing temperature_MODEL_ESTIMATES Gearbox bearing temperature_MEASURED

Ambient temperature (for model vaildity) Error_%

PLCs

SCADA

Local compute

rCentral server

Parameters/TimeStamp

parameter1

parameter2

parameter3

parameter4

parameter5

parameter6

parameter7

1. time point 98 98 34 34 29 59 572. time point 99 99 34 36 29 60 573. time point 97 97 34 40 29 60 574. time point 98 98 34 41 29 61 575. time point 86 86 34 41 29 61 576. time point 98 98 34 41 29 62 577. time point 98 98 35 43 29 63.75 588. time point 99 99 35 43 29 66 599. time point 97 97 35 44 29 66 5910. time point 98 98 35 44 29 66 5911. time point 98 98 35 44 29 66 5912. time point 99 99 35 44 29 65 5913. time point 97 97 35 44 29 65 5914. time point 98 98 35 44 29 64 5915. time point 86 86 35 44 30 63 5916. time point 98 98 35 44 30 63 5817. time point 100 100 34 43 30 63 5818. time point 102 102 34 43 30 62 5819. time point 104 104 34 42 30 62 5820. time point 102 102 34 42 30 62 58

Analysis

DWGenerated input data in

SQL SERVER

SQOOP

HADOOP

HIVEHIVE in HDFS

HIVE in HDFS

SQOOP

Aggregated data inSQL SERVER

SQL queries

NOSQL

SQL

Big Data intiativelayers

3

MySQL

PostgreSQLHadoop

Storm

InfoBright GreenPlumVertica

HBaseNetezza

MapR

VoltDB

OracleSQLServer

Cloudera

custom hardware

Matlab

R

SPSS

SASMahout

custom software

Revolution

GB Size PB

IT logs

fraud detection

wind turbine sensors

navigation, mobility

media pricing

Web content

online reputation

Size

BigAnalytics

FastData

Speed

Real time

Batch

Data processing alternatives for wind farm data

4

SQL SQL

DW

SQL

DW

Big Data layer ETL

DW

Big Data layer

SQL adapter Streaming

Real time alarms

Present Present with DW Big Data with ETL Direct Big Data

Wind farm Wind farm Wind farm Wind farm Wind farm Wind farm Wind farm Wind farm

Data flow of the SQL and Big Data (NOSQL) prototypes

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SQ

LS

QL

DW

SQ

L

DW

Big

Da

ta la

yer E

TL

DWB

ig D

ata

laye

r

SQ

L a

da

pte

rS

trea

min

g

Re

al tim

e

ala

rms

Present

Present w

ith DW

Big D

ata with E

TL

Direct B

ig Data

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

DWGenerated input data in

SQL SERVER

SQOOP

HADOOP

HIVEHIVE in HDFS

HIVE in HDFS

SQOOP

Aggregated data inSQL SERVER

SQL queries

NOSQL

SQL

Benchmarking data set●The task is to load a “heavy” aggregate wind farm data cube

●number of commands,●the number and average, minimum, maximum and standard deviation of the length of all alarms, warnings and events,

●number and average length of different statuses●minimum, maximum, average and standard deviation of 8 selected typical, mostly relevant signals.

6

DWGenerated input data in

SQL SERVER

SQOOP

HADOOP

HIVEHIVE in HDFS

HIVE in HDFS

SQOOP

Aggregated data inSQL SERVER

SQL queries

NOSQL

SQL

Data processing times (SQL vs. Big Data)

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140

0 20 40 60 80 100 120 140 160 180

exec

utio

n tim

e (m

inut

es)

number of wind farms

SQL Server

Hadoop and Hive (2 nodes)

Hadoop and Hive (48 nodes)

SQ

LS

QL

DW

SQ

L

DW

Big

Da

ta la

yer E

TL

DWB

ig D

ata

laye

r

SQ

L a

da

pte

rS

trea

min

g

Re

al tim

e

ala

rms

Present

Present w

ith DW

Big D

ata with E

TL

Direct B

ig Data

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

20072008

20092010

2011

05000000

100000001500000020000000250000003000000035000000400000004500000050000000

70 71 72 73 74

Year

s

Cum

ulati

ve C

OM

MA

ND

num

ber

Wind farms

Cumulated COMMAND numbers for five wind farms by years

0

10

20

30

40

50

60

70

80

90

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

2008

01

2008

02

2008

03

2008

04

2008

05

2008

06

2008

07

2008

08

2008

09

2008

10

2008

11

2008

12

2008

Mon

thly

ave

rage

:N

acel

le te

mpe

ratu

re, R

ectiv

e ne

twor

k po

wer

,W

ind

spee

d

Mon

thly

ave

rage

pro

duce

d en

ergy

Months of 2008

Monthly average SCADA signal values at wind farm level

Produced energy

Wind turbine temperature

Network power

Wind speed

0

5

10

15

20

25

0

1000000

2000000

3000000

4000000

5000000

6000000

Mon

thly

ave

rage

of t

he n

acel

le's

tem

pera

ture

s

Cum

ulati

ve C

OM

MA

ND

num

ber

Year: 2008

Monthly cumulative COMMAND number and average wind turbine temperature for a wind farm in 2008

Command Number

Average Wind Turbine Temperature

Business Intelligence example reports

8

SQ

LS

QL

DW

SQ

L

DW

Big

Da

ta la

yer E

TL

DWB

ig D

ata

laye

r

SQ

L a

da

pte

rS

trea

min

g

Re

al tim

e

ala

rms

Present

Present w

ith DW

Big D

ata with E

TL

Direct B

ig Data

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Wind

farm

Contact

Dr. Zsolt János VIHAROS

MTA SZTAKIzsolt.viharos@sztaki.mta.hu www.sztaki.hu/~viharos

http://bigdatabi.sztaki.hu/

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