william wei song dalarna university wso@du.se. outline what is “big data”? from where comes...

WHAT “BIG DATA” & IoT CAN AND CANNOT

DO?- WIND ENERGY DATA ANALYSIS

William Wei Song

Dalarna University

wso@du.se

Outline

What is “Big Data”? From where comes big data Smart cities

A smart county project brief Big data analysis

A case study of wind farms What “big data” can and cannot do

What is “Big data”? Five Vs.

VALUE

Profit

ProftProfit

From where we get “Big Data” At technic level - Internet of Things (IoT)

From where we get “Big Data” At application level - Smart cities (IDF2013)

Smart Yudu– a joint project A descriptive outline of the county (data as public transport

data, traffic data, weather data, governmental statistic data, demographic data, healthcare data)

Data analysis (vertical analysis – to analyse the data to find out the places, time, casuality in a traffic accident; horizontal analysis – making a historical data analysis; comparison study)

Visualised presentation (dynamically show the place of accident – e.g. deluge causes traffic problem)

Service provision analysis (transport of a wind turbine blade)

Smart Yudu – problems Problems in the process of collection of “big data”

Data volume versus time scope Data structures (rational data) versus data formats (XML data or NoSQL

data) Data granularity (e.g. weather – taken once a day or once an hour) Data values and metadata (meanings of data)

Processing of the data Organizing and cleansing Digestion and structuring Classification and clustering Preliminary data analysis (solving problems of incompleteness and

inaccuracy, missing data) Requiring feedbacks from the users (county government)

Jiangxi Province

Yudu County

A case - Wind Farm

Typical small-scale Offshore Wind Farm at Lillgrund, in the Baltic Sea, Sweden

10

Typical Turbine and its signals

SCADA, < 0.001 HzContinuous signals and alarms

Structural Health

Monitoring,SHM,< 5 Hz

Not continuous

ConditionMonitoring,

CM,< 35 Hz

Continuous

Diagnosis,10 kHzNot continuous

• 3 blades machine• Blade is pitch-able

SCADA Data• Signals

• Measured in every 10 minutes• 90 different signals/channels. For example: wind speed, power

output and nacelle temperature.• Alarms

• 673 different alarms• The average alarm rate is up to 15 alarms per 10 minutes • The maximum alarm rate is 1,570 alarm per 10 minutes. • Wind Farm operators are not able to handle such big amount of

alarms.• Obviously, alarm is rich in information and we do need to

provide a solution to reduce the alarm rate.• Consider a Pattern Recognition approach to analyse WT alarm

(Pitch System failure as a Case study)

Criteria to identify a WT Pitch System Failure

1. Irregular Motor Torque Difference

2. No significant change in Wind Speed

3. Maintenance Records

Get corresponding Alarm Pattern

Alarm Pattern

Result from a WT with Pitch Problem By analysing the data of a WT from 17/05/2006 to 01/10/2008, we found:

• 31 Pitch System alarms• 5,739 alarms in this period. (Note: this only count the 31 relevant alarms)• 221 different alarm patterns• Among 221 different alarm patterns, there are:

• 15 alarm patterns stand for Pitch System failure and,• 206 stand for no Pitch System failure.

Next Step – Neural Network

31 inputs, j hidden neurons and 2 outputs

Then – Training Artificial Neural Network

• Training Algorithm: Back-propagation network• Minimum mean square error E was set to 0.0001

ANN Models By choosing different number of neuron j in hidden layer,

3 different ANN models were constructed. They were:

Table: Training cycles to achieve mean square error to 0.0001

The optimum number of hidden layer neurons was 50.

Then – Learn from a WT and apply on another WT

221 Alarm Patterns

Applied to another 4 WTs

Learned

Summary

“Big data” can support us to Trust the “data”

69%

Summary

“Big data” can support us to Trust the “data”

“Big data” cannot provide semantics (meaning) of data

What Big Data can and cannot do? "Big Data", from a variety of

sources, e.g. mobile devices, are not only big, but also increases at astonishing speed in size and have diverse forms. The conventional methods and tools no longer meet the demands to solve massive data analysis problems in the application domains such as e-commerce, bioinformatics and medical informatics, and energy consumption. This lecture intends to discuss a possible solution – transforming data (information) into semantics: relationships and conceptual relativity. – William Song

Questions, please!

Wei Song (William) fil. dr.Professor i Informatik & Business Intelligence

Head of Business Intelligence

Akademin Industri och Samhälle

Högskolan Dalarna

Telefon: +46 23 77 87 60

Fax: +46 23 77 81 00

E-post: wso@du.se

Postadress: Box 175, SE-791 88, Falun, Sweden

Besöksadress: Röda vägen 3, Borlänge, Sweden

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