1. William Wei Song Dalarna University wso@du.se

2. 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

3. What is “Big data”? Five Vs. VALUE

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

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

6. 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)

7. 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)

8. Jiangxi Province Yudu County

9. A case - Wind Farm Typical small-scale Offshore Wind Farm at Lillgrund, in the Baltic Sea, Sweden

10. 10 Typical Turbine and its signals SCADA, < 0.001 Hz Continuous signals and alarms Structural Health Monitoring, SHM, < 5 Hz Not continuous Condition Monitoring, CM, < 35 Hz Continuous Diagnosis, 10 kHz Not continuous 3 blades machine Blade is pitch-able

11. 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)

12. Criteria to identify a WT Pitch System Failure 1. Irregular Motor Torque Difference 2. No significant change in Wind Speed 3. Maintenance Records

13. Get corresponding Alarm Pattern Alarm Pattern

14. 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.

15. Next Step – Neural Network 31 inputs, j hidden neurons and 2 outputs

16. Then – Training Artificial Neural Network Training Algorithm: Back-propagation network Minimum mean square error E was set to 0.0001

17. 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.

18. Then – Learn from a WT and apply on another WT 221 Alarm Patterns Applied to another 4 WTs Learned

19. Summary  “Big data” can support us to Trust the “data” 69%

20. Summary  “Big data” can support us to Trust the “data”  “Big data” cannot provide semantics (meaning) of data

21. 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

22. Transport