global tech i offshore wind gmbh am sandtorkai 62, dock 4 ...sdienst/publications... · 80 turbines...

Global Tech I Offshore Wind GmbH

Am Sandtorkai 62, Dock 4

D-20457 Hamburg

© Global Tech I Offshore Wind GmbH Automatic Anomaly Detection In SCADA Data

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Profile Jonas Beseler

Professional Experience

Asset Manager WEC

Global Tech I

Asset Manager

Diplom-Wirtschaftingenieur (FH)

Asset Management

Reporting / Data Analytics

Database

Onshore wind power

Skills and Focus

2008 Diploma in Industrial Engineering University of Applied Sciences in Darmstadt

2008 – 2015 SEP GmbH, Buxtehude (Independent experts wind power onshore)

2015 Global Tech I

Fleet Monitoring & Data Analysis- Automatic Anomaly Detection 2

Profile Steffen Dienst

Professional Experience

PhD student

Diplom-Informatiker (Uni)

Software engineering

Functional programming

Anomaly Detection

Machine Learning

Skills and Focus

2008 Diploma in Computer Science, University of Leipzig

2008 – 2009 Software Developer at stoneball, working for Siemens Industry

2009 – 2016 PhD student with Prof. Fähnrich

2013 – Thesis: „Efficient Condition Monitoring of Renewable Energy Power Plants“


Name:

Location:

Size:

Water depth:

Wind turbines:

Rated output per turbine:

Total output:

„Global Tech I“

more than 100 km off the coast

ca. 41 km²

38 - 40 m

80 WEC AD 5-116 (ADWEN)

5 MW

400 MW

The Offshore Wind Farm Global Tech I


History of the Project

Date Description

2001 Nordsee Windpower GmbH submits an application to the BSH for authorisation

2006 BSH issues the consent for construction and license for operation

2009

Multibrid GmbH (now: Areva Wind GmbH) signs a preliminary contract for the supply of 80 wind turbines

and AREVA Energietechnik (now: Alstom Grid GmbH) is awarded a contract for the planning of the

transformer station

May 2010

TenneT TSO (grid operator) grants unconditional grid connection commitment

August 2012 Start of construction with the installation of test piles

July 2014 The 80th tripod is installed

29 August 2014 The 80th rotor star is installed

September 2014 Trial phase of BorWin beta, GTI is connected to the grid

End of October to End

of Jan. 2015 Trial phase of grid connection BorWin 2 (wind turbines are being progressively put into operation)

27th July 2015 The 80th WEC supplies electricity

2nd September 2015 Official opening of the wind farm Global Tech I


Asset Management Goal: Optimization of Costs and Turbine Availability

The maintenance concept is focused on answering the

following questions:

Which components have to be supervised

intensively to ensure a safe and reliable operation?

When and where do preventive measures have to

be taken to avoid failure and to secure a high base

load capacity?

How can offshore energy be produced as

economically as possible?

6 Fleet Monitoring & Data Analysis- Automatic Anomaly Detection

Asset Management Buzzwords

7

Predictive maintenance

Predictive analytics

Big data

“techniques are designed to help determine the condition of in-service equipment in order to predict when

maintenance should be performed. This approach promises cost savings over routine or time-based

preventive maintenance, because tasks are performed only when warranted.”

“encompasses a variety of statistical techniques from predictive modeling, machine learning, and data

mining that analyze current and historical facts to make predictions about future or otherwise unknown

events.”

“is a term for data sets that are so large or complex that traditional data processing applications are

inadequate.

The term often refers simply to the use of predictive analytics or certain other advanced methods to extract

value from data, and seldom to a particular size of data set.”

Source: Wikipedia

Fleet Monitoring & Data Analysis- Automatic Anomaly Detection

Looked at commercial solutions


First contact Steffen Dienst & University of Leipzig

9

Project from a photovoltaic park

• very intuitive visualization

• showing more than 6 years of

operation


Goal in anomaly detection in SCADA data

10

Automatically identify individual WEC with atypical

measurements with high accuracy using existing

operational SCADA data from wind turbines


BAX SCADA System


Data Situation Global Tech I

80 Turbines AD-116

313 Sensors in data model 10min average

Statuslog, Parameter changes, counters

Trace around errors (sampling rate 10ms, 200ms, 1000ms)

Different types of configuration (different settings per WEC)

SCADA alerts only for selected sensors using manually configured thresholds


WEC Data Situation

13

Windpark

Management

System

FTP Server

Realtime

Ethernet OPC UA

FTP FTP

SCADA System

ETL


Until April 2016:

• 100 GB

• 32 billion values

• Per Day: app. 200 MB

Anomaly Detection - Definition

Latin „anomalia“: unequal, uneven, irregular

Meaning in our project: Unexpected qualitative change in the behaviour of a WEC

over time

Problem: What is unexpected?

Assumption: Potentially interesting is any difference to a past refererence date

range:

„used to work like that“ or

„what used to be similar in the past should still be so“


Anomaly Detection - Requirements

No/as little metadata as possible

No manual definition of „normal“ sensor values

Fast learning (nearly interactive experiments)

Efficient model application (single server, no special hardware requirements)

Low false alert rate

Complements SCADA alerts (not a replacement)


Anomaly Detection – Main Idea

Use redundant and/or similar measurements as potential references for comparisons

between sensor values

Temperature „rotor bearing 1“ ≈ „ rotor bearing 2“ ≈ „rotor bearing 3“

Current coolant pump ≈ coolant pressure

Produced power ≈ wind speed³

Temperature changes proportional, sometimes with a time lag


Anomaly Detection - Method

LASSO-Regression (Least Absolute Shrinkage and Selection Operator):

Multiple linear regression with integrated feature selection

SensorX ≈ weighted sum of other features

Feature: sensor, sensor – x minutes, added sensors, transformed sensors (square, square root, logarithm…)

Quelle: Icons made by Freepik from www.flaticon.com Fleet Monitoring & Data Analysis- Automatic Anomaly Detection 17

http://www.freepik.com/

http://www.flaticon.com/

Anomaly Detection – Regression Model

Example: model of „Motor current coolant pump 1+2“

Name Time lag Proportion

Coolant Pressure 1 0 min 0,8856

Inverter Case Temperature -20 min 0,0477

Temperature Drawing-Off Air -20 min 0,0176

Axis 3 Contouring Error -20 min 0,0133

Temperature Drawing-Off Air 0 min 0,0127

Temperature Drawing-Off Air -10 min 0,0083

Axis 3 Battery Discharge Current -20 min 0,0067

Axis 1 Battery Discharge Current -10 min 0,0047

Axis 1 Battery Discharge Current 0 min 0,0035


Anomaly Detection – Regression Model Application

Model prediction

Measured sensor values

Residual: Difference of

prediction and

measurements

Example: model of „Motor current coolant pump 1+2“


Anomaly Detection – All Models, One WEC

Example: Drop in coolant

pressure,January 2016


Anomaly Detection – Schematic Process

Process to continuously

• increase the quality of

the models

• and the precision of the

detected anomalies

Learning time for one WEC

and four months of 10min

data: 40s

Create LASSO models

Compare predictions and measurements

Heuristically determine most probable root causes for model

divergences

Interactive data analysis by the

operator

Define thresholds, exclude sensors, augment features

Source: Icons made by Freepik from www.flaticon.com

Fully automatic

Optional


http://www.freepik.com/

http://www.flaticon.com/

Anomaly Detection – Representative Findings

Automatic detection of

Gradual increase in temperatures





Pressure changes





Pressure changes

Sensor defects





Pressure changes

Sensor defects

Failure of redundant component

operation





Pressure changes

Sensor defects


operation

Oil/water leaks





Pressure changes

Sensor defects


operation

Oil/water leaks

Misalignments of nacelle /

wind direction

Etc.


Anomaly Detection - Lessons learned

Long enough „error free“ reference date

ranges are hard to find

Ramp up phase, frequent parameter changes

Optimal length of reference date range unclear

Not every model makes sense

Need to exclude bogus sensors: counters, parameter

settings

Counters may be misleading: intermitting resets

Redundant components need to be added up

Alternating motors and pumps

23

The more models, the better the results

If an error changes several temperature values

in a similar way, models may not see it

Needs good integration with interactive

data visualizations

Often, manually comparing different sensors,

date ranges, plants very helpful

Latency disrupts investigative mindset

Not all anomalies are „interesting“:

Heating increases currents/power usage (not

observed in summer)

Needs classification of changes (sensor

defects, trend changes, …)


Anomaly Detection – Integrated Prototype


Thank you for your attention!

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global tech i offshore wind gmbh am sandtorkai 62, dock 4 ...sdienst/publications... · 80 turbines...

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