global tech i offshore wind gmbh am sandtorkai 62, dock 4 ...sdienst/publications... · 80 turbines...
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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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An diese Position
Foto einfügen aus
g-Datei,
150 dpi,
RGB-Modus, sw)
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
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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“
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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
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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
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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?
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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
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Looked at commercial solutions
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First contact Steffen Dienst & University of Leipzig
9
Project from a photovoltaic park
• very intuitive visualization
• showing more than 6 years of
operation
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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
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BAX SCADA System
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BAX SCADA System
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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
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WEC Data Situation
13
Windpark
Management
System
FTP Server
Realtime
Ethernet OPC UA
FTP FTP
SCADA System
ETL
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Until April 2016:
• 100 GB
• 32 billion values
• Per Day: app. 200 MB
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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“
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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)
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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
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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
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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
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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“
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Anomaly Detection – All Models, One WEC
Example: Drop in coolant
pressure,January 2016
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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
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Anomaly Detection – Representative Findings
Automatic detection of
Gradual increase in temperatures
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Anomaly Detection – Representative Findings
Automatic detection of
Gradual increase in temperatures
Pressure changes
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Anomaly Detection – Representative Findings
Automatic detection of
Gradual increase in temperatures
Pressure changes
Sensor defects
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Anomaly Detection – Representative Findings
Automatic detection of
Gradual increase in temperatures
Pressure changes
Sensor defects
Failure of redundant component
operation
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Anomaly Detection – Representative Findings
Automatic detection of
Gradual increase in temperatures
Pressure changes
Sensor defects
Failure of redundant component
operation
Oil/water leaks
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Anomaly Detection – Representative Findings
Automatic detection of
Gradual increase in temperatures
Pressure changes
Sensor defects
Failure of redundant component
operation
Oil/water leaks
Misalignments of nacelle /
wind direction
Etc.
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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
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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, …)
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Anomaly Detection – Integrated Prototype
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Thank you for your attention!
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