An Analysis To Identity Critical Component Failure In Onshore Wind

TurbinesWind Ac Africa Conference

14 November 2017 ,Cape Town, South Africa

Mpendulo Kona –Globeleq

Globeleq – South Africa

• Globeleq is a company that actively develops energy projects in Africa and the Americas by developing the electric power sectors in these regions.

• Globeleq South Africa Management Services is contracted to manage three independent power producing utility projects in South Africa’s renewable energy sector.

• De Aar Solar Power -49,4 MW

• Droogfontein Solar Power - 49,4 MW

• Jeffreys Bay Wind Farm – 138 MW

Remote Monitoring Centre

Key 0bjectives and organisational functions

• Monitor the portfolio of assets (Wind and Solar PV)

• Improve performance of O&M activities

• Maximise energy production

• Provide technical services support to on site teams

• Data analytics

Content

• Availability and Reliability in Wind Turbines

• Benefits of Reliability Assessments for O&M Optimisation

• Wind Farm Data

• Approach

• IEA wind Task 33 recommended practices

• Data groups and taxonomy / component designation

• Defining operating states and failure

• Statistical and Quantitative assessments – stochastic non parametric model

• Key Findings and Recommendations

Availability In Wind Turbines

1. IEC 61400-25-1 : Time based availability in wind turbines

• Operational availability (Wind turbine operator / users view )

• Technical availability (Manufactures view )

2. IEC 61400-25-2 : Production based availability in wind turbines

3. IEC 61400-25-3 : Time and production based availability of wind turbines

• The fraction of a given operating period in which a wind turbine generating's system is performing its intended services within the design specification. (IEC 61400-25)

Availability

𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 1 −𝑈𝑛𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒

(𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒 + 𝑈𝑛𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑇𝑖𝑚𝑒)

Benefits of Availability and Reliability in Wind Turbines

Maintenance strategies optimised to address critical failures and improve on measures to address

faults and minimise reoccurrence

• O&M strategy upgrades

• Optimising stock-keeping of spare parts

Source: Weibull.com

Turbine is designed to operate 120 000 hours in its 20 year estimated life span

Hypothetical failure rate in its life stages

Infant mortality – decreasing failure rate

Normal life –constant failure

End of life –increase in failure rate

• Reliability is the probability that a component part, equipment, or system will satisfactorily perform its intended function under given circumstances for a specified period of time. (IEC 61400-25)

Reliability

Approach - IEA Wind Recommended Practices

IEA Wind Task 33 – Recommendations to owners

1. Access all relevant data

2. Identify your use case and be aware of the resulting data needs

3. Map all Wind turbine components to one taxonomy/ designation

system

4. Align operating states to IEC 61400-26

5. Train team to understand what data is useful for

6. Support data quality by using computerised systems

7. Share reliability data to achieve a broad statistical basis

Approach- Data Group and TaxonomyData Groups

▪ Equipment data – describes system configuration to identify assemblies and equipment▪ Operating data – time stamp, operational states▪ Failure data – failure description, failure detection, fault properties

Database used▪ 60 - Wind Turbine generators sub-system equipment

data, alarm architecture ,SCADA alarm log , turbine operational states ,Fault information, timestamp

▪ 24- months of SCADA alarm data

Equipment boundaries (ISO 14224)

Defining Operating States and Failure

• The failure of an item is an event leading to a fault of the item, where a fault is a state. (IEC 60050)

• Event – Is a state transition (IEC 61400)

• Status – condition of a component or system (IEC 61400)

Wind Turbine Operating States

The failure of equipment which causes an immediate cession off the ability to perform required function (ISO 14224)

Critical failure

24 Months Wind Turbine Failure Alarm Data 14595 Wind Turbine Failure Alarms

Statistical and Quantitative AssessmentsStochastic models - descriptive statistics

• Mean• Variance and standard deviation• Minimum and maximum values • Median and percentiles • Frequencies • Standard error of the mean.

Source: Quora.com

Reliability analysis techniques

1. Qualitative assessments• Functional failure analysis• Critical item selection• FMECA

2. Statistical and quantitative• Stochastic models• Physical models • Data driven and machine learning• Combined models

Sources: IEA Wind TCP RP17

Statistical and Quantitative Assessments

Wind Turbine #Alarm System Anemometer

Wind Turbine Taxonomy

Brake Controller

Wind Turbine 1

Wind Turbine 2

Wind Turbine 3

Wind Turbine Population Mean

Failure Data

Process Failure Data

Statistical and Quantitative Assessments

• Outliers (turbines) above P95 in each sub-system assessment are identified for further failure interrogation

WT36 Hydraulics

WT19 Converter

WT16 Converter

WT44 Hub

WT37 hydraulics

Case Scenario - Results

Findings

1. Manual Stops

2. Low pressure brake Accumulator

3. Low brake pressure

4. Bad blade pitching on blade C

5. CANBUS: Input output modules in the wind turbine controller

6. UPS battery failures.

Wind Turbine Sub-systemSub-system Failure

Alarm rate / Month

Sub-system Failure Alarm

Duration Hours / Month

WTG36

Alarm System 0,29 0,09

Anemometer 0,04 0,56

Brake 0,33 1,10

Controller 1,67 10,85

Converter 5,13 6,72

Gearbox 0,04 0,19

Gen.Inverter 7,46 5,04

Generator 0,17 0,05

Grid inverter 8,71 5,37

Hub 0,42 6,88

Hydraulics 0,83 15,77

Yaw System 0,08 1,51

WTG36 Total 25,17 54,13

Wind Turbine 36 Critical Sub-system Failure Data

Sub-system Failure Interrogation

Case Scenario - Results

Findings

1. Low pressure brake Accumulator

2.Low brake pressure

3. Bad blade pitching on blade C

4.Yaw motor – Superheated

Wind Turbine 44

Critical Sub-system Failure DataWind Turbine Sub-systemSub-system Failure

Alarm rate / Month

Sub-system Failure Alarm

Duration Hours / Month

WTG44

Alarm System 0,46 0,13

Anemometer 0,04 0,58

Brake 1,17 1,77

Controller 4,67 12,44

Converter 4,71 6,49

Gen.Inverter 8,96 6,62

Generator 0,04 0,60

Grid inverter 8,75 6,13

Hub 2,13 10,33

Yaw System 0,25 2,03

WTG44 Total 31,17 47,10

Wind Turbine Sub-system Failure Alarms Activated Total Duration "Hours"

Alarm System

HW-profile not selected 1,0 0,2

Manual stop 7,0 2,5

Stopped for SW update 2,0 0,5

Alarm System Total 10,0 3,2

Brake

Brake acc. pressure too low 10,0 21,2

Brakepressure too low 9,0 21,2

Brake Total 19,0 42,4

Hub

Bad pitch calibration, blade C 12,0 88,2

Hub: No feedback MTS-sens C 9,0 42,6

Hub: Valve Feedb. A range Err 1,0 0,2

Hub: Valve Feedb. B range Err 1,0 0,2

Hub: Valve Feedb. C range Err 1,0 0,2

Pitch A tracking during opera. 1,0 0,0

Pitch B tracking during opera. 1,0 0,0

Pitch C tracking during opera. 1,0 0,0

Pitch C tracking during stop 17,0 73,4

Pitch pawl A Feedb. Stop 1,0 14,3

Pitch pawl B Feedb. Stop 1,0 14,3

Pitch pawl C Feedb. Stop 1,0 14,3

Hub Total 47,0 247,9

Yaw System

Yaw motor(s) superheated 5,0 48,6

Yaw System Total 5,0 48,6

Sub-system Failure Interrogation

Conclusions1. SCADA wind turbine operational alarm data has the potential to be particularly useful in the identification of critical

component failure in wind turbines for wind power plant owners and operators when used correctly.

2. Case summary

• Controller , Grid inverter and Generator inverters in this wind turbine experienced a high frequency of faults

• Turbine Yaw motor(s) alarms appeared with relatively less frequency but had longer durations when they occurred, this alarm was due to a overheated yaw motor.

• The number of failure alarms triggered in the generator inverter followed abnormal distribution – no outliers identified

3. Plant Data

• Outliers were identified from the statistical sub-system assessment which can be used for a further interrogation of failure.

• The mechanical sub-systems (Yawing system, Hub, Brakes & hydraulics ) experience less frequent failures but of longer durations.

4. Wind turbine SCADA data has proven reliable in approaching reliability and availability studies , all data recorded within the measurement period are a available.

5. The approach allows you to compare wind turbines within a population and identify outliers however adequate data samples need to be available.

6. The data sample or number of units is an important aspect to when applying a quantitative stochastic – non parametric approach.