1. Advances in Condition Monitoring – Linking the Input to the Output Martin Jones Insensys

2. Turbine Monitoring Today zxcz Many advanced turbines continuously acquire and transmit measurement data to a remote location Wide range of parameters often measured covering –Inputs Wind conditions, yaw angle, blade pitch angle, etc –Outputs Power, bearing wear, etc Measurements are used for performance monitoring, condition monitoring and fault protection –Providing analysis of turbine performance –Indicating when intervention is required

3. SKF Condition Monitoring System szxcz Data from a wide range of sensors gathered in iMU Data reduction in iMU prior to onward transmission over ethernet Alarms flagged via SMS and email Data sent to remote client in control room for display and interpretation iMU ProCon Server Ethernet Control Room Client Alarm messages Ethernet Email SMS

4. Drive Train Monitoring Today zxcz Monitoring of drive train outputs –Unbalance –Alignment errors –Bearing problems –Damage on gear wheels –Shaft bending –Mechanical looseness –Tower vibrations –Electrical problems –Resonance problems

5. Drive Train Monitoring Method szxcz Drive train vibration monitored by accelerometers Characteristic frequencies identify source of vibration But these are measurements of the output, effect or result of degradation The major input or cause of these parameters is the loads from the blades

6. Insensys Blade Load Instrumentation zxcz Benefits –Realtime blade load measurement as input to cyclic pitch control –Long term measurement for structural health monitoring System configuration –4 optical fibre strain sensors located in the blade root to measure flapwise and edgewise bending moment –Optical signals converted to digital electronic data in OEM-1030 instrument located in the hub Measures the key input loads to the drive train

7. Insensys Data Reduction Algorythms zxcz For condition monitoring, large volumes of blade load data need to be reduced for analysis, display and interpretation –1 minute blocks of data processed to a single summary file –Powerful on board DSP performs analysis –Statistical analysis of all raw measurement performed Max, min, average 1F and 3F amplitude and phase

8. Insensys Blade Load Data Interpretation zxcz However blade load measurements can infer other parameters about the blades and the rotor –Key blade parameters are calculated including Blade bending moment Blade cumulative fatigue at 4 locations around each blade root –Key rotor parameters are calculated including Drive torque Resolved horizontal & vertical shaft load Load on tower All derived parameters are summarised –For local ‘black box’ storage –Or transmission to a condition monitoring system

9. Insensys / SKF System Integration zxcz Insensys instrument located in hub –Performs data interpretation and first stage data reduction –Is polled for data like any other sensor connected to the iMU –Can be retrofitted to turbines already containing iMU SKF iMU located in the nacelle –Further data processing and onward transmission iMU

10. Blade Service History zxcz Flapwise and edgewise bending moment histories are processed to blade load service histograms 0 10 20 30 40 50 60 70 80 90 100 110 120 Hours at Given Load ‘000 0 10 20 30 40 50 60 70 80 90 100 110 120 200 180 160 140 120 100 80 60 40 20 200 180 160 140 120 100 80 60 40 20 0 10 20 30 40 50 60 70 80 90 100 110 120 200 180 160 140 120 100 80 60 40 20 Blade 1 – EdgeWise Measured Blade Bending Moment kNm Blade 2 – EdgeWiseBlade 3 – EdgeWise Hours at Given Load ‘000 0 10 20 30 40 50 60 70 80 90 100 110 120 200 180 160 140 120 100 80 60 40 20 200 180 160 140 120 100 80 60 40 20 0 10 20 30 40 50 60 70 80 90 100 110 120 200 180 160 140 120 100 80 60 40 20 Blade 1 –FlapWise Measured Blade Bending Moment kNm Blade 2 – FlapWiseBlade 3 – FlapWise 0 10 20 30 40 50 60 70 80 90 100 110 120

11. Blade Fatigue zxcz Rainflow counting of stress time histories Fatigue characteristics of blade stored in Insensys instrumentation Fatigue calculated at 4 locations around the root of each blade 4 cumulative fatigue values continually updated per blade to minimise data transfer and storage requirements Blade 1 Blade 3 Blade 2 0 10 20 30 40 50 60 70 80 90 100

12. Rotor Drive Torque zxcz Calculated by resolving individual blade loads Time domain summary statistics generated Frequency components analysed –1F amplitude and phase provide measure of rotor balance A fully balanced rotor has zero 1F amplitude Phase of 1F component identifies unbalanced blade –3F amplitude and phase provide measure of drive variability in different parts of blade sweep Wind shear, tower shaddowing Components at 1F and 3F

13. Resultant Rotor Offset Load Main component at 3F Calculated by combining individual blade loads Time domain summary statistics generated Frequency components analysed –3F amplitude and phase provide measure of resultant offset load on drive shaft in differrent parts of blade sweep Wind shear, tower shaddowing

14. Linking Input And Ouput Simple to analyse cause and effect by logging all parameters via a single system Can correlate blade and rotor loads with changes in other turbine parameters

15. Acting on Information Condition monitoring –Event driven alarms generated in iMU Based on threshold values stored in iMU –Alarm flagged via email or SMS –Alarm event information transmitted to SKF client in control room –Provides the opportunity for planned intervention and maintenance Load reduction –Improve understanding of blade load variations on drive train degradation –Implement improved control algorythms to reduce drive train wear

16. Summary Blade load data is interpreted in the load monitoring system to generate key parameters for both the blades and the rotor Large volumes of data are compressed using time and frequency domain algorythms and statistical analysis Blade load monitoring system integrates with either new or existing SKF turbine condition monitoring system –Linking the drive train input loads to the drive train health monitoring output parameters via single system –Enabling retrofit installation Further data reduction and alarm generation by iMU in nacelle Enables cause and effect analysis of turbine degradation and performance optimisation through scheduled intervention and load reduction

17. Acknowledgements: Harry Timmerman, SKF Fredrik Sundquist, SKF