GE Energy Transformer Monitoring: How Moving forward from Monitoring to Diagnostics can Positively Impact Indian Business and Industry

Transcription

1 GE Energy Transformer Monitoring: How Moving forward from Monitoring to Diagnostics can Positively Impact Indian Business and Industry Brian Sparling, SMIEEE GridTech 2007, Delhi February 5-6, 2007

2 The Transformer A complex system Bushings Tap changer Cooling System Coils Oil Tank Control Cabinet Core 2/

3 Monitoring vs. Diagnostics Monitoring: function is to avoid unexpected transformer failure and insure continuous normal operation Diagnostic : Application of On-Line and Off-Line devices & techniques to confirm and determine the exact nature of the anomaly 3/

4 Monitoring vs. Diagnostics Monitoring Diagnostic Maintenance & Repair Transformer Is it Normal? No 10% Is it Serious? Yes 2% Yes 90% No 8% Do nothing else Do something else Broadband technique applied routinely Focused technique applied as required Maintenance & Repair Shop Cigre Report No. 227, Life Management Techniques for Power Transformers. WG A2.18 4/

5 Monitoring vs. Diagnostics Over the last 10 to 15 years; Function Specific sensors, gas-in-oil, temperature monitoring, have evolved from dumb sensors to microprocessor based devices, commonly called Intelligent Electronic Devices (IED s) Expanded the capability of these systems to perform more data processing at the point of measurement Along with this, IED s have the capability for data communication 5/

6 Monitoring vs. Diagnostics Measure multiple parameters Support multiple calculations and models Provide a more complete view of the condition of a specific type of equipment Examples of equipment level monitors: Load Tap Changer monitors (thermal, operational, acoustic signature) Transformer monitors (gas content, multiple models, partial discharge, hot spot) Bushing monitors 6/

7 Broadband monitoring technique for Main tank Gas-in-oil Oil is in contact with every component in the main tank. If a fault occurs in a component, oil will be degraded and gases will be generated A sudden increase of dissolved gas level is the best indicator of a developing incipient fault 7/

8 Failure Avoidance When the insulation system is stressed, KEY fault gases are produced and they will dissolve in the oil Hydrogen from oil Carbon Monoxide from paper 8/

9 The HYDRAN Technology Detection and Monitoring of Key Fault Gases in oil Responds Mainly to H 2 and CO Detects a deviation From the Base line Continuously monitors the Evolution of the gases in the Transformers, any sudden increase is an indication of an incipient fault 9/

10 HYDRAN Fault Detection Detection of a core Hot Spot Low Level evolution HYDRAN 201i Reading PPM Transformer fault Dec 15-Dec 29-Dec 12-Jan 26-Jan 9-Feb 23-Feb Date 10 /

11 Broadband monitoring technique for Main tank Moisture-in-oil Moisture degrades paper Moisture reduces dielectric strength Moisture ages transformer faster Moisture is everywhere Moisture is a key element to monitor, especially in the solid insulation (paper) DGA testing or monitoring cannot help with moisture evaluation 11 /

12 Impact of moisture in Paper The amount of water in paper is a very important parameter to know, as it directly determines the following: Aging rate of the winding insulation Bubbling temperature (limits the amount of overloading of a transformer) Dielectric resistance of the barriers at the bottom of the winding 12 /

13 Impact of moisture in Paper Area of interest for winding insulation Winding insulation Thin barriers Oil Winding Guided convection flow through disk windings Temperature ( o C) Moisture content (%) Area of interest for barrier insulation 13 /

14 Moisture and Bubbling Model Water condensation temperature Winding bubbling temperature Bubbling temperature margin, alarm point Absolute water content in oil 14 /

15 Winding Hot Spot Cooling Efficiency Aging Cooling Status Gas Level Water Level Moisture in Paper Load 15 /

16 Moisture and Bubbling Model Is this transformer behaving normally? 16 /

17 17 /

18 Predictive Modeling: Anomaly Detection Premise: By modeling the steady-state behavior of the transformer using multivariate analysis, we can deduce an impending fault in that same transformer as its behavior changes over time. This analysis will generate an alarm before a univariable control limit would be exceeded Application: Turn forced outages into planned outages Typical Detectable Faults: Incipient Fault Detection of Insulation System Failure Loss of Cooling Efficiency Degradation of Tap Changer moving parts (gear slop) Any class of fault that occurs over time (non-instantaneous) 18 /

19 Predictive Modeling: Compare static model against online data Compare current behavior (relationships between sensors) to previous behavior. Determine if the current behavior is normal based on data that has been previously sampled. If the behavior appears abnormal, what are the contributing sensors? 19 /

20 Predictive Modeling: PCA Observation #337 approaches 99 th percentile Confidence Interval. Why? 20 /

21 Predictive Modeling: Model Error Contributions Tap Changer Position & Temperature profile Differs from what model is accustomed to seeing 21 /

22 Diagnostic Modeling: Bayesian Belief Networks Premise: The failure modes of transformer components are well known. The relative failure probabilities are also known. It should be possible to build a model that will link failure modes and effects to the underlying damaged component. By adding test results (findings) to the model, the model diagnostics can be refined until the faulted component can be inferred with a high probability of accuracy. > Approach: FMEA process employed to enumerate transformer failure types (faults) and their relative frequencies Assist Maintenance personnel to quickly isolate transformer faults 22 /

23 Diagnostic Modeling: Bayesian Belief Networks 23 /

24 Summary Benefits of a combined early fault detection and diagnostic system Better knowledge of the operating condition of the fleet Early warning of incipient faults on any of the 5 components of the transformer Better management of capital and allocating resources to improve the network development Improved predictability of network performance Early detection & correct diagnosis of a problem, saves money 24 /

25 Summary Every transformer has its own normal behavior, in operation, and very different behavior when failing. Like in medicine, early diagnosis often avoids severe pain or worse 25 /