A comparative assessment of PD and EMI methods from the EPRI generator field condition monitoring program

J. Keith Nelson

Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA

Abstract — Since 1997, the US Electric Power Research Institute (EPRI) has been conducting field studies on Partial Discharge (PD) and Electromagnetic Interference (EMI) methods applied to selected representative utility generators which include both air- and H2-cooled units. A number of testing companies have been using different technologies to assess the condition of the generators over a period of several years (and the results eventually compared with internal inspections of the units).

Selected results from this study will be presented to illustrate the relative attributes of the various methods of data acquisition and interpretation. The acquisition includes considerations of the coupler characteristics, the frequency bands, and the noise rejection methods, while interpretation involves the selection of descriptive indices and the use of computer-based intelligence in the detailed interpretation of the signatures obtained. Included in this analysis will be a discussion of the problem areas identified and a comparison of time- and frequency-domain analysis

Index Terms — Generator Diagnostics, Partial Discharge Analysis, Electromagnetic Interference, Condition Monitoring

I. background and Introduction

With the overall objective of providing an assessment of various different technologies for assessing the condition of large utility generators, the US Electric Power Research Institute (EPRI) has been undertaking field measurements on selected generators since 1997. These measurements have been limited to the interpretation of discharge signatures from Electromagnetic Interference (EMI) in the frequency domain or Partial Discharge (PD) in the time domain. The units selected for the program have been chosen to be representative of a range in utility service including air, H2 and water cooled units from a number of different manufacturers. The units discussed in this paper are summarized in Table 1. A total of 8 different testing companies have been involved in this study. It must be emphasized, however, that it is not the purpose of this work to rank or assess companies, but to highlight the attributes (and shortcomings) of various different techniques, or variations of techniques, employed. To that end the companies are not associated with individual results by name.

In many instances, the techniques employed imply a trade-off between competing requirements and so there are not always clear cut answers since pseudo philosophical issues are sometimes involved. In particular, this program seeks to look not only at the measurement techniques, but also the means available for the interpretation of the signatures obtained. This aspect is becoming increasingly important as the cadre of experts diminishes, and there is a need to automate the diagnosis of machine condition to permit

inexperienced personnel to undertake the procedures reliably.

This review provides only a very few examples to illustrate the primary features of the study. The full results may be found elsewhere [1,2]. The predictions of condition made in this program are ultimately cross-checked against visual inspections made on the machines when they are eventually taken out for service and the rotors removed.

II. THE ISSUE OF NOISE

While it is universally recognized that it is imperative to discriminate against external noise in obtaining discharge signatures from machines, it is nevertheless true that the EPRI program has detected more significant problems outside the generators than from the internal insulation structure. For PD measurements, there are three basic ways to separate signal from noise in machines:

► Select a frequency range remote from the major sources

► Use a time-of-flight method [3] to discriminate the source

► Employ software techniques to separate unwanted signals [4]

The use of high frequencies will undoubtedly help, but has the attendant disadvantage that the PD signals from deep in the winding may be attenuated at high frequencies so as not to be captured by a line-end coupler. Time-of-flight is unquestionably effective, but again requires a high bandwidth system as well as the expense of a second set of couplers. However, in order to be able to detect problems outside the machine (such

TABLE IMachines monitored in the epri pd/emi program

Unit # Sammis 6 Marshall 3-4 Lake Rd 1-3

Manufacturer Westinghouse GE ABB

Type # 1S-72P.285 180X357 60WY23Z

Voltage (kV) 20 24 21

Rating (MVA) 800 790 340

RPM 3600 3600 3600

Power Factor 0.85 0.9 0.85

Cooling Hydrogen Water/H2 Air

Approx. Year 1965 1970 2001

Insulation Thermalastic Micapal® I MicaDur®

Notes Rewedged 1998

Rewedged 2001

Combined Cycle

as loose hardware in the iso-phase bus duct), it is important to analyze both the internal signal and the “noise” signature. The software approach attempts to identify noise from its temporal and frequency characteristics. It is not clear that it can effectively discriminate between internal and external discharges, but the technique is still in its infancy and advocates argue that it may be possible to both obtain adequate discrimination and the added sensitivity that comes from the use of lower frequencies.

The issue of the selection of measurement bandwidth may be seen from Fig. 1. which depicts the low frequency attenuation experienced with systems equipped with three different couplers. It would seem asthough a high capacitance coupler used with a detection system operating below 50 MHz would result in the capture of a major proportion of the PD energy. Indeed, the Marshall plant does employ couplers as large as 9 nF which provides a bandwidth extending down to 20 kHz. However, the issue is not as straightforward as depicted in Fig. 1 since the region below 50 MHz is also the band in which essentially all the unwanted interference exists. Use of this part of the spectrum is thus characterized by an abundant signal, but a severe problem of identifying the relevant machine signatures buried in noise.

III. General Observations from the Program

While the issue of external noise discrimination is pivotal, other issues have also been commonly observed in the EPRI program. On several occasions, a number of the testing companies erroneously identified the phase generating the signals. This usually occurred as the result of the use of an unverified source for synchronization. If such references are derived from PTs connected line-to-line or the path involves a Δ-Y transformer, phase shifts will occur which make both identification and interpretation problematical. The safest approach is to use a reference derived (through a low-pass filter) from the coupler in use.

Not only can the legitimacy of a calibration of extensive inductive equipment be questioned, but the lack of “absolute” readings forces testers to rely on other measures. Trending (comparing readings with historical values on the same machine) is the most universally used, but one may also make comparisons with another phase on the same machine, a sister machine, or an assembled database of machines of similar cooling type and rating. The use of a database is very appealing, but it does tend to stifle innovation since major changes in technique may make the database inapplicable. The other class of techniques being widely used by testers is the use of a parametric approach. If PD or EMI measurements are taken with varying load, power factor, or gas coolant pressure, it is sometimes possible to infer problems by examining the changes in discharge signatures. On-line operating constraints will often make it difficult to change loads, but in a multi-machine plant the simultaneous import and export of MVAr can permit the change of power factor. This will move the bar forces due to the leakage magnetic flux in relation to the voltages giving rise to discharges [5] and expose the presence of looseness in the insulation structure.

Some testers also undertake supplementary surveillance to augment a PD or EMI analysis. Examples of this are acoustic or radio emission scans of areas surrounding a machine as depicted in Fig. 2. This can be particularly useful in identifying, for example, flexlink arcing or cracked bus insulators which corrupt the machine signature.

Fig. 2. Examples of handheld discharge location. Left: acoustic, Right: EMI “sniffer”.

Fig. 1. Typical frequency transmission characteristics in comparison with PD signal [Courtesy: Adwel International]

IV. Emi AnalysisThe primary proponent of EMI analysis in the US has

usually derived signals on-line from a high-frequency CT placed at the generator neutral. This has the considerable advantage that it can be installed without shutdown, but gives no phase identification and there is inherently a long propagation path for discharges emanating from the top third of the winding. Notwithstanding that, this frequency domain technique has been used to pinpoint a multitude of problems with machine accessories (grounding brushes, exciter open diodes, busduct arcing, etc). There is no inherent limitation to this form of coupling, and some advantages for the technique may be gained through the use of line couplers as has become common for PD analysis. Once again, the primary impediment to this lies with an inability to use a comparison with previous measurements in the diagnosis.

The unit at the Sammis station (see Table 1) has been in the program for 7 years, and Fig.3. shows an EMI spectrum from this unit trended over several years. Two salient things are evident from these plots. Firstly,

there are no very damaging levels of discharge activity evident, and, secondly, the machine appears to be stable over time. A recent inspection of this unit (Fig.4) has verified that there are no major signs of PD nor structural deterioration. This conclusion was made independently also by several PD vendors operating at both high and low frequencies.

In contrast, Fig. 5. depicts a comparison between EMI spectra taken in Marshall Unit #3 (see Table 1) over a 1 year time span. In 2003, the signature was dominated by high level activity centered at 10 MHz which was found on inspection to be the result of a cracked insulator and connector arcing in the iso-phase bus. This was repaired, but a year later an intermittent turn-to-turn short on a bushing CT yielded the saturated signal shown in Fig.5. Of greater concern is the increase seen in the signature below 1 MHz. The

frequency band would suggest this was due to slot related activity and represents significant deterioration.

V. PD Analysis

PD methods applied to machines may be broadly categorized into those operating in the frequency band from about 50 to 250 MHz and those advocating the use of frequencies primarily below 50 MHz. The use of time-of-flight discrimination is usually associated with the high frequency range, but PD systems working at lower frequencies also sometimes use filters to prescreen the signals obtained. Phase-resolved PD analysis is universally used to aid in the interpretation

A. “High” Frequency Methods

The signature in Fig. 6 was taken from Phase A of Unit #3 at Lake Road Generating (see Table 1) at 75% load using frequencies in the range 50 -250 MHz and

Fig. 8. Phase-resolved filtered PD measurements at Lake Road #3. Left: 40 – 800 kHz, Right: 2 – 20 MHz

Marshall Unit 3 GeneratorComparison 02-03

Frequency (MHz)0.01 0.1 1 10 100

Qua

si-P

eak

(uV

)

0.1

1

10

100

1000

October 2003Mean = 54 uVStd. Dev. = 137 uVSeptember 2002Mean = 194 uVStd. Dev. = 510 uV

Corona

Bushing CTSparking

Iso Bus"Z" PhaseDefects

Fig. 5. EMI spectra taken for Marshall #3 over a one year time span

Fig. 4. Inspection of Sammis #6 – rotor removed ( see text)

-20

-10

0

10

20

30

40

50

60

70

0.01

0.05

0.09

0.14

0.18

0.22

0.27

0.36

0.45

0.53

0.69

0.85

1.02

1.22

1.58

1.93 2.

3

2.95

3.87

4.68

5.45

7.01

8.94

10.8

12.6

16.9

21.5

26.5

31.5

39.3

46.5

52.9

67.7

82.9

98.4

Frequency(Mhz)

uV

2002 2001 Quasi-Peak 2003

2004 data

Fig. 3. EMI spectra derived from Sammis #6 over the years 2001 - 04

employing time-of-flight discrimination so that signals from the machine (upper plot) can be separated from the external noise (lower plot). The discharges seen from the machine are not only large (> 98% of air-cooled machines in this voltage class), but are also anomalous in that they are centered close to the voltage peaks (superimposed in Fig.6) instead of being at the classical positions of ~45° for ~225° for ground wall discharges. It is thus suggested that these may result from surface discharges in the stator end-winding area. However, it does illustrate the subtle issues involved with the interpretation of such signatures, particularly as some of the Phase A activity has cross-coupled to Phases B and C (not shown here).

B. “Low” Frequency Techniques

It is instructive to examine the phase-resolved PD signatures taken on the Marshall #3 unit which correspond to the 2002 curve for which a frequency domain result is given in Fig. 5. The results for Phase C provided by two different companies at the same load and using essentially the same technique employing a filter to narrow the captured signal to the 40 – 800 kHz range are shown in Fig. 7. Like all PD measurements, the amplitudes are essentially uncalibrated and thus amplitude comparison cannot be made. Nevertheless, if account is taken of the bipolar nature of the lower plot, the similarities in the patterns can been seen. However, restriction of the frequency components to less than 1 MHz has masked the external duct problem seen in the EMI result of Fig.5.

Fig.8 illustrates a “low” frequency PD test conducted on Phase A of unit #3 at Lake Road, within 4 months of the date on which Fig. 6 was taken. The left plot is restricted to components less that 1 MHz, and the right plot embraces 2 – 20 MHz. At very low frequencies the periodic spikes are the result of excessive exciter noise – a condition which was missed using the high frequency technique. However, the activity shown in the right plot was judged to be low level groundwall PD which contrasts strongly with the conclusions drawn from Fig. 6. This is yet another case in which there is often no agreement among specialists, and highlights the challenge behind the automation of interpretation.

VI. Interpretation of Signatures

A. EMI Technique

The interpretation of EMI spectra is complicated by the dominant pattern of signal reinforcement and annihilation due to the resonance and anti-resonance caused by the standing waves created in the stator. There is also corruption of the spectra by radio frequency emission from broadcast transmitters, power line carrier equipment, etc. Despite that, there are some general guidelines for identifying the signatures derived from machines from the frequency band in which they appear [6]. Nevertheless, diagnosis on the basis of an EMI spectrum requires significant experience, and there have really been no attempts to automate it. In principle, one could compensate the standing wave patterns, but this would require a detailed knowledge of the internal construction and dimensions. It is also often unrecognized that much supplementary information is used in interpreting EMI spectra – e.g. local emissions, time domain signal characteristics, etc.

Fig. 7. Marshall #3 phase-resolved PD data from two different vendors using the frequency range 40 – 800 kHz

Fig. 6. Phase-resolved PD analysis for Phase A, Unit #3 at Lake Road Generating. HF technique.

B. PD Technique

The more widespread use of PD has generally relied on a few time-honored methods [2,3] for diagnosis. Clearly the relative magnitude of the discharge activity is a primary yardstick together with an integrated measure of activity such as the normalized quantity number (NQN) analysis undertaken by some testers contributing to this study. NQN can be represented mathematically [8] as:

(1)

where Pi is number of pulses per second in magnitude window, i, N is number of magnitude windows, G is gain of the partial discharge detector (arithmetic, not decibels), FS is maximum magnitude window (in mV) at unity gain. However, polarity predominance, position (phase shifts) and pattern on a phase-resolved plot [9], parametric variations, asymmetry, etc. may all be used to good effect by an experienced professional.

Without doubt, there is a substantial interest in applying computer-based intelligence to the diagnostic process. To this end the discharge pulse train can be viewed as a stochastic time and/or amplitude series and descriptive indices derived which characterize the discharge patterns. Examples of this include distribution moments, time series indices, correlation functions, fractal dimensions, dynamic stagnation voltage (pulse phase interval). Many of these have shown some promise in laboratory studies, but have not been fully tested in the challenging and noisy machine environment.

As indicated earlier some of this impetus has extended to the problems of noise rejection, and means are now available to try to reject noise by a cluster analysis based on the interval and frequency characteristics of the incoming pulses [4]. Pulses having common characteristics can be separated by this means as depicted in Fig 9. The selected filtered signal may then be applied to a decision tree in which its attributes can be examined in order to try to interpret the features in terms of machine condition. In this way some of the traditional methods can be augmented by more sophisticated algorithms to apply artificial intelligence to the monitoring problem. Fig.10 shows an example of such an inference engine.

VII. AppraisalThe evaluation made during the conduct of the EPRI

PD/EMI program has made it clear that no one technique available commercially today is able to offer a complete and comprehensive machine condition monitor. However, the combination of a plurality of techniques can, indeed, provide substantial and extremely valuable information to a machine owner. The approaches in use today do not always come to the

same conclusion and interpretations of the derived data by experts vary widely – particularly with regard to the Phase involved. The application of artificial intelligence outlined in the previous section represents the “next generation” of PD detector applied to the machine environment. While it still needs substantial refinement in the light of field application, it does, in principle, remove some of the subjective judgment involved in today’s approach. This program has indicated that there are many instances in which the PD and EMI patterns are anomalous and do not conform to a “classical” pattern. As a result, it may be some time before inference engines can be developed to provide reliable diagnosis in most circumstances.

Fig. 3 is a reminder that many situations are dominated by external problems generating discharge activity. It is the author’s view that time-of-flight methods (with the concomitant implications for the bandwidth of the measurement) remain today the best method for discrimination between problems arising within the machine envelope and those outside. However, methods such as those depicted in Fig. 9 have been shown to be workable, and it is not clear that, in the future, software discrimination may not prove as effective. That would have important consequences in lowering the cost of monitoring.

Although there are some general guides [8] published, the industry lacks standards for PD detection in generators and motors. In particular, the type of couplers installed has important implications for the methods that can be applied. For example Fig. 11 shows the time response of a PD when captured with an

Clusters of pulse types

Fig. 9. Example of the clustering of pulse types to identify interference

Fig. 10. Inference Engine for interpretation of generator discharges [7]. Blocks 1 – 6 evaluate the membership of a data set to a defect type; blocks 7 exist to flag cases which cannot be identified.

1

2

1010

10

2log

log2

log N

i

Ni

i PP

PGNFSNQN

80 pF coupler in comparison with the situation when a 1000 pF (and integral radio-frequency current transformer) was used.

The effect of the CT is not only to change the signal amplitude, but will also have an impact on the frequency response of the measurement system. Fig. 11 clearly shows that the 1000pF/RFCT combination is responding to the lower frequency component not seen by the 80 pF unit. On the other hand much of the signal structure is lost due to the limited h.f. bandwidth. Clearly, these issues have a profound effect when computer-based methods are used in the diagnostics.

Acknowledgements

This program was conducted under the auspices of EPRI and resulted from the guidance and council of Mr. Jan Stein. It could not have been undertaken without the help of the engineering staff at Duke Energy, First Energy and Lake Road Generating, and the expertise of the testing companies: Alstom Power, Adwel International, AEP, Eaton Corp, General Electric, Iris Power Engineering, and MDA. Machine inspections were conducted by Mr. Clyde Maughan of Maughan Engineering Consultants.

References

[1] Maughan C.V. “Partial discharge on-line testing of turbine-driven generator stator windings”, EPRI Report 1001209, December 2000

[2] Nelson J.K., “Partial discharge on-line testing of turbine-driven generator stator windings”, EPRI Reports: #1007742, 2003, #1004958, 2004 and #1010207, 2005

[3] Stone G.C., Boulter E.A., Culbert I., and Dhirani H. “Electrical insulation for rotating machines”, IEEE Press, 2004

[4] Cavallini A., Contin A., Montanari G.C. and Puletti F., “Advanced PD inference in on-field measurements. Part I: Noise rejection”, and “Part II: Indentification of defects in solid insulation systems”, IEEE Trans DEI, Vol. 10, 2003, pp 528-38, and pp 216-24

[5] Nelson J.K. and Azizi S. “Measures and technologies to enhance the insulation condition monitoring of large electrical machines”, Trans. IEEE, Vol. DEI-11, #1, 2004, pp 102-12

[6] Timperley J.E. and Chambers E.K., “Locating defects in large rotating machines and associated electrical

systems through EMI diagnostics”, Paper 11-311, CIGRE 1992 Session, Paris, Aug. 30th 1992

[7] Cavallini A., Conti M., Contin A., Montanari G.C., and Puletti F., “A new algorithm for the identification of defects generating partial discharges in rotating machines”, Conf. Record of the 2004 IEEE EI Symposium, Indianapolis, IEEE, 2004, pp 204-07

[8] IEEE Power Engineering Society, Electric Machinery Committee, IEEE Std. 1434-2000, “IEEE Trial-use Guide to the Measurement of Partial Discharges in Rotating Machinery”, IEEE, 2000

[9] Hudon C., and Bélec M., “Partial discharge signal interpretation fo generator diagnostics”, Trans. IEEE, Vol. DEI-12, #1, 2005, pp 297-319

1 ) 8 0 _ A : 2 0 m V o lt 5 0 0 ns 2 ) A B B _ A : 5 m V o lt 5 0 0 n s Fig. 11. Time response of the signals obtained with (a)

80 pF and (b) 1000 pF + RFCT couplers. y-scale: 20mV/div (upper) 5 mV/div (lower); x-scale: 500 ns/div. [1]

(b)

(a)