[James_H._Harlow]_Electric_Power_Transformer_Engin(BookSee.org)
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temperature. As the transformer cools, the moisture returns to the solid insulation at a slower rate. The<br />
time constants for these migrations depend on the design of the transformer and the solid and liquid<br />
components in use. The combination of moisture, heat, and oxygen are the key conditions that indicate<br />
accelerated degradation of the cellulose. Excessive amounts of moisture can accelerate the degradation<br />
process of the cellulose and prematurely age the transformer’s insulation system. The existence of a<br />
particular type of furanic compound in the oil is also an indication of moisture in the cellulose insulation.<br />
Moisture-in-oil sensors were first successfully tested and used in the early 1990s (Oommen, 1991;<br />
1993). The sensors measure the relative saturation of the water in oil, which is a more meaningful measure<br />
than the more familiar units of parts per million (ppm). Continuous measurements allow for detection<br />
of the true moisture content of the transformer insulation system and of the hazardous conditions that<br />
may occur during temperature cycling, thereby helping to prevent transformer failures.<br />
3.13.3.1.3 Partial Discharge<br />
One cause of transformer failures is dielectric breakdown. Failure of the dielectrics inside transformers<br />
is often preceded by partial-discharge activity. A significant increase either in the partial-discharge (PD)<br />
level or in the rate of increase of partial-discharge level can provide an early indication that changes are<br />
evolving inside the transformer. Since partial discharge can lead to complete breakdown, it is desirable<br />
to monitor this parameter on-line. Partial discharges in oil will produce hydrogen dissolved in the oil.<br />
However, the dissolved hydrogen may or may not be detected, depending on the location of the PD<br />
source and the time necessary for the oil to carry or transport the dissolved hydrogen to the location of<br />
the sensor. The PD sources most commonly encountered are moisture in the insulation, cavities in solid<br />
insulation, metallic particles, and gas bubbles generated due to some fault condition.<br />
The interpretation of detected PD activity is not straightforward. No general rules exist that correlate<br />
the remaining life of a transformer to PD activity. As part of the routine factory acceptance tests, most<br />
transformers are tested to have a PD level below a specified value. From a monitoring and diagnostic<br />
view, detection of PD above this level is therefore cause for an alarm, but it is not generally cause for a<br />
tripping action. These realities illustrate one of the many difficulties encountered in PD diagnosis. The<br />
results need to be interpreted with knowledge of the studied equipment. Two methods are used for<br />
measuring partial discharges: electrical and acoustic. Both of these have attracted considerable attention,<br />
but neither is able to yield an unambiguous PD measurement without additional procedures.<br />
3.13.3.1.3.1 <strong>Electric</strong>al Method — The electrical signals from PD are in the form of a unipolar pulse with<br />
a rise time that can be as short as nanoseconds (Morshuis, 1995). Two electrical procedures for partialdischarge<br />
measurement exist. These give results in microvolts or picocoulombs. There is no fundamental<br />
conversion between the procedures applicable to all cases. The signals exhibit a very wide frequency<br />
content. The high frequencies are attenuated when the signal propagates through the equipment and the<br />
network. The detected signal frequency is dependent both on the original signal and the measurement<br />
method.<br />
<strong>Electric</strong>al PD detection methods are generally hampered by electrical interference signals from surrounding<br />
equipment and the network, as illustrated in Figure 3.13.1. Any on-line PD sensing method<br />
has to find a way to minimize the influence of such signals. One way is to use a directional high-frequency<br />
field sensor (Lemke, 1987). The high detection frequency limits the disturbance from PD sources at a<br />
distance, and the directionality simplifies a remote scan of many objects. Therefore, this type of sensor<br />
seems most appropriate for periodic surveillance. It is not known whether this principle has been tried<br />
in a continuous monitoring system.<br />
A popular method to interpret PD signals is to study their occurrence and amplitude as a function of<br />
the power-phase position; this is called phase-resolved PD analysis (PRPDA). This method can give<br />
valuable insight into the type of PD problem present. It is suggested that by identifying typical problem<br />
patterns in a PRPDA, one could minimize external influences (Fruth and Fuhr, 1990). The conceptual<br />
difficulty with this method is that the problem type must be known beforehand, which is not always the<br />
case. Second, the relevant signals may be corrupted by an external disturbance.<br />
FIGURE 3.13.1 <strong>Electric</strong> PD measurements on transformers in underground and open-air substations. The overhead<br />
transmission lines cause a multitude of signals, making a PD measurement very insensitive. Underground stations<br />
are generally fed by cables that attenuate the high-frequency signals from the network, and PD measurements are<br />
quite sensitive. Horizontal scale in seconds, vertical scale in mV.<br />
There have been many attempts to use neural networks or adaptive digital filters (Wenzel et al., 1995a),<br />
but it is not clear if this has led to a standard method. The problem with this approach is that the<br />
measured and the background signals are very similar, and the variation within each of the groups may<br />
be much larger than the difference between them. Adaptive filters and neural networks have been used<br />
to diminish other background sources such as medium-wave radio and rectifier pulses.<br />
These methods employ a single sensor for the PD measurement. If several sensors of different types<br />
or at different locations are employed, the possibilities of reducing external influences are greatly<br />
enhanced. Generally, the multisensor approach can be split into two branches: separate detection of<br />
external signals and energy flow measurements.<br />
When there is a clear source for the disturbing signals, it is tempting to use a separate sensor as a<br />
pickup for those and simply turn off the PD measurements when the external level is too large. Methods<br />
like this have the disadvantage of being insensitive during some portion of the measurement time. In<br />
addition, a very large signal from the equipment under study may be detected by the external pickup as<br />
well, and thus be rejected.<br />
Energy-flow measurements use both an inductive and a capacitive sensor to measure current and<br />
voltage in the PD pulse (Eriksson et al., 1995; Wenzel et al., 1995b). By careful tuning of the signals from<br />
the two sensors, they can be reliably multiplied, and the polarity of the resulting energy pulse determines<br />
whether the signal originated inside or outside the apparatus. This approach seems to be the most<br />
promising for on-line electric PD detection.<br />
3.13.3.1.3.2 Acoustic Method — Like electrical methods, acoustic methods have a long history of use for<br />
PD detection. The sensitivity can be shown to be comparable with electric sensing. Acoustic signals are<br />
generated from bubble formation and collapse during the PD event, and these signals have frequencies<br />
of approximately 100 kHz (Bengtsson et al., 1993). Like the electric signals, the high frequencies are<br />
generally attenuated during propagation. Due to the limited propagation velocity, acoustic signals are<br />
commonly used for location of PD sources.<br />
The main advantage of acoustic detection is that disturbing signals from the electric network do<br />
not interfere with the measurement. As the acoustic signal propagates from the PD source to the<br />
sensor, it generally encounters different materials. Some of these materials can attenuate the signal<br />
© 2004 by CRC Press LLC<br />
© 2004 by CRC Press LLC