[James_H._Harlow]_Electric_Power_Transformer_Engin(BookSee.org)

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