07-02-2013, 04:54 PM
A New Method of Voltage Sag and Swell Detection
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Abstract
The fundamental voltage, current, and phase angle
are required for a wide variety of power system applications. An algorithm
that is capable of calculating or estimating these quantities
in real time, in the presence of distorted waveforms, finds application
in diverse areas of power systems. Techniques to detect voltage
sag include the root mean square (rms), Fourier transform, and
peak voltage detection methods. The problem with these methods
is that they use a windowing technique and can therefore be too
slow when applied to detect voltage sags for mitigation since they
use historical data. Recent work in the field of signal processing has
led to an algorithm that can extract a single non-stationary sinusoidal
signal out of a given multi-component input signal. The algorithm
is capable of estimating the amplitude, phase and frequency.
In this paper, the algorithm is compared to existing methods of sag
detection.
INTRODUCTION
POWER quality has been the focus of considerable research
in recent years. Voltage sags, in particular, can cause expensive
downtime. Voltage sags are defined as a decrease in root
mean square (rms) voltage at the power frequency for durations
from 0.5 cycles to 1 min [1]. The duration of a voltage sag is the
time measured from the moment the rms voltage drops below
0.9 pu of nominal voltage to when it rises above 0.9 pu of nominal
voltage. It is therefore possible for sags of short duration to
cause problems in some sensitive equipment.
Voltage sag may be caused by switching operations associated
with a temporary disconnection of supply, the flow of inrush
currents associated with the starting of motor loads or the
flow of fault currents. These events may emanate from the customers
system or from the public supply network. Lightning
strikes can cause momentary sags. Voltage swells are brief increases
in rms voltage that sometimes accompany voltage sags.
They appear on the unfaulted phase of a three-phase circuit that
has developed a single-phase short circuit.
EXPERIMENTAL SETUP
For experimental testing of the performance of the algorithm,
a voltage sag generator was required that is capable of generating
sags of varying magnitudes and duration.
Fig. 5 shows the experimental setup that was arranged to conduct
the experiments. A transformer was used with two output
voltages. The first output was set to 100% rated voltage. The
second output was set to the required sag magnitude value. It
has taps that can be set from 40 V to 400 V in steps of 40 V. A
TMS320F240 processor was used to log data and switch solid
state relays very fast between the two outputs to obtain the desired
sag magnitude and duration. When testing the performance
for rate of change, a cascaded configuration was used. A resistor
bank was used as a load.
INFLUENCE OF POINT ON WAVE
The point on wave is the instant on the sinusoid when a disturbance
begins. In practice, one cannot control the point on
wave when a fault occurs. Hence for a sinusoidal waveform,
the point-on-wave is minimum near the zero-crossing area and
maximum near the peak value of a waveform. For simulations
presented in this section, Matlab Simulink is used as the computational
tool.
Sag
Fig. 10 shows the algorithm tracking 80% voltage sag at 0 ,
90 , and 180 point on wave. Point on wave was simulated for
different sag magnitudes. For the best scenario, a detection time
of 1ms was recorded. This corresponded to 20% sag at 90 point
on wave. Results from simulations show that detection time is
affected more by sag magnitude than point on wave. The worst
delay time recorded was 4 ms (less than quarter cycle). This
corresponded to 80% sag at 180 point on wave.
CONCLUSION
A new algorithm has been presented and applied to the detection
of sags in power systems. It has been compared to existing
methods. This research shows the ability of the algorithm to detect
voltage sag quicker than existing methods. This has a distinct
advantage when mitigation is concerned. Time saved can
be translated into reducing the component of lost energy during
the sag. The algorithm can be further extended to voltage sag
analysis. For analysis, the algorithm offers the ability to calculate
the amplitude, frequency and phase angle jumps of the sag.
The influence of point on wave, magnitude and frequency variations
has been investigated. It was found that sag magnitude
has the greatest influence on the detection time. At worst case,
it was shown that the proposed algorithm can detect voltage sag
within 4 ms.