Electrical Power Quality & Utilization Magazine
Volume 3, Issue 2
Power Quality Monitoring System—
Voltage Dips, Short Interruptions
and Flicker
Daniel Kottick, Israel Electric Corp.
Available online August 2008
EPQU Magazine
Electrical Power Quality & Utilization Magazine
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Abstract
The Israel Electric corp. deployed a nationwide Power Quality Monitoring System
(PQMS). The PQMS monitors the quality of supply of all of its high voltage costumers, as
well as a sample of its medium voltage customers. The PQMS monitors both the voltages
and the currents. The PQMS has been operational since October 2005. The paper
presents preliminary measurement results obtained from the PQMS.
1. Introduction
In recent years, Power Quality (PQ) assessment and improvement has been attracting a
growing amount of attention, as the standard of living increases, and as a result of the
growing dependency on a reliable and continuous supply of electricity [1]. The cost of
damages caused by PQ events has become significantly higher than in the past,
because of the increased usage of electronic equipment in the industrial and commercial
sectors [2, 3, 4].
Semiconductor switching devices are being utilized for a wide range of applications in the
domestic, commercial and industrial sectors. These devices offer economical solutions
for the optimized use of electrical energy. However, they have nonlinear characteristics,
and thus cause disturbances to the voltage and current waveforms. At the same time,
semiconductor devices are sensitive to PQ problems.
For the last few years, PQ has become an increasingly important issue. Some electric
companies worldwide have initiated monitoring systems and have begun tackling PQ
related problems [5, 6]. When tackling PQ problems, there are two main approaches: The
first deals with the planning and manufacturing of electrical equipment, which is designed
and manufactured according to standards that ensure that it is less sensitive to PQ
events [7]. The second approach involves the installation of mitigation equipment that
depends on the characteristics of the protected device. The costumer usually buys the
mitigation equipment. The most commonly used mitigation equipment is the
Uninterruptible Power Supply (UPS).
The Israel Electric corp. deployed a nationwide Power Quality Monitoring System
(PQMS). The PQMS monitors the quality of supply of all of its high voltage costumers, as
well as a sample (200 measuring points) of its medium voltage customers [8]. The PQMS
monitors both the voltages and the currents. The PQMS has been operational since
October 2005.
This paper presents preliminary measurement results. A large majority of customer
complaints regarding power quality focus on short interruptions and voltage dips. A
statistical analysis of the latter phenomena is presented. In order to demonstrate the
ability of the PQMS to serve as an investigation tool a specific event of flicker caused by
a metal processing plant is introduced.
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2. Definitions
This section summarizes the definitions and causes of the PQ phenomena that are dealt
with in this paper.
2.1 Voltage Dips
A typical voltage dip, as measured by the PQMS, is presented in Figure 1. The voltages
of the PQMS are measured at the secondary coil of the billing transformer (22 kV / 0.110
kV).
Figure 1 – Typical voltage dip.
The main causes of voltage dips are: short-circuits in the supply network, start-up of large
loads, faulted grounding system and failures of the customer facilities.
Aggregation of Voltage Dips
A voltage dip may refer to one phase, two or three phases. A three phase voltage dip can
be symmetrical or asymmetrical. Aggregation of voltage dips is sometimes performed in
order to represent it according to two parameters: duration and depth.
A method of three phase voltage dip aggregation is presented in Figure 2. The dip starts
when the voltage of the first phase drops bellow a certain point and ends when the last
phase recovers (in Figure 2 Δt = t6—t1). The dip depth is defined as the maximum of the
three values of the different phases (in Figure 2 ΔV = max[ΔV1, ΔV2, ΔV3])
If several dips occur in a relatively short time frame, for example two dips that are caused
by a short-circuit in the supply network that is not cleared after the first re-closing of the
current breaker, a second aggregation step may be performed. A series of two dips is
presented in Figure 3.
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∆V1
t1
t2
∆V2
3 phase voltage dip
t3
t4
∆V3
t5
t6
∆V
Aggregated voltage dip
t1
t6
Figure 2 – Aggregation of a three phase voltage dip.
∆V1
t1
t2
∆T
∆V2
t3
t4
Figure 3 – Multiple voltage dips.
The second step of the aggregation is performed if ΔT < T (for example T=1 sec.). The
parameters that describe the aggregated event are: depth ΔV = max[ΔV1, ΔV2] and
duration:
(1) Δt = t4—t1,
(2) Δt = max[(t2—t1)(t4—t3)],
(3) Δt = (t2—t1)+(t4—t3).
2.2 Short Interruptions
An interruption of a medium voltage customer as measured by the PQMS is presented in
Figure 4. The voltages of the PQMS are measured at the secondary coil of the billing
transformer (22 kV / 0.110 kV). According to the definitions used by IEC a short
interruption lasts less than 3 min. The Figure represents only the first few cycles following
the interruption and the first few cycles after the voltage restoration. The main causes for
short interruptions are: short-circuits in the supply network and malfunction of customer
facilities.
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200
100
0
-100
-200
-20
200
Voltage
Voltage
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30
80
100
0
-100
-200
Tim e [m Sec.]
Tim e [m Sec.]
Figure 4 – Interruption of a M.V. customer (the first few cycles following the interruption
and the first few cycles after the voltage restoration).
2.3 Flicker
Figure 5 presents flicker fluctuating voltage and its spectrum.
Figure 5 – Voltage flicker, a. voltage fluctuations, b. spectrum.
The main causes of flicker are the following loads: pulsed power output where there is
burst-firing control, arc furnaces, drives with steeply changing loading etc.
The assessment of the flicker level is based on human response to voltage fluctuations
and its influence on illumination systems.
3. Power Quality Monitoring in Israel
The Israel Electric Corp. (IEC) has deployed a Power Quality Monitoring System
(PQMS), which was purchased from TeamWare s.r.l. - Italy. The PQMS consists of 200
monitoring units (Figure 6) connected to the points of common coupling of 200 medium
voltage customers (the IEC has app. 2800 medium voltage customers), as well as 30
monitoring units measuring high voltage customers.
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Figure 6 – Power quality monitoring unit.
At the time when the IEC decided to deploy its PQMS it had limited knowledge on the
statistical distribution of the power quality indices. In the past the IEC gathered data on
short interruptions of medium voltage distribution lines (not customers). The
determination of the sample size of the PQMS was based on analyzing this data. The
sample size was determined according to the following equation:
S2
n=
S2
N
( )
+d X
2
Z2
Where:
N
- Population size.
n
- Sample size.
2
S
X
d
- Disturbance variance.
- Disturbance average.
- Error ratio.
In order to estimate the average number of short interruptions per power line and year
with an error of up to 20% the sampling size should be (N=1063 power lines, S=9.4,
X=5, d= 0.2, Z=1.7):
9.4 2
9.4 2
+ 1 2
1063
1.7
= 206
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The IEC decided to use this result for its PQMS size.
Each monitoring unit is equipped with local memory, capable of storing data complying
with EN50160. The data is then transferred via cellular communication to a central
database. All the monitoring units are equipped with a GPS card in order to synchronize
their time stamps.
A schematic layout of the PQMS is presented in Figure 7.
Field unit
Field unit
Cellular network
direct TCP/IP
connection for data
transfer
Internal IEC network
communication
Communication computer for
data collection
Communication computer for
data collection
Database software
Database
Managing software
Client software
Prepared by: Information Systems & Communications division
Figure 7- Schematic layout of the PQMS.
The PQMS consists of several layers:
Computers
-
Users, managers, a data acquisition server and a
communication server
Communication
-
TCP/IP communication for data transfer.
Monitoring units
-
Installed at the point of common coupling of medium
voltage consumers. Each monitoring unit is equipped with
a GPS card for time synchronization.
The PQMS has been fully operational since October 2005.
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4. Short Interruptions and Voltage Dips
This section presents data regarding short interruptions and voltage dips during a one
year period from June 2005 to June 2006. At that time, the PQMS was composed of 122
monitoring units that were operational during the whole period. A distribution of the
number of voltage dips per customer during the one year period is presented in Figure 8.
Number of Voltage Dips
Distribution of Voltage Dips
14
12
10
8
6
4
2
0
May- Jul05
05
Sep- Oct- Dec- Feb- Mar- May- Jul05
05
05
06
06
06
06
Figure 8 – Distribution of the number of voltage dip per customer and year.
Each point in Figure 8 represents the average number of voltage dips per customer for
each month. The graph also includes a tendency curve. The tendency curve is a fourth
order polynomial function that approximates the measured number of dips with a
minimum means square error. One can observe that most voltage dips occur during the
winter months (October-February). The rain in Israel appears only during winter period
while the summers are dry. Contamination on insulators built up during the summer and
the rain causes flashovers that cause voltage dips. The average number of voltage dips
per customer per year was 87.
The distribution of voltage dips according to time is presented in Figure 9.
Number of Voltage Dips
Distribution of Voltage Dips
7
6
5
4
3
2
1
0
0
5
10
15
20
Time [Hour]
Figure 9 – Distribution of the number of voltage dip per customer and day.
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Each point in Figure 9 represents the average number of voltage dips per customer
during periods of one hour; the graph also includes tendency curve. The tendency curve
is a fourth order polynomial function that approximates the measured number of dips with
a minimum means square error. One can observe that most voltage dips occur during the
night hours, probably because the relative humidity during nigh-hours is higher than that
of day-hours.
A distribution of the number of short interruptions per customer during the one year
period is presented in Figure 10.
Number of Short Interruptions
Distribution of Short Interruptions
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
May- Jul- Sep- Oct- Dec- Feb- Mar- May- Jul05
05
05 05
05 06
06 06
06
Figure 10 – Distribution of the number of short interruptions per customer and year.
Each point in Figure 10 represents the average number of short interruptions per
customer for each month, the graph also includes tendency curve. The tendency curve is
a fourth order polynomial function that approximates the measured number of dips with a
minimum means square error. One can observe that most short interruptions occur
during the winter months (October-February). The average number of short interruptions
per customer per year was 5.7.
The distribution of short interruptions according to time is presented in Figure 11.
Number of Short
Interruptions
Distribution of Short Interruptions
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10
20
Tim e [Hour]
Figure 11 – Distribution of the number of short interruptions per customer and day.
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5. Flicker Event
On December 17, 2006 a flicker event was caused by a high-voltage metal processing
plat. Figure 12 presents the Pst parameter and the minimal voltage during the event
period.
minimal voltage
80.00
75.00
70.00
65.00
60.00
55.00
50.00
45.00
40.00
17/12/2006 17/12/2006 17/12/2006 18/12/2006 18/12/2006
00:00:00 10:00:00 20:00:00 06:00:00 16:00:00
Flicker
2.00
1.50
1.00
0.50
0.00
17/12/2006 17/12/2006 17/12/2006 18/12/2006 18/12/2006
00:00:00 10:50:00 21:40:00 08:30:00 19:20:00
Figure 12 - Pst parameter and the minimal voltage during the event period.
During the event the flicker level reached Pst=1.7, this rise in the flicker level was
accompanied by a power consumption rise as can be seen in Figure 13. One can notice
the high correlation between the power consumption changes and the flicker level.
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Flicker
2.00
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
17/12/2006 17/12/2006 17/12/2006 18/12/2006 18/12/2006
00:00:00 10:00:00 20:00:00 06:00:00 16:00:00
Pow er consumption
500.00
400.00
300.00
200.00
100.00
0.00
17/12/200617/12/200617/12/200618/12/200618/12/2006
00:00:00 10:00:00 20:00:00 06:00:00 16:00:00
Figure 13 – Flicker and power consumption.
Figure 14 presents flicker levels during the event as measured at some other highvoltage customers.
2.00
Flickr
1.50
1.00
0.50
0.00
17/12/2006 18:00:00
17/12/2006 18:50:00
Metal Plant
Cust. I
Cust. III
Cust. IV
Cust. II
Figure 14 - Flicker levels during the event as measured at some high-voltage customers.
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One can notice that the flicker event that was caused by the metal plant was spread
through the network and influenced other customers.
6. Conclusions
Power quality problems constitute a growing concern, as electricity consumption
becomes more and more vulnerable to distortions of the sinusoidal voltage and current
waveforms. A distorted waveform can cause malfunction of electronic equipment and
electrical control systems. It is therefore important to monitor the quality of the electric
power supply and to mitigate it according to international standards.
The paper described the power quality monitoring system installed in Israel, and
presented the following one year measuring results:
•
The Distribution of voltage dips during a one-year period was
analyzed. An M.V. customer suffered on average 87 voltage dips,
most of which occurred during the winter months (OctoberFebruary).
•
The distribution of voltage dips according to time was analyzed.
Most voltage dips occurred during the night hours.
•
The Distribution of short interruptions during a one-year period
was analyzed. An M.V. customer suffered on average 5.7 short
interruptions, most of which occurred during the winter months
(October-February).
•
The distribution of short interruptions according to time was
analyzed. Most short interruptions occurred during the morning
hours.
•
A Flicker event caused by a metal processing plant was presented
and analyzed.
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