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Cascading Collapse Assessment Considering Hidden Failure
1Nur Ashida Salim
1Faculty of Electrical Engineering
Universiti Teknologi MARA
13500, Penang, Malaysia
2Muhammad Murtadha Othman
2Ismail Musirin
2Faculty of Electrical Engineering
Universiti Teknologi MARA
40450, Shah Alam, Malaysia [email protected]
3Mohd Salleh Serwan
3Advanced Power Solutions Sdn. Bhd
Worldwide Business Centre,
Jalan Tinju 13/50,
40000, Shah Alam, Selangor [email protected]
Abstract — Hidden failure relay protection is the major cause
of cascading failure in power system. Therefore, in this study, a
hidden failure model has been developed to study the impact of
certain parameter that could cause cascading collapse. The
parameters that could lead to major blackout include system
loading level, spinning reserve, hidden failure probability and
other factors. As the overall load is the key factor that could
affect the risk of cascading outages, this study will reveal the
impact of it to the system. A test system of IEEE 24 bus RTS is
used as a case study. The hidden failure model adopts here is
the steady state analysis, which is caused by line tripping. The
significant loads at which blackout risk sharply increases are
identifiable for cascading collapse. This study can provide
guidance for the utility on when and how to mitigate the
cascading collapse from spreading to the entire power system.
This study also can determine the critical loading in the risk of
cascading failure.
Keywords – power system reliability; cascading collapse; hidden
failure;
I. INTRODUCTION
In a deregulated power system, the aim of the utility is to supply electricity in a reliable, secure and economical manner[1]. Therefore, each utility company is competing among themselves to provide such quality energy. For that reason, they are trying to avoid any disturbances affecting the system. One of the disturbances that could cause danger for the power system to operate effectively is cascading collapse.
Cascading collapse is continuous outages of components in a power system network. Even though the disturbance rarely happens, but if it does happen, it would disturb the whole system. For worse cases of power system outages, it could lead the whole system to blackout. Cascading failure happens when a component in the power system fails to operate properly and trip. Then the faulted component can affect other components that are connected to it. This situation will go on until the entire system collapse if prevention does not take action fast.
Some major blackouts caused by cascading failure have been reported in [2-4]. According to those reports, most of the cascading failure occurs during ‘shoulder’ periods, which is during spring or fall, and also when the lines or generators is outage due to service or maintenance. This results in higher probability of a cascading outage caused by the
unexpected forced outage of the components near to the faulted one.
In this research a cascading collapse assessment is performed by taking into consideration the total load variation in the system. Here, as the overall load in the system increases, the severity of the system is monitored in terms of its average probability distribution function. The system is in stable condition if the curve shows indication of some exponential decay. However, if the curve shows a power tail, therefore the system is in unstable condition. The critical loading is identified during this condition. The test system used for this case study is the IEEE 24 bus RTS system.
II. CASCADING COLLAPSE MODEL
Throughout the history of the interconnected power
system, disastrous outages of the electric power systems
have been occurring for quite some time. Cascading
collapse is the one of the main reason of the major blackout.
Therefore, actions need to be taken to analyze the cascading
collapse so that it could be mitigated. Some models of
cascading collapse analyses used nowadays are listed below.
A. OPA model
OPA model that has been studied in [5-7] is a blackout model in power system that represents probabilistic cascading line outages and overloads [8]. OPA looked into the overall dynamics in the network blackouts. Its initial outage is generated by random line outages and load variation. When line outage happened the new solution is obtained. Linear programming (LP) and DC load flow is modeled in OPA aiming to reduce the cost function. The overall process basically is to generate a possible cascade of failures and the LP dispatch optimization is obtained.
B. CASCADE model
Other model to model blackout in power system is by
using CASCADE model. CASCADE model that has been
explored in [7, 9, 10] is a probabilistic model that the
cascading is depending on the loading. The initial
disturbance is created by increasing the load to the
components and caused outage. The situation continues as
the load of each components increased. Even though this
model can analyze major blackouts, it assumes all
2011 First International Conference on Informatics and Computational Intelligence
978-0-7695-4618-6/11 $26.00 © 2011 IEEE
DOI 10.1109/ICI.2011.59
318
2011 First International Conference on Informatics and Computational Intelligence
978-0-7695-4618-6/11 $26.00 © 2011 IEEE
DOI 10.1109/ICI.2011.59
318
transmission lines are the same. It also neglects the network
configuration while performing the redistribution during
overload.
C. Hidden Failure model
Of all the failures occurred in a power system, the ones
that remain hidden are the most crucial. According to [11],
more than 70% of power system major disturbances
involved hidden failure protection system. Ref. [9, 11, 12]
adopts hidden failure model to study the distribution of rare
events. It uses DC load flow during the simulation and
performs tripping until the system collapse.
III. CRITICAL LOADING
It is sure that as load in the power system increases, the
possibility of a blackout to happen is more likely to happen.
In a complex network, criticality is associated with power
tails in the probability distributions [13].
The critical loading is important in power system
operation because it identifies a reference point for rising
risk of large disturbances[14]. If the curve of probability
distribution shows some indication of power tail, it indicates
bad situation could happen to the network. Other than that,
there is also a gain in the economics point of view. One of
the aims of exploring the risk analysis of cascading failures
is to identify and quantify the tradeoffs so that early defense
actions can be made. The concept of load increment on the distribution of
blackout size is shown in Table. I. If the network is operating at a very low loading level, each component has a minimum impact of failure. This could result a large operating margins of that components. Nonetheless, if the system is operating at a very high loading, each component is operating near their operating limit. As a result, if an initial outage happens, it would cause cascading outages, leading to a total or near total blackout.
IV. HIDDEN FAILURE MODEL
During normal operating condition, hidden failure
cannot be detected [15]. However it will exposed as a
straight effect of further system disturbances. Hidden failure
could cause a relay system to incorrectly separate circuit
elements[16]. In this study, line protection hidden failure is
adopt to model the operation of protection relays.
Each line has a different probability of tripping depends
on the load. In this study, it is modeled as an increasing
function of the line flow as seen by the line relay. The
probability is at a pre-determined value below the line limit
and will increase linearly up to 1.4 times of the line limit.
The probability of incorrect line tripping is modeled as in
Fig. 1[12].
TABLE I. LOG-LOG PLOT FOR DIFFERENT TYPES OF LOADING LEVEL
Log-log plot Loading Level Characteristic
Very Low Load Exponential tails
Critical Load Power Tail
Very High Load Total blackout
likely
Figure 1. Probability of an exposed line tripping incorrectly In hidden failure model, DC load flow approximation is
adopted in the simulation. This is due to the linearized, lossless power system which is equivalent to a current source in a resistive circuit. Here, the transmission lines are considered like resistors along with the generation and load is current sources and sinks.
Here, the model uses DC load flow is a method to estimate power flows through an AC system, assuming that the entire system has a voltage magnitude of 1.0 per unit. It also neglects the transmission line resistance. DC load flow solution is non-iterative. It has the solution of convergence at every simulation.
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V. CASCADING COLLAPSE ALGORITHM
The simulation procedure starts from a base case load
flow and follow the following steps. The procedure is
repeated for N time to obtain the statistical distribution of
the blackout.
1) Increase load at each load bus. Select an initial line as the
triggering line and trip that line. Perform the DC load
after each tripping.
2) Check for line flow violations and trip the line upon
violation.
3) If there is no more violation, determine the currently
exposed lines, which are all lines that are connected to
the last violated lines.
4) Determine the probability of incorrect tripping for each
exposed line according to Fig. 1.
5) For each exposed line/generator, generate a random
number to determine whether the line/generator tripped.
(The exposed line/generator will trip if its probability of
incorrect tripping is greater than the random number
generated)
6) For the exposed lines, determine the conditional
probability of tripping, Pcj for each stored exposed
line/generators as in equation (1).
( )∏∏ −=didnt trip that lines all
j
that triplines all
jcjp1 p P
(1)
7) Record the lines that tripped.
8) Compute and plot the probability distribution function
(PDF) for the cascading outage.
9) Repeat until no lines are loss or all lines connected to any
bus are tripped (blackout).
The overall flow chart for the algorithm of cascading
collapse is depicted in Fig. 2.
Figure 2. Overall flow chart for cascading collapse
VI. RESULTS & DISCUSSION
The simulation uses the IEEE 24 bus RTS as shown in
Fig. 3. This system has two areas with three generators in
area one and eight generators in area two[17]. For each
generator in the same area, it is assumed to own by the same
owner. As for the loads also, for each load in the same area,
it belongs to the same owner. The simulation starts from a
base case load flow. A line is selected as the triggering
event and the following procedures is repeated N times.
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Figure 3. Single line diagram for IEEE 24bus RTS
In this study, the outages correspond to the internal
cascading processes is the transmission line outages. With a
line tripping as the initial triggering event, the algorithm is
simulated for several times to get the accumulated statistics
of the probability distribution function (pdf) of blackout
size. Here, the overall loading is varies from very low up to
very high loading.
At a very low loading, the impact of cascading collapse
to occur is very minimal. During this condition, the
components have large operating margin that could make
them secure for any failure. However if the power system is
operated recklessly at a very high loading, each components
in the system will operate near to its emergency operating
limit. Therefore, the tendency for the system to collapse
with an initial outage is high.
It is obvious that the pdf of the blackout size changes
from the exponential tail form to the certain total blackout
form as the overall loading increases.
Figure 4. Variation of pdf with loading
Fig. 4 shows the average pdf as a function of loading
level for the IEEE 24 bus RTS network. From 100% of the
loading level until up to 200%, the average pdf for the
system to collapse is significantly small until it approaches
to zero. As the loading level increases above 200%, the
average pdf increases. From the curve, it can be seen clearly
that the change in slope occurs near loading of 210%. At
this point of loading level, the pdf start to increase as the
loading level increases.
Figure 5. PDF of blackout at 200% loading
Fig. 5 shows the average pdf of blackout as function of
loading. Here, the loading level is 200% of the overall total
loading. The curve shows an exponential decline in the
average pdf. This implies that the system is still in stable
condition.
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Figure 6. PDF of blackout at 210% loading
Fig. 6 shows the average pdf of collapse at loading level
of 210% of the accumulated load in the system. During this
loading level, the curve still shows an exponential decay in
the average pdf. This also implies that the system is still in
stable condition.
Figure 7. PDF of blackout at 220% loading
Fig. 7 shows the average pdf of collapse at loading level
of 220% of the total load in the system. At this loading
condition, the curve still shows an exponential decay in the
average pdf. At this condition, the system is still in stable
condition.
Figure 8. PDF of blackout at 240% loading
Fig. 8 shows the average pdf of blackout at loading level
of 240% of the accumulated load in the system. During this
loading level, the curve shows some indication of power
tail. From the curve, the critical loading at which the system
starts to become unstable can be identified.
The results obtained for IEEE 24 bus RTS is quite
similar to those using the OPA model for IEEE 118 bus
system as in [18, 19] and hidden failure model for WSCC
179 bus system performed in [9]. However, by using the
CASCADE model as in [9, 20] the results obtained
qualitatively similar but the results are sharper.
From the results obtained, hidden failure and OPA
model presents similarity in the loading assessment for
probabilistic cascading outages that could cause cascading
failure due to the criticality in loading level. The
significance of this research is that it can exactly monitor
the critical loading as it is one of the parameters that have to
be concerned in the power system operation. The threat of a
major disturbance could be reduced by reducing the overall
loading of the system in order to obtain an exponential tail.
By performing this analysis, the system operator could find
the loading margin to the critical loading as a preparation to
face large blackouts.
VII. CONCLUSIONS
With the development of power market, power system
are forced to operate near the critical loading as proposed in
[5, 19]. This is because, the industry need to supply as much
electricity as possible in order to compete among other
utility. However, by implementing this analysis in their
system operation, the chances of a large blackout to happen
can be reduced. A hidden failure mechanism has been
developed using a DC load flow model to study the
behavior of cascading collapse of a transmission network
during the variation of overall loading level in the system.
This shows that the power system will follow power law
near the critical loading. Also, the average pdf will increase
sharply as the load increases. Although the DC model of
hidden failure presents a simplified system than the real
system operation, it is a benchmark for further research to
fully understand the idea of cascading failures. This research
could be further pursued in the dynamic analysis where the
frequency violation is monitored with an initial generator
tripping.
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