Using a Neural Network to Predict the Dynamic
Frequency Response of a Power System to an
Under-Frequency Load Shedding Scenario
Matthew A. Mitchell
Student Member
INESC Porto
Faculty of Engineering
University of Porto
Polto. Portugal
J.N.Fidalgo
J.A. PeGas Lopes
Senior Member
m c Porto
Faculty of Engineering
University of Port0
Porto, Portugal
Abstract - This paper proposes a method to quickly and
accurately predict the dynamic response of a power system
during an under-frequency load shedding scenario. Emergency
actions in a power system due to loss of generation typically
calls for under-frequency load shedding measures to avoid
potential collapse due to the lack of time in which to correct the
imbalance via other means. Due to the slow and repetitious use
of dynamic simulators the need for a fast and accurate
procedure is evident when calculating optimal load-shedding
strategies. A neural network INN) seems to be an ideal solution
for a quick and accurate way to replace standard dynamic
simulations. The steps taken to produce a viable NN and
corresp~ndingresults will be discussed.
James D.McCalley
Senior Member
INESC Porto
Faculty of Engineering
University of Porto
Porto,Portugal
Dept. of Electrical and
Computer Engineering
Iowa State University
Ames, IA.USA
The dynamic analysis of an electrical network after a
disturbance is a very time demanding task. Thus in recent
years a large effort has been made toward developing fast
approaches to deal with the prediction of system dynamic
behavior, using namely neural networks [I], decision
trees [2], and regression trees [3].
In this paper, a NN is proposed to predict the systems
dynamic frequency response through the motivation of
trying to identify load shedding strategies that will lead to
minimum amounts of load to be shed.
In section I1 the current under-frequency load shedding
strategies will be discussed. In section III the steps taken
to create the proposed neural network will be addressed,
followed by numerical analysis and conclusions in
sections IV and V respectively.
Keywords - Neural network, under-frequency load shedding,
dynamic system response.
I. INTRODUCTION
Dynamic security is a crucial issue with respect to the
operation of power systems, especially isolated systems
due to their natural weaknesses. Following a system
disturbance, a fast assessment of the systems robustness
in reference to dynamic behavior and the identification of
effective emergency control measures that prevent system
collapse are most important. Throughout the world there
are numerous medium size isolated networks, especially
on islands, but also in some continental regions where,
due to economical reasons, it is not feasible to have
interconnections. In some of these systemq, the
contribution of wind power is playing a more significant
role as it provides a cheaper and independent way of
producing electricity. However. in these cases, the
vulnerability of the system may increase if large
penetrations of wind power are foreseen.
11. STATE OF THE ART
A. Load Shedding
As already mentioned, when a power system disruption
creates a large generation load imbalance, resulting in a
frequency decline, emergency action such as underfrequency load shedding may be needed. If system
frequency reaches a given threshold, even for a short
amount of time, power stations may trip off resulting in
further load imbalance which may lead to a global system
collapse.
When there is a rapid decline in frequency, simple
governor response may be neither sufficient nor quick
enough to stop the frequency excursion before it reaches
the protection threshold of frequency relays in other
power plants. Thus, there is a need for a complementary
emergency action in order to assure that the declining
frequency is stopped before reaching this threshold.
Although load shedding is usually effective, problems can
arise due to ineffective shedding, which can lead to
system collapse anyway [4]. Therefore the response of the
power system to a frequency decline needs to be
understood in order to judge how these influences will
effect the steps taken in preparing an efficient and swift
load shedding scheme [4,5,6].
Typically load shedding schemes are developed following
the same basic design, in which under-frequency relays,
In any isolated sysiem, the loss of a production facility
will lead to a severe disturbance that must be effectively
compensated using the spinning reserve. Assuming
enough spinning reserve is available, generator frequency
regulators must respond fast and effectively, otherwise
emergency measures should be taken, like load shedding,
to avoid a frequency system collapse.
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Member
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Table 1
Frequency Variable
Knowledge Acquired
determines amount of load needed to be
shed in order to, minimize Initial frequency
determines the first relay’s frequency setting and
time delays, in order to insure a quick response
will be dose to nominal
I
Maxjrnum
Positive Devlation
df,dt
Residua Frequency
I
helps minimize over-shedding thus avoiding the activation of any over-frequency protection
devices, thus contributing more losses to the disturbance
assesses the sevefity and location of the
disturbance
determines the amount of load to be shed, In
order to insure that the resldual frequency Is
above the deslred threshold
The procedure to be followed in this case involves four
main steps.
1. Identification of input / output relevant variables
2. Data set generation
3. Design of the NN
4. Performance evaluation of the neural nets
In reference to the output variables, and as mentioned
Identification of input / outputs
before, the interest in knowing the maximum negative and
positive frequency deviation. the final system resting
frequency, and the slope of the first negative swing lead
to the selection of these 4 variables as outputs of the NN.
The identification of the variables that are going to
characterize a given operating scenario is an important
step for a successful application of these techniques.
Sometimes a pre-processing stage is needed to select the
most relevant variables to be used as inputs of a NN. In
this work, we decided by just selecting a set of
meaningful variables considering the following issues:
availability from SCADA, direct physical meaning
towards the phenomenon, and potential controllability.
Having this in mind, in this research the following
variables have been used as inputs of the NN.
0
Actual real power generation;
0
Available real power (effective spinning reserve);
Active load generation level prior to disturbance;
Amount of active load being shed;
Percentage of exponential type Loads being shed.
b
B.
Generation of the Data Set
The replication of a given power systems response
through any machine learning technique, like a NN, can
only be accurate if the data used to train these structures
describes with enough coverage and quality the different
operating conditions. Optimally. this data set would
include all possible system scenarios, however this would
require unrealistic hours of computational time. Therefore
the objective of the data generation stage is to capture the
breadth of the system operating conditions and behavior,
while limiting computational and engineering efforts.
This data set includes the data used for training a NN and
the test data for evaluation purposes.
Supplying the NN with actual and available real power
enables it to make the needed correlation between the
dynamic frequency response and system generation and
effective spinning reserve levels.
Due to the potentially numerous system disturbances that
could lead to a load shedding situation it is unfeasible to
create a NN that could predict the system.. dynanuc
behavior for all of them. Therefore, we decided to
develop the approach so that a single predicting structure
will be used to deal with a single pre-specified
disturbance.
With respect to system loading levels and having in mind
the characteristics of isolated systems (single dynamic
area), individual loading levels were not needed.
However, since a complex load model [ 161 is being used
within this work and each load type has its own profound
influence on the systems frequency response it is critical
that each load type is used separately as an input to the
NN, so it can produce more accurate conclusions. Thus
load level inputs to the NN can be broken down into total
real ZIP (constant impedance, constant current, comtant
power types), exponential 1, and exponential 2 load types.
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determines if any other system IntewenUon will
be needed to recapture nominal frequency
Finally concerning load shedding variables. the total
amount of real load, percentage of exponential types 1,
and 2 that are being shed were used. These variables
provide the NN with valuable information, such that it
can make the needed assessment with respect to how
much the generation-load imbalance has been corrected
and the influence each load type has on the resulting
frequency response. Therefore. for the case under study,
there are a total of 21 inputs to the NN.
The next sections provide a description of the procedure
adopted to deal with each one of these steps.
A.
I
~~
determine the time delays needed, or what delays
to expect from df/& sensflive relays
Having in mind the particularity of the network under
analyses, namely that the wind parks in the network
constitute its weak point. the following disturbance was
considered:
a short circuit in the lines connecting to the wind park,
producing afterwards the disconnection of this production
facility, and hence creating a generation-load imhalairce.
347
assuming instead a worst case scenario. Therefore, actual
scenarios frequently lead to excess load shedding.
Utilities would like to minimize the amount of load to be
shed, therefore the load shedding scheme should change
according to the system operating conditions. This means
that investigation is needed in the field of defining remote
settings of U F L S relays.
at the feeder level, are set to operate at different triggering
frequencies and time delays, in order to arrest falling
system frequency [4,7,8,9,10,11].
Analyzing the operating conditions and disturbances that
may be encountered throughout system is the fvst step in
developing successful load shedding strategies. After a
thorough understanding of the system is attained, the
amount of system overload for which the load shedding
scheme will be designed needs to be selected. Next an
acceptable value for the post-shedding residual (resting)
frequency threshold of the system needs to be established.
This value is of great importance since it not only affects
the amount of shedding to plan for but it also influences
the potential for further intervention, by manual or
automatic system devices.
In order to deal with the minimization of the load
curtailment, a one step shedding scheme produces a better
frequency response than a stepping scheme, due to its one
time bulk shed versus cascade shedding [13]. The
identification of the new relay settings must be done in a
fast way, thus a new tool able to assess and predict system
behavior in these conditions must replace the
conventional dynamic evaluation.
Since system load and generation are constantly changing
to meet new consumer demands, the use of stepping
schemes and different relay settings to stop declining
frequency is best-suited [ 121.
In the work reported in this paper we are describing a new
approach to deal with the remote setting of UFLS relays,
to be applied in isolated systems that have large shares of
wind power production. In this approach two frequency
settings are considered, such that the first setting is
optimized and remotely defined and the second is kept
pre-defined and is to be used as final resource to save the
system if the first measures fail.
In this work we are assuming that the operation of the
system is based on AGC that is in charge of defining
globally the final generation set points. The required
amount of load shedding, in case of enough spinning
reserve in the system, is given by (1).
111.
RESPONSE
Prs = AP, - KAfss
Here
The need to determine and evaluate the dynamic
frequency response of a system during an underfrequency load shedding scenarios is of the utmost
importance. Establishing an effective under-frequency
load shedding scheme typically hinges on knowing what
to expect from a given disturbance, especially in regards
to the maximum negative and positive deviations, the
residual frequency, along with the slope of the first
negative swing. Prediction of these values can be done
using machine learning techniques as described in [ 141.
C P U , when positive, is the required amount of
load shed, dp, is the generation lost, K is the systems
stiffness, and Afss is the maximum tolerable steady state
frequency after shedding. In situations when there is not
enough spinning reserve the amount of load to be shed is
given by (2).
Obtaining knowledge about the system behavior after a
shedding scheme, following a given disturbance will help
in deciding between potential load shedding schemes. In
regards to specific frequency information. a summary of
the knowledge that can be acquired is described in table 1.
In situations where many evaluations of the dynamic
behavior of a system are needed, like in this case of
planning a load shedding scheme, the time taken by
dynamic simulators to perform complete frequency
evaluations is overwhelming. Therefore, machine learning
techniques are again well suited to deal with this problem.
NNs are particularly appropriate to do so, due to their
ability to map a nondescriptive function (like the one
associated with the system dynamic behavior) and to
provide fast response with sufficient accuracy. Their
success when dealing with similar problem.., for example
the prediction of the operation of under frequency load
shedding relays [IS]is well known.
Where SR is the amount of spinning reserve left [7].
The decision about the number of shedding steps and their
settings to deal with an emergency situation takes long
man hours of off-line planning and testing, analyzing
system response obtained through set-by-step simulation,
to come up with a well suited comprehensive plan.
With this type of strategy, once fixed the settings are kept
until a new assessment is made and it is determined that a
different and “bette? scheme is required in order to fulfill
new system security and reliability needs. With the onset
of a more competitive power market and the need for
better system reliability, reliance on these pre-determined
load shedding schemes may not be satisfactory. Namely,
step type schemes do not consider current system
conditions or new topologies, due to growth in system
size, nor do they optimize the load shedding selection,
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PREDICTING THE DYNAMIC
FREQUENCY
348
Wind Gen 1
c
1
3
iOUVA
()T
10 kV 6
These results are evidence that the NN has the capability
to predict the dynamic frequency response of the power
system during an under-frequency load shedding scenario.
c\,
5Mvar
A comparison was made with a dynamic simulator,
ETMSP, in which a loss of 7.9 MW of wind power
production was simulated. The numerical results obtained
from the dynamic simulator and the NN can be seen
graphically in figure 2 and numerically in table 3. As was
expected, the NN provides results that match quite well in
comparison with the ones obtained from full dynamic
simulation given through ETMSP.
+
6.6kV
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(MVA
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a
p
All conventional generators are synchronous
machines and are mathematically described using a
second order model;
48
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1.203
1.192
0.011
2.185
2.147
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49.991
49.999
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nrui.l,i,.nm-,
V.
CONCLUSIONS
This paper described the application of a NN to make a
fast prediction of the system behavior for a load
imbalance disturbance followed by a load shedding
control action.
The excellent results obtained in an isolated system show
the applicability of this method for the purpose of
evaluating the quality of different load shedding schemes
for a pre-specified disturbance. Due to the fast prediction
of system behavior, the dispatch center can select the
feeders to be disconnected in emergency conditions
(meaning the amount of load to disconnect). This can be
done by adapting the settings of the feeder UFLS relays
periodically, according to the range of operating condition
foreseen within a day time horizon.
Test
0.037 0.041 0.0R9 0.101 0.0475 0.0503 0.1130 0.1235
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I
DcvLabn(Hr)
RMS
Train
2S
Neural Network vs. Dynamic Simulator
b
Neural Network Performance
Test
20
I5
Table 3
Table 2
Train
1u
Figure 2. System Frequency Response
After the design of the NN (adopting the architecture
described before), a performance evaluation stage was
implemented. These results can be seen in table 2. As it
can be seen from this table, the NN's training
performance was quite satisfactory and provides a good
predictability with respect to the test set.
Train Test Train Test
5
lime (sec)
Using the data set generation procedure described in
section 111, 14,046 operating points have been obtained
which provides an excellent picture of the system
behavior.
Max'Pos'
Deviation
4
0
All applicable UFLS relays were set to trigger at a
frequency of 49Hz with a 3cycle time delay.
Abs. Deviation
49
41.5
The system analysis is being performed after
governor response, but before new AGC determined
set points can be established
All system loads were modeled using the composed
model referenced previously;
R M S Error
49.5
4R.S
In all synchronous generators, automatic voltage
regulators and governors are adopted using IEEE
models;
It was assumed that an AGC is used for establishing
all generators governor final production set points:
Mean
f
51
Figure 1. Terceira Network
Variable
NN Point&
9
,
51.5
Hydro Gen 1
Frequency
MW
349
EPRI’s Output Analysis Program (OAP) was used for
numerical analysis and creation of the NN target
variables.
The procedure used to develop the data set is laid out as
follows.
1. Generate system operating scenarios;
2. For each scenario, perform a load-flow simulation to
guarantee operating point feasibility and get a
complete characterization of that operating point
C.
The first step in the design of a NN is to determine an
architecture that will yield good results. The idea is to use
the simplest architecture while maximizing performance.
Usually, NN architecture is determined based on
subjective assessment on the part of the engineer.
(OP);
3.
4.
5.
6.
Define shedding scenarios, for each OP, considering
that the previous disturbance took place;
Perform dynamic analysis;
Retain frequency behavior parameters;
Save variables characterizing the OP and frequency
behavior parameters.
Within this work it was concluded after a few trials that
an architecture of 2 hidden layers, the first with 14 nodes
and the second with 10 nodes, was best suited for this
application.
Each of these steps will be discussed in further detail in
the paragraphs that follow.
An Adaptive Back Propagation technique was used to
train the NN [20]. This consists of the same routine as
typical back propagation with the exception that instead
of one learning rate for all the NN nodes, a learning rate
was assigned to each of the nodes in order to speed up
convergence. The activation function used within this
work was a hyperbolic tangent function and the inputs
were normalized to have a mean of zero and a variance of
one.
A structured Monte Carlo sampling technique was used to
generate the system operating scenarios [17,18] with an
adaptation for the purpose of localizing these scenarios in
order to limits its size, yet maintain its effectiveness. A
load curve was established in order to decrease the need
for scenarios outside of the established curve and instead
focus the generation of scenarios to areas most probable
to be realized in actual system conditions, i.e. the load
curve.
D.
After obtaining a complete set of scenarios covering the
probable range of operating conditions, a loadflow
simulation is performed on each to ensure feasibility and
calculate system losses. This step was achieved using
EPRI’s IPFLOW software.
+ Ploss *buffer
If the training set provides good results, in terms of
accuracy, and the test set does not, this generally indicates
over-fitting in the learning stage and/or that the current
NN structure is too complex and needs to be simplified.
On the other hand if the training set and test set provide
comparable results, but not satisfactory ones regarding the
user this generally implies that a more complex structure
should be tried. Once desirable results are attained from
both the training and test data, a comparative evaluation
can be made with the dynamic simulator.
(3)
Iv. NUMERICAL
RESULTS
In this work, the Terceira Island system (Azores Portugal) has been selected for testing the approach
developed, considering the large amount of wind power
production foreseen and the need for effective emergency
control measures. Its single line diagram is presented in
figure 1.
Here, Shed m a , is the maximum allowable load to be
shed, Ploss, is the amount of generation lost and BufJer, is
the percentage of extra load to be considered for
shedding. No load slzeddinr! is also an acceptable load
shedding scheme, in order to establish a base frequency
response for a given operating condition. such that
planners can see the benefit of shedding.
EPRI’s, Extended Transient-Mid-Term Stability Program
(ETMSP) was used to perform the dynamic simulation.
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Evaluation of NN performance
In order to get an idea for what kind of performance is to
be expected from a NN architecture, a preliminary
evaluation is needed of its capabilities. The “training set”
data, typically composed of % of the OPs from the overall
data set, is used to teach the NN and give a relative
inclination as to its suitability for that particular
application. The “test set,” which is comprised of the
remaining data, is used to evaluate the prediction
capabilities and generalization performance of the
structures.
Since the scope of this work is to take into consideration
the effects load shedding has on the power systems
dynamic performance, it is crucial that all potential load
shedding schemes are analyzed via a dynamic simulation
and included in the data set. Therefore for one given OP a
number of different potential load shedding schemes were
developed, depending on the amount of generation that
was lost. However, due to the overwhelming shedding
scheme potential a few additional steps were taken to
minimize the number of needed schemes. First, although
load shedding is usually performed at the feeder level, an
aggregation of feeder loads into one busload was
performed, using the load modeling procedure addressed
in [19]. Second, a shedding constraint to avoid scenarios
that cause too much load shedding was applied, as seen in
(3).
Shed max = Ploss
Design of NN
An island model allows for a compact, simple, and
precise system analysis. The following is a list of
assumption and device models u e d in the dynamic
simulation of Terceira network.
350
[I61 lEEE Task Force Report, ‘Toad representation for Dynamic
Perfonnmce.” IEEE Tran, on Power Systems, Vol. 7, No. 1,
Feb.1992.
A NN designed in this way is presently being used to
identify optimal shedding levels, using a Genetic
Algorithm approach. Results obtained from the
application of this philosophy are also quite promising.
VI.
[ 171 McCalley, J.D., et al.. “On-Line Visualization of Transmission
System Operating Constraints using Intelligent Infomtion
Processing.” Final report of contract No. 219-2-384-94 between
Pacific Gas & Electric and Iowa State University. May 1997.
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The authors would l i e to thank the Power System5 Unit
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BIOGRAPHIES
Matthew A. Mitchell is from Marion, Iowa, U.S.A. He
graduated with a B.S. in Electrical Engineering from Iowa
State University in 1998. He is currently enrolled in the
MSc program at the University of Porto and a researcher
of the Power Systems unit at INESC Porto, Portugal.
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JoHo A. P e p s Lopes was born in Portugal. He graduated
in Electrical Engineer from the Faculty of Engineering of
Porto University (FEUP) in July 1981. and obtained the
Ph.D. and Aggregation degrees also from FEUP in
October 1988 and November 1996 respectively. Dr. P e p s
Lopes is presently Associate Professor with Aggregation
at FEUP and Assistant Coordinator of the Power Systems
unit at INESC Porto, Portugal.
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Actuated Load Shedding and Restoration Piut I- Philosophy,”
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J.N. Fidalgo was born in Portugal. He graduated in
Electrical Engineer from the Faculty of Engineering of
Porto University (FEUP) in July 1985. and obtained the
Ph.D in 1995 also from FEUP. Presently he is an
Assistant Professor at FEUP and a Senior Researcher of
the Power Systems unit at INESC Porto, Portugal.
[I31 P y a s Lopes, J.A., et al.. “Genetic Algorithm for Planning
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System. in Large Islands with High Wind Power Penetration”.
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Conference, vol. 1. Trondheim - Norway, June 1999. pp. 331-
James D.McCalley is an Associate Professor of Electrical
and Computer Engineering Department at Iowa State
University, where he has been employed since 1992. He
worked for Pacific Gas and Electric Company from 1986
to 1990. Dr. McCalley received the B.S. (1982), M.S.
(1986), and Ph.D. (1992) degrees in Electrical
Engineering from Georgia Tech. He is a registered
professional engineer in California and a senior member
of the IEEE.
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[I51
Kottick, Daniel. Or Ofer. “Neural-Networks for Predicting the
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