ANN Based Power System Restoration
INTRODUCTION
The importance of electricity in our day to day life has reached such a
stage that it is very important to protect the power system equipments from
damage and to ensure maximum continuity of supply. But there are power
system blackouts by which the continuous power supply is being interrupted.
What is more important in the case of a blackout is the rapidity with which the
service is restored. Now- a -days power system blackouts are rare. But
whenever they occur, the effect on commerce, industry and everyday life of
general population can be quite severe. In order to reduce the social and
economic cost of power system blackouts, many of the electric utility
companies have pre-established guidelines and operating procedures to restore
the power system. They contain sequential restoration steps that an operator
should follow in order to restore the power system. They are based on certain
assumptions which may not be present in the actual case. This reduces the
success rates of these procedures.
This paper mainly focuses on:
The limitations encountered in some currently used PSR techniques.
A proposed improvement based on ANN.
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ANN Based Power System Restoration
WHAT ARE ANNs?
Artificial Neural Network (ANN) is a system loosely modeled on human
brain. It tries to obtain a performance similar to that of human’s performance
while solving problems. As a computational system it is made up of a large
number of simple and highly interconnected processing elements which process
information by its dynamic state response to external inputs. Computational
elements in ANN are non-linear and so the results come out through non-
linearity can be more accurate than other methods. These non-linear
computational elements will be working in unison to solve specific problems.
ANN is configured for specific applications such as data classification or pattern
recognition through a learning process. Learning involves adjustment of
synaptic connections that exist between neurons. ANN can be simulated within
specialized hardware or sophisticated software. ANNs are implemented as
software packages in computer or being used to incorporate Artificial
Intelligence in control systems.
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ANN Based Power System Restoration
BIOLOGICAL NEURON
The most basic element of the human brain is a specific type of cell,
which provides us with the abilities to remember, think, and apply previous
experiences to our every action. These cells are known as neurons, each of these
neurons can connect with up to 200000 other neurons. The power of brain
comes from the numbers of these basic components and the multiple
connections between them.
All natural neurons have four basic components, which are
dendrites, soma, axon and synapses. Basically, a biological neuron
receives inputs from other sources, combines them in some way, performs
a generally non-linear operation on the result, and then output the final
result. The figure below shows a simplified biological neuron and the
relationship of its four components.
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ANN Based Power System Restoration
ARTIFICIAL NEURON
The basic unit of neural networks, the artificial neurons, simulates the
four basic functions of natural neurons. Artificial neurons are much simpler than
the biological neurons. The figure below shows the basic structure of an
artificial neuron.
Note that various inputs to the network are represented by the
mathematical symbol, x(n). Each of these inputs are multiplied by a
connection weight, these weights are represented by w(n). In the simplest
case, these products are simply summed, fed through a transfer function to
generate a result, and then output. Even though all artificial neural networks
are constructed from this basic building blocks the fundamentals may
vary in these building blocks and there are differences.
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ANN Based Power System Restoration
NEURAL NETWORKS
Artificial neural networks emerged from the studies of how brain
performs. The human brain consists of many million of individual processing
elements called neurons that are highly interconnected.
ANNs are made up of simplified individual models of the biological
neurons that are connected together to form a network. Information is stored in
the network in the form of weights or different connection strengths associated
with the synapses in the artificial neuron models.
Many different types of neural networks are available and multilayered
neural network are the most popular which are extremely successful in pattern
reorganization problems. An artificial neuron is shown in the figure. Each
neuron input is weighted by wi. Changing the weights of an element will alter
the behavior of the whole network. The output y is obtained summing the
weighted inputs and passing the result through a non-linear activation function.
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ANN Based Power System Restoration
PROCEDURE FOR ANN SYSTEM DESIGN
In realistic application the design of ANNs is complex, usually an
iterative and interactive task. The developer must go through a period of trial
and error in the design decisions before coming up with a satisfactory design.
The design issues in neural network are complex and are the major concerns of
system developers.
Designing of a neural network consists of:
Arranging neurons in various layers.
Deciding the type of connection among neurons of different layers , as
well as among the neurons within a layer.
Deciding the way neurons receive input and produces output.
Determining the strength of connection that exists within the network by
allowing the neurons learn the appropriate values of connection weights
by using a training data set.
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ANN Based Power System Restoration
The process of designing a neural network is an iterative process.
The figure below describes its basic steps.
As the figure above shows, the neurons are grouped into layers. The input
layer consists of neurons that receive input from external environment. The
output layer consists of neurons that communicate the output of the system to
the user or external environment. There are usually a number of hidden layers
between these two layers. The figure above shows a simple structure with only
one hidden layer.
When the input layer receives the input , its neurons produces output,
which become input to the other layers of the system. The process continues
until certain condition is satisfied or until the output layer is invoked and fire
their output to the external environment.
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ANN Based Power System Restoration
FEATURES OF ANNs
ANNS have several attractive features:
Their ability to represent non-linear relations makes them well suited for
non-linear modeling in control systems.
Adaptation and learning in uncertain system through off line and on line
weight adaptation.
Parallel processing architecture allows fast processing for large-scale
dynamic system.
Neural network can handle large number of inputs and can have many
outputs.
ANNs can store knowledge in a distributed fashion and consequently have a
high fault tolerance.
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ANN Based Power System Restoration
LEARNING TECHNIQUES
An ANN can been seen as a union of simple processing units, based on
neurons that are linked to each other through connections similar to synapses.
These connections contain the “knowledge” of the network and the pattern of
connectivity express the objects represented in the network. The knowledge of
the network is acquired through a learning process where the connections
between processing elements is varied through weight changes.
Learning rules are algorithms for slowly alerting the connection weights
to achieve a desired goal such as minimization of an error function. Learning
algorithms used to train ANNs can be supervised or unsupervised. In supervised
learning algorithms, input/output pairs are furnished and the connection weights
are adjusted with respect to the error between the desired and obtained output.
In unsupervised learning algorithms, the ANN will map an input set in a state
space by automatically changing its weight connections. Supervised learning
algorithms are commonly used in engineering processes because they can
guarantee the output.
In this power system restoration scheme, a multilayered perceptron(MLP)
was used and trained with a supervised learning algorithm called back-
propagation. A MLP consists of several layers of processing units that compute
a nonlinear function of the internal product of the weighted input patterns.
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ANN Based Power System Restoration
These types of network can deal with nonlinear relations between the variables;
however, the existence of more than one layer makes the weight adjustment
process for problem solution difficult.
BACK PROPOGATION ALGORITHM
This method has proven highly successful in training of multilayered
neural networks. The network is not just given reinforcement for how it is doing
on a task. Information about errors is also filtered back through the system and
is used to adjust the connections between the layers, thus improving
performance of the network results. Back-propagation algorithm is a form of
supervised learning algorithm.
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ANN Based Power System Restoration
CONVENTIONAL RESTORATION TECHNIQUES
VARIOUS PRICIPLES USED:
Automated restoration : In this restoration technique, computer programs
are responsible for program development and implementation. The PSR
techniques based on this principle acquire data from the supervisory
control and data acquisition system (SCADA) and the energy
management system (EMS). Under a wide area disturbance, a PSR
program installed in the EMS system will use the acquired system to
develop a restoration plan for the transmission system. After developing
the restoration plan, a switching sequence program, which is also a part of
the EMS, will be responsible for the transmission of control signals
through SCADA to circuit breakers and switches to implement the plan.
In this technique, the system operator plays the role of supervisor.
Computer aided restoration : In this technique, the PSR plan development
and implementation is performed by the system operator. The PSR
technique that uses this principle also acquire system data from the
system local SCADA/EMS. Following a wide area disturbance, the
system operator uses power system data provided by the SCADA/EMS to
develop a PSR plan. The system operator can use the PSR procedure and
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ANN Based Power System Restoration
power system analysis programs as aid to develop restoration plans. The
system operator will also use the local SCADA/EMS to transmit control
commands to circuit breakers and switches in order to implement the
chosen PSR plan.
Cooperative restoration : In this technique, a computer program installed
at the EMS will propose a PSR plan after the occurrence of the blackout.
The system operator is responsible for the implementation of PSR plan.
The PSR systems that apply this technique also use power system data
obtained from local SCADA/EMS. When the power system is under
going a wide area disturbance, the PSR program installed in the EMS will
use the system data to develop a restoration plan. With this restoration
plan, the system operator can send controlling signals through local
SCADA/EMS to circuit breakers and switches to implement the plan.
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ANN Based Power System Restoration
PROPOSED ANN BASED RESTORATION SCHEME
The proposed restoration scheme is composed of several Island
Restoration Schemes(IRS). Each IRS is responsible for the development of an
island restoration plan when the power system is recovering from a wide-area
disturbance. The number of IRSs will be defined by off-line studies and will
depend on regional load-generation balance. The division of the system into
islands is a common action in large transmission systems where parallel
restoration is more efficient and desired. The parallel restoration technique is
commonly used in the restoration schemes applied to large transmission
systems. This technique is also used in the proposed restoration scheme. The
“all-open” switching strategy where all circuit breakers of the system are open
will be used to create the islands. In order to restore a power system following a
wide-area disturbance, each IRS of restoration scheme will generate local
restoration plans composed of switching sequences of local circuit breakers and
a forecast restoration load.
Each IRS is composed of two ANNs and a switching sequence program
(SSP). The first ANN of each IRS is responsible for an island restoration load
forecast. The input of this ANN will be a normalized vector composed of the
pre-disturbance load. The second ANN of each IRS is responsible for the
determination of the final island configuration and the associated forecast
restoration load pick up percentage that will generate a feasible operational
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ANN Based Power System Restoration
condition. The input of this ANN will be a normalized vector composed of the
forecast island restoration load provided by the first ANN of the respective IRS,
three elements describing possible unavailable transmission paths(because of
outages) for use in the restoration plan. The final element of each IRS is the
SSP. The SSP will determine the energizing sequence of transmission paths that
will lead to the final configuration chosen by the second ANN. The SSP input
vector is composed of the final restoration island configuration generated by the
second ANN of the IRS and an energizing sequence database. The energizing
sequence database of each IRS is composed of transmission path sequences
connecting island generators to island loads. The following figure illustrates the
functional block diagram of an IRS.
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ANN Based Power System Restoration
The proposed restoration scheme will present a restoration plan to the
EMS operator following the occurrence of a wide area disturbance. The power
system operator must apply the all open switch strategy through the
EMS/SCADA or through regional control centers before the plan is
implemented. The restoration plan provided by the proposed scheme will be
composed of energizing sequences and restoration load percentage pick up
values for all islands. As the final step of the total restoration, the closing of the
tie-lines will be the responsibility of the system operator. The tie-lines should be
closed when all the islands are restored and are in steady state.
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ANN Based Power System Restoration
RESTORATION CONSTRAINTS
In order to generate a feasible restoration plan to be used as a training
pattern by the IRSs, certain operational constraints must be considered.
The various constraints considered are:
Thermal limits of transmission lines
Stability limits
Number of lines used in the restoration plan
Allowable over and under voltage
Recognition of locked –out circuit breakers
The thermal rating of the normally designed transmission lines depends
mainly on the voltage level at which they operate, the line length and reactance.
Power system stability is a subject of major concern in PSR. The restored
system generated by the PSR scheme has to be able to allow for sufficiently
large load and generation variations without encountering undesirable and
uncontrollable behavior that could lead to instability and a recurrence of the
system blackout. In order to check the stability of the restored power system,
transient stability studies must be conducted.
The number of transmission lines used in the restoration plan also needs
some consideration. The number of transmission lines used in the PSR plan is
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ANN Based Power System Restoration
very important. Transmissions play a critical role in reactive power balance and
over voltage control during the restoration implementation. In order to maintain
a normal voltage profile and avoid the generation of excessive reactive power, it
is advisable to energize the smallest possible number of transmission lines in a
proper sequence during the restoration process.
Circuit breakers have the capability to go through a certain number of
open-close sequences when automatic enclosing is enabled. Once the available
number of open-close sequences is exhausted, the circuit breaker goes into a
lock-out state. Permanent non recoverable equipment faults may also lead to
circuit breaker lock-outs. A locked out circuit breaker will normally require
manual resetting before it can be made available for normal operations. Clearly,
the locked-out circuit breakers cannot be used for automatic restoration and
should be taken into account by the PSR scheme.
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ANN Based Power System Restoration
CONCLUSION
PSR has become a field of growing interest. Several techniques based on
artificial intelligence have been proposed to improve power system restoration.
These techniques propose the use of the computer as an operator aid instead of
the use of predefined operating procedures for restoration. The stressful
condition following a blackout and the pressure for achieving a restoration plan
in minimum time can lead to misjudgment by system operator. This paper
proposes the use of ANN for service restoration plan, since it has generalization
capability and high processing speed. The large number of possible faulty
conditions and the need to provide a restoration plan in minimum time are
arguments in favor of this technique.
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ANN Based Power System Restoration
REFERENCES
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 18,
NO. 4, OCTOBER 2003
“NEURAL NETWORKS” – CONTROL SYSTEMS
ENGINEERING (THRID EDITION)
BY I.J.NAGRATH & M.GOPAL
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ANN Based Power System Restoration
ABSTRACT
Power System Restoration (PSR) has been a subject of study for many
years. In recent years many techniques were proposed to solve the limitations of
predetermined restoration guidelines and procedures used by a majority of
system operators to restore a system following the occurrence of a wide area
disturbance. This paper discusses limitations encountered in some currently
used PSR techniques and a proposed improvement based on Artificial Neural
Networks (ANNs). This proposed scheme has been tested on a 162-bus
transmission system and compared with a breadth search transmission system.
The results indicate that, this is a feasible option that should be considered for
real time applications.
Artificial Neural Networks (ANNs) are computational techniques that try
to obtain a performance similar to that of human’s performance when solving
problems. The building block of ANN is Artificial Neuron, which has got
structural & functional similarities with biological neurons. ANN is also an
efficient alternative for problem solutions where it is possible to obtain data
describing the problem behavior, but a mathematical description of the process
is impossible. The proposed restoration scheme is composed of several Island
Restoration Schemes (IRS). Each IRS is responsible for the development of an
Island Restoration Plan when the power system is recovering from a wide area
disturbance.
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ANN Based Power System Restoration
CONTENTS
1. INTRODUCTION 1
2. WHAT ARE ANNS? 2
3. BIOLOGICAL NEURON 3
4. ARTIFICIAL NEURON 4
5. NEURAL NETWORKS 5
6. PROCEDURE FOR ANN SYSTEM DESIGN 6
7. FEATURES OF ANN 8
8. LEARNING TECHNIQUES 9
9. CONVENTIONAL RESTORATION TECHNIQUES 11
10. PROPOSED ANN BASED RESTORATION SCHEME 13
11. RESTORATION CONSTRAINTS 16
12. CONCLUSION 18
13. REFERENCES 19
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ANN Based Power System Restoration
ACKNOWLEDGEMENT
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