Artificial Neural Network Based Power System Restoratoin

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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ACKNOWLEDGEMENT

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Artificial Neural Network Based Power System Restoratoin

Documents

island restoration schemes

switching sequence program

energizing sequence database

wide area disturbance

psr program installed

artificial neural networks

power system blackouts

island restoration plan