artificial intelligence and neural network applications in power systems

8/7/2019 Artificial Intelligence and Neural Network Applications in Power Systems

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ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK

APPLICATIONS IN POWER SYSTEMS

Document BySANTOSH BHARADWAJ REDDY

Email: [email protected]

Engineeringpapers.blogspot.com

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ABSTRACT:

The electric power industry is

currently undergoing an

unprecedented reform, ascribable to,

one of the most exciting and

potentially profitable recent

developments in increasing usage of

artificial intelligence techniques. The

artificial neural network approach has

attracted number of applications

especially in the field of power

system since it is a model free

estimator. Neural networks provide

solutions to very complex and

nonlinear problems. Nonlinear

problems, like load forecasting that

cannot be solved with standardalgorithms but can be solved with a

neural network with remarkable

accuracy. Modern interconnected

power systems often consist of

thousands of pieces of equipment

each of which may have an effect on

the security of the system. Neural

networks have shown great promise

for their ability to quickly and

accurately predict the system

security when trained with data

collected from a small subset of

system variables.

The intention of this paper is to

give an overview of application of

artificial intelligence and neural

network (NN) techniques in power

systems to prognosticate load on

power plant and contingency in case

of any unexpected outage. In this

paper we present the key concepts of

artificial neural networks, its history,imitation of brain neurons

architecture and finally the

applications (load forecasting and

contingency analysis). The

applications of artificial intelligence
mailto:[email protected]:[email protected]


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in areas of load forecasting by error

Backpropagation learning algorithm

and contingency analysis based on

Quality index have been

perspicuously explained.

INTRODUCTION:

Modern power systems are required

to generate and supply high quality

electric energy to customers. To

achieve this requirement, computers

have been applied to power system

planning, monitoring and control.

Power system application programs

for analysing system behaviours are

stored in computers. In the planning

stage of a power system, system

analysis programs are executed

repeatedly. Engineers adjust and

modify the input data to these

programs according to their

experience and heuristic knowledge

about the system until satisfactory

plans are determined.

For sophisticated approaches

to system planning, development of

methodologies and techniques to

incorporate practical knowledge of

planning engineers into programs

which also include the numerical

analysis programs are needed. In the

area of power System monitoring and

control, computer based Energy

Management Systems are now

widely used in energy control

centers. The abnormal modes of

system operation may be caused by

network faults, active and reactive

power imbalances, or frequency

deviations. An unplanned Operation

may lead to a mal or a complete

system blackout. Under these

emergency situations, power

systems are restored back to the

normal state according to decisions

made by experienced operation

engineers. There is also a need to

develop fast and efficient methods

for the prediction of abnormal

system behaviour.

Artificial intelligence (AI)

has provided techniques for

encoding and reasoning with

declarative knowledge. The advent

of neural networks (NN"s), in

addition, provides neural network

modules which can be executed in

an online environment. These new

techniques supplement conventional

computing techniques and methods

for solving problems of power

system planning, operation and

control.


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Areas of Applications:

Possible applications of artificial

intelligence in power system planning

and operation were investigated by

power titles and researchers. In the

last decade, many artificial

intelligence systems and expert

systems have been built for solving

problems in different areas within the

field of power systems only. These

areas are summarized below.

System planning

Transmission planning and design,

Generation expansion, Distribution

planning.

System Analysis

Loadflow engine, Transient stability

System Operation h Monitoring

Alarm processing, Fault diagnosis,

Substation monitoring, System and

network restoration, Load shedding,

Voltage / reactive power control,

Contingency selection, Network

switching, Voltage collapse.

Operational Planning

Unit commitment, Maintenance

scheduling, Load forecasting.

1. Artificial Neural Networks

1.1 What is a Neural Network?

An

Artificial

Neural

Network

(ANN) is an

information

processing paradigm that is inspired

by the way biological nervous

systems, such as the brain, process

information. The key element of this

paradigm is the novel structure of

the information processing system.

It is composed of a large number of

highly interconnected processing

elements (neurons) working in

unison to solve specific problems.

ANNs, like people, learn by

example. An ANN is configured for

a specific application, such as

pattern recognition or data

classification, through a learning

process. Learning in biological

systems involves adjustments to the

synaptic connections that exist


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between the neurons. This is true of

ANNs as well.

The major breakthrough in the

field of ANN occurred with the

invention of Backpropagation

algorithm which enabled design and

learning techniques of multilayered

neural networks. Since then the

development and areas of application

in which ANN is applied has been

thriving.

1.3 Biological inspiration:

The brain is principally composed

of a very large number (circa

10,000,000,000) of neurons,

massively interconnected (with an

average of several thousand

interconnects per neuron, although

this varies enormously). Each neuron

is a specialized cell which can

propagate an electrochemical signal.

The neuron has a branching input

structure (the dendrites), a cell body,

and a branching output structure (the

axon). The axons of one cell connect

to the dendrites of another via a

synapse. When a neuron is activated,

it fires an electrochemical signal

along the axon. This signal crosses

the synapses to other neurons, which

may in turn fire. A neuron fires only

if the total signal received at the cell

body from the dendrites exceeds a

certain level (the firing threshold).

To capture the essence of biological

neural systems, an artificial neuron

is defined as follows:

It receives a number of inputs

(either from original data, or from

the output of other neurons in the

neural network). Each input comes

via a connection that has a strength

(or weight); these weights

correspond to synaptic efficacy in a

biological neuron. Each neuron also

has a single threshold value. The

weighted sum of the inputs is

formed, and the threshold

subtracted, to compose the
http://www.statsoft.com/textbook/glosn.html#Neuronhttp://www.statsoft.com/textbook/glosn.html#Neuronhttp://www.statsoft.com/textbook/glosn.html#Neuronhttp://www.statsoft.com/textbook/glosn.html#Neuronhttp://www.statsoft.com/textbook/glosn.html#Neuron


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activation of the neuron (also known

as the post-synaptic potential, or PSP,

of the neuron).

The activation signal is passed

through an activation function

(also known as a transfer

function) to produce the output of

the neuron.

If a network is to be of any use, there

must be inputs (which carry the

values of variables of interest in the

outside world) and outputs (which

form predictions, or control signals).

The input, hidden and output neurons

need to be connected together.

1.4 Neural networks versus

conventional computers

Neural networks take a different

approach to problem solving than that

of conventional computers.

Conventional computers use an

algorithmic approach i.e. the

computer follows a set of

instructions in order to solve a

problem.

Neural networks process

information in a similar way the

human brain does. Neural networks

learn by example. They cannot be

programmed to perform a specific

task. On the other hand,

conventional computers use acognitive approach to problem

solving; the way the problem is to

solved must be known and stated in

small unambiguous instructions.

These instructions are then

converted to a high level language

program and then into machine code

that the computer can understand.

Neural networks and conventional

algorithmic computers are not in

competition but complement each

other. Even more, a large number of

tasks, require systems that use a

combination of the two approaches

(normally a conventional computer

is used to supervise the neural

network) in order to perform at

maximum efficiency.


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1.5 Features

1. High computational rates

due to the massive parallelism.

2. Fault tolerance.

3. Training the network

adopts itself, based on the

information received from the

environment.

4. Programmed rules are

not necessary.

5. Primitive computational

elements.

1.6 The Learning Process

Supervised learning which

incorporates an external teacher, so

that each output unit is told what its

desired response to input signals

ought to be. During the learning

process global information may be

required.

Unsupervised learning uses no

external teacher and is based upon

only local information. It is also

referred to as self-organisation

1.7 Transfer Function

The behaviour of an ANN depends on

both the weights and the input-output

function (transfer function) that is

specified for the units. This function

typically falls into one of three

categories:

Linear (or ramp)

Threshold

Sigmoid

For linear units, the output activity

is proportional to the total weightedoutput.

Forthreshold unit, the output is set

at one of two levels, depending on

whether the total input is greater

than or less than some threshold

value.

Forsigmoid units, the output varies

continuously but not linearly as the

input changes. Sigmoid units bear a

greater resemblance to real neurons

than do linear or threshold units, but

all three must be considered rough

approximations.

2. APPLICATIONS

App1: Power Systems Load

Forecasting


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Commonly and popular problem that

has an important role in economic,

financial, development, expansion

and planning is load forecasting of

power systems. Generally most of the

papers and projects in this area are

categorized into three groups:

Short-term load forecasting

(STLF) over an interval

ranging from an hour to a

week is important for various

applications such as unit

commitment, economic

dispatch, energy transfer

scheduling and real time

control. A lot of studies have

been done for using of short-

term load forecasting with

different methods. One of

these methods may be

classified as follow:

Regression model, Kalman filtering,

Box & Jenkins model, Expert

systems, Fuzzy inference, Neuro

fuzzy models and Chaos time series

analysis.

Some of these methods have

main limitations such as neglecting of

some forecasting attribute condition,

difficulty to find functional

relationship between all attribute

variable and instantaneous load

demand, difficulty to upgrade the set

of rules that govern at expert system

and is ability to adjust themselves

with rapid nonlinear system-load

changes. The NNs can be used to

solve these problems. Most of the

projects using NNs have considered

many actors such as weather

condition, holidays, weekends and

special sport matches days in

forecasting model, successfully.

This is because of learning ability of

NNs with many input factors.

Mid-term load

forecasting(MTLF) that

range from one month to five

years, used to purchase

enough fuel for power plants

after electricity tariffs are

calculated.

Long-term load forecasting

(LTLF), covering from 5 to

20 years or more, used by

planning engineers and

economists to determine the

type and the size of

generating plants that


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minimize both fixed and

variable costs.

2.1.1 Overview of STLF

Techniques:-

A wide variety of

techniques/algorithms for STLF have

been reported in the literature (These

procedures typically make use of two

basic models peak load models and

load shape modes.

Standard Load Concept :- (Load

Shape Model)

The load forecasting is divided into

two general parts; peak load model

and load shape model .Former deals

with daily or weekly peak load

modeling & later describes load a

discrete time series over forecasting

intervals.

Standard Load :-The

standard load curve is

produced once a day . It

needs rescaling over time .

The standard load

characterizes the base load .

It is calculated by using

historical load data .The

standard load calculation

can be divided in two

parts. The first one makes

an average using all

common days in the same

period. The holidays are included

with Saturdays and Mondays. The

second part investigates on the

particular characteristic for each day

of the week, separately. For this a

simple weighted moving average is

made.

Residual/Deviation Load:-The

residual load is used to represent the

most recent variation of the load .

This value contains information for

last 3 hours. Auto regressive and

exponential smoothing are the most

common methods used to calculate

the deviation of load value.


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2.1.2 Artificial Neural network

based short term load forecasting:

The development of an ANN

based STLF model is divided into two

processes, the "learning phase" and

the "recall phase". In learning phase,

the neurons are trained using

historical input &output data and

adjustable weights are gradually

optimized to minimize the difference

between the computed and desired

output. The ANN allows outputs to be

calculated based on some form of

experiences, rather than

understanding the connection between

input and output (or cause and effect).

In recall phase the new input data is

applied to the network & and its

outputs are computed and evaluated

for testing purpose. In the ANN based

STLF model, a layered ANN

structure (Input layer, Hidden layer,

Output layer) is used. In this method

the weights are calculated by a

learning process using error

propagation in parallel distributed

processing. The STLF problem is

formulated with the past data as the

input data and the latest data are the

desired output for training the

network.

An initial input data set is

presented to ANNSTLF which

adjusts the weight values for a

minimum error. Following a new

input data set is presented and the

weight values are adjusted in

accordance .The process finishes

when the difference between target

output and the found output for all

the input sets is close to zero. The

feed forward Multilayer Perceptron

(MLP) neural network model is used

for implementing the STLF model

(ANNSTLF). Fig5 shows a MLP

with single hidden 1ayer.Tlie

advantage of this model is that it is

able to learn highly non-linear

mappings. The MLP model is

trained by standard backpropagation

training algorithm and developed by

Rumelhart.

2.1.3 Multilayer Perceptron and


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Its application in load forecasting:-

The multi layer Perceptron and the

associated backpropagation algorithm

proposed a sound method to train

networks having more than two layers

of neurons. The learning rule is

known as Backpropagation which is a

gradient decent technique with

backward error(gradient) propagation

is depicted in Fig.6. The back

propagation network in essence learns

a mapping from a set of input patterns

(e.g. extracted features) to, a set of

output patterns ( e.g. class

information ) .This network can be

designed and trained to accomplish a

wide variety of mappings. This ability

comes from the nodes in hidden layer

or layer of the network which learns

to respond to features found in input

pattern. The features recognized or

extracted by the hidden units ( nodes)

correspond to the correlation of

activity among different input units.

As the network is trained with

different examples, the network has

the ability to generalize over similar

features found in different patterns.

The hidden unit (nodes)must be

trained to extract a sufficient set of

general features applicable to

instances ; which is achieved , thus

avoiding overloading of network ,

by terminating Learning once a

performance pattern has been

reached . The backpropagalion

network is capable of approximating

arbitrary mappings given a net of the

output.

The name backpropagation comes

from the fact that the error (gradient)

of hidden units are derived from

propagating backward the errors

associated with the output Units

since the target values for hidden

units are not given or it is defined to

obtain the values of the desired

output at hidden layer.

Error Backpropagation:-

The back propagation (or backup)

algorithm is a generalization of the


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Widrow Hoff error correction rule. In

the Widrow-Hoff technique an error

which is the difference between what

the output is and what it is supposed

to be is formed and the synaptic

strength is changed in proportion to

error times the input signal in a

direction which reduces the error. The

direction of change in weights is such

that the error will reduce in the

direction of the gradient (the direction

of most rapid change of the error).

This type of learning is also called

gradient search. In the case of

multilayer networks, the problem is

much more difficult.

Choice of activation function

The most common activation

function used in multilayer perceptron

is the sigmoid. The equation of the

sigmoid function is

The back propagation algorithm for

network using the sigmoid 88

activation function is described below

.The equation of sigmoid function is

Written as

2.1.4 The Application of ANN to

STLF & Results:-

The ANNSTLF implements

multilayer feed forward neural

network which was trained by using

backpropagation training algorithm.

Naturally 24 hours data points leads

to 24 input nodes in MLP model.

Here 2 hidden layers are considered.

MSEB data for the period Oct 94 to

June 95 i.e of 35 weeks for

development and implantation of the

software was utilized.


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The Backpropagation

algorithm with MLP model of

Artificial Neural Network (A") is

developed for the problem of short

Term Load Forecasting (STLF) with

a lead time of at least 24 hours. The

best performance was obtained for the

load forecasting for the Tuesday

which gives the maximum and

average percentage error of 2.00%

and 0.20% respectively. This comes

very close to the precision obtained

by the human forecaster. The turningof the gain, momentum terms

selection of the weights and threshold

values play key role in convergence

of the network. High values of the

weights lead to the divergence and

generally small values of the order of

10-2 the yield better results.

App2: Power System Contingency

Analysis

Contingency analysis and risk

assessment are important tasks for the

safe operation of electrical energy

networks. During the steady state

study of an electrical network any

one of the possible contingencies

can have either no effect, or serious

effect, or even fatal results for the

network safety, depending on a

given network operating state.

Load flow analysis can be used

as a crisp technique for contingency

risk assessment. However

performing at run time the necessary

load flow analysis studies is a

tedious and time consuming

operation. An alternative solution is

the off-line training and the run-time

application of artificial neural

networks. This article aims at

describing how artificial neural

networks can be used to bypass the

traditional load flow cycle, resulting

in significantly faster computation

times for online contingency

analysis. A discussion over the

efficiency of the proposed

techniques is also included.

2.2.1 What is contingency in

power system?

system contingency is defined

as a disturbance that can occur in the

network and can result in possible


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loss of parts of the network like

buses, lines, transformers, or power

units in any of the network areas.

Load flow analysis is an adequate

ans for studying the effect of a

possible contingency on a given

operating point of the network. It is

often the case that experienced

engineers, involved in operation of a

given system, can guess effectively

contingency without the support of

numerical computations. This

intuition of the operators is useful in

supporting the initial selection of a

list of possible contingencies, which

then will be analysed using the

described here technique.

2.2.2. System Architecture

A suitable way of studying the effects

of contingencies on an electrical

network is through the definition of

representative operating points,

creation of a relevant data base, in

which parameters relating to these

operating points is stored as these

have been measured directly through

network snapshots. Once a number of

operating points is simulated, a list of

contingencies to be studied upon is

formed. Each contingency is applied

on all operating points found in the

database and then a power flow

solution is attempted on the

network. According to the results of

the power flow solution the

contingency applied of the specific

operating point can be ranked as

innocent, violating, or

diverging / serious. The pre-

contingency operating point

parameters, various operating point

indices and metrics, the contingency

and the power flow result are next

stored in a table per contingency.

This contingency table constitutes a

set of features and tuples that can be

considered as suitable neural

network input layer data elements if

selected in any combination and

after being statistically normalized.

The power flow solution classifying

any contingency for any operating

point, is the output layer value of the

neural network.

Neural network training is a

computer intensive work that needs,

however, to be done only once. As

soon as the neural network is trained

for a contingency, the predictions

about the effects of a contingency on


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any operating point can easily be

deduced. The efficiency of the

predictions depends on various

factors such as the quality and the

quantity of the training features, the

type, complexity and connectivity of

the neural network.

2.2.4 Neural Network Input

Feature Selection

A wide range of electrical network

parameters can be used for describing

the network state. Some of them can

be the network load level expressed

as a percentage of the maximal

network load, the number of lines, the

cumulative rating of all lines, the

cumulative active load, active

generation, reactive load, reactive

generation, apparent power etc. In

recent bibliography there are

references in more elaborate

aggregates that yield better results

when applied, such as the active

apparent power margin index

(expressed as the fraction of the

flowing aggregate apparent power,

over the aggregate MVA line

transmission limits) and the voltage

stability index. The voltage stability

index is computed as the

sensitivities of the total reactive

power generation to a reactive

power consumption, known as

reactive power dispatch

coefficients .

Neural networks can be

trained with any number of input

features. The neural network

training process can selectively

overweight the most salient features

and underweight the least significant

ones. However, the selection

procedure is time consuming for the

training of the neural network, while

after the training is complete, it is

not always obvious which of the


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input nodes are of greater importance.

Further more, the least important

input layer nodes may add noise to

the neural network training process.

Bearing this in mind, a pre-selection

of the neural network input nodes is

of great use. This can be achieved

through the use of statistical methods.

The statistical methods that apply in

the procedure of the selection of

features are used in the classification

theory. The classification of a set of

training examples by two features in

two classes is considered to be better

when the sub-populations look

different. the simplest test proposed is

the test of separating two classes

using just the means. A feature

selection test from Means and

Variances is also proposed:

A and B are of the same feature

measured for the classes 1 and 2 n1

and n2 are the corresponding numberof cases sig is a significance level. In

[4] the following measure for filtering

features separating two classes is also

proposed:

where M1 and M2 are the vectors of

feature means for class 1 and class

2, C1 -1 and C2 -1 are the inverse ofthe covariance matrix for class 1 and

class 2 respectively. For reasons of

simplicity, a combination of bus and

line losses only has been considered

as a constituent element of a

contingency under study. The four

most salient features found were the

aggregate reactive power generation,

the voltage stability index, the

aggregate MVA power flow and the

real power margin index. This set of

selected features has been used for

the training and testing of the neural

networks subsequently built.

2.2.5 Quality index

The quality index is a qualitative

measure of the classification power

of the neural network. It is an index

that has been calculated for all

simulations and applies on the idea

that within the three classes ofcontingency states, the major

difference can be considered to

occur between two possible

categories of contingencies:

innocent and non-innocent


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contingencies. In order to compute

this quality index (QA) the

following formula has been used:

where ai,j is the I-th element of the j-

th column of the confusion matrix

Ai,j. The confusion matrix Ai,j is a

matrix of frequencies. For each

element of the matrix ai,j the i index

refers to predicted values, while the j

index refers to real values. The values

range from one to three denoting the

three possible contingency cases: one

in case of nonconvergence /

potentially serious contingency, two

in case of MVA and voltage

violations and three in case of an

innocent contingency.

This technique is involves a

tedious training phase, where a set of

neural networks is created,

corresponding to a given set of

possible contingencies. The resulting

set of ANNs demonstrate satisfactory predictive power in classifying the

contingencies correctly at run time.

The run time performance of the

system is very good in terms of

computational time and recourses

requirements.

2.5.6 Result

Seven ANNs have been trained for

predicting the severity of

contingencies for the network of the

island of Crete, the testing set

performance of these ANNs was in

the range of 57% to 96%, this

performance did not seem to be

affected by the split between the

training and testing cases, as for

both a 70-30 and a 85-15 split the

results were simillar.

while sensitivity analysis in terms of

the ANN architecture demonstrated

that the number of hidden nodes

seem to have a serious effect on the

performance of the network,

suggesting use of more complex

ANNs.


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The promising results of this study

suggest application of similar

techniques in other areas of security

assessment of power systems and

other industrial processes.

4. BIBILIOGRAPHY:

Neural networks

1.PatrickK. Simpson, Artificial

Neural Systems, Pergamon Press,

Elmsford, N. Y., 1990.

2.Special Issue on Neural Networks

I: Theory and Modeling.

Proceedings of the IEEE, September

1990.

Load forecasting

3.Jacques de Villiers and Etienne

Barnard, Backpropagation Neural

Nets with One and Two Hidden

Layers, IEEE Transactions on

Neural Netcuorks, Volume 4, January

1993, pages 13G144.

4.Special Issue on Neural Networks

11: Analysis, Techniques, and

Applications, Proceedings of the

IEEE, October 1990.

D.C. Park, et al., Electric Load

Forecasting Using an artificial

Neural Network, IEEE

Transactions on Power Systems,

Volume 6, Number 2,May 1991,

pages 442-449.

5.Load Forecasting by ANN-IEEE

computer applications in power

systems. Duane D. Highley" and

Theodore J. Hilmes "

Contingency analysis:

6.Mitchell T. M., Machine Learning,

McGraw-Hill

Series in Computer Science, 1997,

p.81

7 Grainger J. J., W D. Stevenson,

Jr., Power System

Analysis, McGraw-Hill, 1994, chap.

9

8 Wehenkel L.A., Automatic

Learning Techniques in

Power Systems, Kluwer Academic

Publ., 1998, (p.

210)


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3. Keywords:

Artificial neural networks, contingency analysis, load forecasting, applications

of ANN in power system, Artificial intelligence training and testing.

Document By

SANTOSH BHARADWAJ REDDY

Email: [email protected]

More Papers and Presentations available on above site
mailto:[email protected]:[email protected]

artificial intelligence and neural network applications in power systems

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