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
More Papers and Presentations available on above site
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
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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
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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]