material

1. ANN APPLICATIONS TO ELECTRICAL
SYSTEMS

2. ANN APPROACH TO ELECTRICAL LOAD
FORECASTING PROBLEM
 Load forecasting is a very essential tool for an electrical utility to form
necessary choices together with choices on the purchase, also for banking of power
(with alternative corporations or identical state utilities or with the neighboring
states), in the generation of power, in load change and development in
infrastructure.
 It is very necessary for energy suppliers as well as for other alternative
participants within the electrical energy transmission, generation, distribution, and
markets.
 Load forecasting is also beneficial for an electric utility in creating necessary
selections on generating, interchanging, and buying wattage, load change.
 Besides this, it is important for suppliers’ utility, many establishments and
others concerned with the electric energy generation and regulation.

3.  Load forecasting strategies can be classified into four parts i.e. very short,
short, medium and long-term models in accordance with the time duration.
 In very-short-term forecasting, the time span may be short a few minutes
whereas in long-term forecasting it may start with few years and can extend up to
few decades.
 In power system planning, generation and transmission, operation and control
the load forecasting plays a crucial part.
 Forecasting suggests that calculating active load at numerous load buses
previous actual load prevalence.
 Application of load forecasting in planning and operation needs an exact
‘lead time’ additionally known as forecasting intervals.

5.  There are mainly three types of categorization for load: short-term forecasting
generally done from few hours to one week, medium-term forecasting is done from
few weeks to a year, and long-term forecasting is done for more than one year.
 In an organization, it is necessary to forecast at various time horizon for
various operations.
 These forecasts are distinct in nature. Most of the strategies employ statistical
methods or artificial intelligence algorithms like fuzzy logic, regression, expert
system and neural networks.
 From medium and long-term load forecasting, end-use econometric technique
is widely used.
 For STLF, various strategies are employed such as fuzzy logic, different
regression models, statistical learning techniques, time series, expert systems and
similar day methods.
 There are a variety of techniques that can be used in the STLF such as fuzzy
logic, regression model, neural network, statistical learning algorithm and time
series.

6.  Statistical approaches completely require the mathematical model that can
represent a dependency of load on various factors such as time, weather and
customer.
 The mathematical model further sub-divided into two categories including
additive model as well as a multiplicative model.
 These models further differ in various terms on the basis of load forecasted
load which is considered as the multiplicative nature of various factors or
addictive nature of that factor too.

10. ANN APPROACH TO SYSTEM IDENTIFICATION
 System Identification is an essential requirement in areas such as control,
communication, power system and instrumentation for obtaining a model of a
system (plant) of interest or a new system to be developed.
 For the purpose of development of control law, analysis fault diagnosis, etc.
 Major advances have been made in adaptive identification and control, in past
few decades for identifying linear time-invariant plants with unknown parameters.
 The choice of the identifier structure is based on well established results in
linear systems theory.
 Stable adaptive laws for the adjustment of parameters in these which assures
the global stability of the relevant overall systems are also based on properties of
linear systems as well as stability results that are well known for such systems.
 System identification concerns with the determination of a system, on the
basis of input output data samples.

11.  The identification task is to determine a suitable estimate of finite dimensional
parameters which completely characterize the plant.
 The selection of the estimate is based on comparison between the actual
output sample and a predicted value on the basis of input data up to that instant.
 An adaptive automaton is a system whose structure is alterable or adjustable in
such a way that its behavior or performance improves through contact with its
environment.
 Depending upon input-output relation, the identification of systems can have
two groups.

12. A. Static System Identification:
 In this type of identification the output at any instant depends upon the input at
that instant.
 These systems are described by the algebraic equations.
 The system is essentially a memory less one and mathematically it is
represented as y(n) = f [x(n)] where y(n) is the output at the n th instant
corresponding to the input x(n).
B. Dynamic System Identification:
 In this type of identification the output at any instant depends upon the input at
that instant as well as the past inputs and outputs.
 Dynamic systems are described by the difference or differential equations.
 These systems have memory to store past values and mathematically
represented as y(n) = f [x(n), x(n-1), x(n-2)………..y(n-1), y(n-2),……] where
y(n) is the output at the nth instant corresponding to the input x(n).

14.  A system identification structure is shown in Figure.
 The model is placed parallel to the nonlinear plant and same input is given to
the plant as well as the model.
 The impulse response of the linear segment of the plant is represented by h(n)
which is followed by nonlinearity(NL) associated with it.
 White Gaussian noise q(n) is added with nonlinear output accounts for
measurement noise.
 The desired output d(n) is compared with the estimated output y(n) of the
identifier to generate the error e(n) which is used by some adaptive algorithm for
updating the weights of the model.
 The training of the filter weights is continued until the error becomes minimum
and does not decrease further.
 At this stage the correlation between input signal and error signal is minimum.

15.  Then the training is stopped and the weights are stored for testing.
 For testing purpose new samples are passed through both the plant and the model
and their responses are compared.
 Nonlinear system identification of a complex dynamic plant has potential
applications in many areas such as control, communication, power system,
instrumentation, pattern recognition and classification.
 Because of the function approximation properties and learning capability,
Artificial Neural Networks (ANN’s) have become a powerful tool for these
complex applications.
 The ANN’s are capable of generating complex mapping between the input and
the output space and thus, arbitrarily complex nonlinear decision boundaries can be
formed by these networks.
 An artificial neural network basically consists a number of computing elements,
called neurons that perform the weighted sum of the input signal and the connecting
weight.

16.  The sum is added with the bias or threshold and the resultant signal is then
passed through a non-linear element such as tanh(.) type.
 Each neuron is associated with three parameters on whose learning of neuron
can be adjusted; these are the connecting weights, the bias and the slope of the
non-linear function.
 From the structural point of view, a neural network (NN) may be single layer
or it may be multi-layer.
 In multi-layer structure, there may be more than one hidden layers and there is
one or many artificial neurons in each layer and for a practical case there may be
a number of layers.
 Each neuron of one layer is connected to each and every neuron of the next
layer.
 A neural network is a massively parallel distributed processor made up of
simple processing unit, which has a natural property for storing experimental
knowledge and making it available for use.

17.  It resembles the brain in two types
1. Knowledge is acquired by the network from its environment through a
learning process.
2. Interneuron connection strengths, known as synaptic weights, are used
to store the acquired knowledge.
 Artificial Neural Networks (ANN) has emerged as a powerful learning technique
to perform complex tasks in highly nonlinear dynamic environments.
 Some of the prime advantages of using ANN models are their ability to learn
based on optimization of an appropriate error function and their excellent
performance for approximation of nonlinear function.
 At present, most of the work on system identification using neural networks is
based on multilayer feed-forward neural networks with back propagation learning
or more efficient variations of this algorithm.
 On the other hand the Functional link ANN (FLANN) originally proposed by a
single layer structure with functionally mapped inputs.

18. ANN APPROACH TO CONTROL SYSTEMS

19.  The ever increasing technological demands of our modem society require
innovative approaches to highly demanding control problems.
 Artificial neural networks with their massive parallelism and learning
capabilities offer the promise of better solutions, at least to some problems.
 By now, the control community has heard of neural networks and wonders
if these networks can be used to provide better control solutions to old
problems or perhaps solutions to control problems that have withstood our best
efforts.
 Neural networks have the potential for very complicated behavior.
 They consist of many interconnected simple nonlinear systems, which are
typically modeled by sigmoid functions.
 The massive interconnections of the rather simple neurons, which make
up the human brain, provided the original motivation for the neural network
models.

20.  The terms artificial neural networks and connectionist models are typically used
to distinguish them from the biological networks of neurons of living organisms.
 In a neural network, the simple nonlinear elements called nodes or neurons are
interconnected, and the strengths of the interconnections are denoted by parameters
called weights.
 These weights are adjusted, depending on the task at hand, to improve
performance.
 They can be assigned new values in two ways: either determined via some
prescribed off-line algorithm-remaining fixed during operation-or adjusted via a
learning process.
 Learning is accomplished by, first, adjusting these weights step by step
(typically to minimize some objective function) and, then, storing these best values
as the actual strengths of the interconnections.
 The interconnections and their strength provide the memory, which is necessary
in a learning process.

21.  The ability to learn is one of the main advantages that make the neural networks
so attractive.
 They also have the capability of performing massive parallel processing, which
is in contrast to the von Neumann machines- the conventional digital computers in
which the instructions are executed sequentially.
 Neural networks can also provide, in principle, significant fault tolerance, since
damage to a few links need not significantly impair the overall performance.
 The benefits are most dramatic when a large number of nodes are used and are
implemented in hardware.
 The hardware implementation of neural networks is currently a very active
research area; optic and more conventional means of implementation of these large
networks have been suggested.
 The use of neural networks in control systems can be seen as a natural step in the
evolution of control methodology to meet new challenges.

22.  Looking back the evolution in the control area has been fueled by three major
needs: the need to deal with increasingly complex systems, the need to accomplish
increasingly demanding design requirements, and need to attain these
requirements with less precise advanced knowledge of the plant and its
environment- that is, the need to control under increased uncertainty.
 Today, the need to control, in a better way, increasingly complex dynamical
systems under significant uncertainty has led to a reevaluation of the conventional
control methods, and it has made the need for new methods quite apparent.
 It has also led to a more general concept of control, one that includes higher-
level decision making, planning, and learning, which are capabilities necessary
when higher degrees of system autonomy are desirable.

23.  Multilayer neural networks have been applied successfully in the identification
and control of dynamic systems.
 Rather than attempt to survey the many ways in which multilayer networks have
been used in control systems, we will concentrate on three typical neural network
controllers:
1) model predictive control,
2) NARMA-L2 control,
3) model reference control.
 These controllers are representative of the variety of common ways in which
multilayer networks are used in control systems.
 As with most neural controllers, they are based on standard linear control
architectures.
 There are typically two steps involved when using neural networks for control:
system identification and control design.

24.  In the system identification stage, we develop a neural network model of the
plant that we want to control.
 In the control design stage, we use the neural network plant model to design (or
train) the controller.
 In each of the three control architectures, the system identification stage is
identical.
 The control design stage, however, is different for each architecture.

25. A. NN Predictive Control
 There are a number of variations of the neural network predictive controller that
are based on linear model predictive controllers.
 The neural network predictive controller uses a neural network model of a
nonlinear plant to predict future plant performance.
 The controller then calculates the control input that will optimize plant
performance over a specified future time horizon.
 The first step in model predictive control is to determine the neural network plant
model (system identification).
 Next, the plant model is used by the controller to predict future performance. The
next section describes the system identification process.
 This is followed by a description of the optimization process and an application
of predictive control to a magnetic levitation system.

26. 1) System Identification
 The first stage of model predictive control is to train a neural network to
represent the forward dynamics of the plant.
 The prediction error between the plant output and the neural network output is
used as the neural network training signal.
 The process is represented by Figure.

27. 2) Predictive Control
 The model predictive control method is based on the receding horizon
technique.
 The neural network model predicts the plant response over a specified time
horizon.
 The predictions are used by a numerical optimization program to determine the
control signal that minimizes the following performance criterion over the
specified horizon.
 The following block diagram illustrates the model predictive control process.
 The controller consists of the neural network plant model and the optimization
block.
 The optimization block determines the values of that minimize, and then the
optimal is input to the plant.

28. Fig: Neural Network Predictive Control

29. B. NARMA-L2 Control
 The neuro controller described in this section is referred to by two different
names: feedback linearization control and NARMAL2 control.
 It is referred to as feedback linearization when the plant model has a particular
form (companion form).
 It is referred to as NARMA-L2 control when the plant model can be
approximated by the same form.
 The central idea of this type of control is to transform nonlinear system
dynamics into linear dynamics by canceling the nonlinearities.
 This section begins by presenting the companion form system model and
demonstrating how a neural network can be used to identify this model.
 Then it describes how the identified neural network model can be used to
develop a controller.
 Figure is a block diagram of the NARMA-L2 controller.

30. Fig: NARMA-L2 Controller

31. C. Model Reference Control
 The third neural control architecture we will discuss in this paper is model
reference control.
 This architecture uses two neural networks: a controller network and a plant
model network, as shown in Figure.
 The plant model is identified first, and then the controller is trained so that the
plant output follows the reference model output.

32. ANN APPROACH TO PATTERN
RECOGNITION

33.  Pattern recognition is a process of finding regularities and similarities in data
using machine learning data.
 Now, these similarities can be found based on statistical analysis, historical
data, or the already gained knowledge by the machine itself.
 A pattern is regularity in the world or in abstract notions. If we discuss sports,
a description of a type would be a pattern.
 If a person keeps watching videos related to cricket, YouTube wouldn’t
recommend them chess tutorials videos.
Examples: Speech recognition, speaker identification, multimedia document
recognition (MDR), automatic medical diagnosis.
 Before searching for a pattern there are some certain steps and the first one is
to collect the data from the real world.
 The collected data needs to be filtered and pre-processed so that its system can
extract the features from the data.

34.  Then based on the type of the data system will choose the appropriate
algorithm among Classification, Regression, and Regression to recognize the
pattern.
Classification: In classification, the algorithm assigns labels to data based on
the predefined features. This is an example of supervised learning.
Clustering: An algorithm splits data into a number of clusters based on the
similarity of features. This is an example of unsupervised learning.
Regression: Regression algorithms try to find a relationship between variables
and predict unknown dependent variables based on known data. It is based on
supervised learning.
Features can be represented as continuous, discrete, or discrete binary
variables. A feature is basically a function of one or more measurements, computed
to quantify the significant characteristics of the object. The feature is one of the
most important components in the Pattern Recognition system.
Example: consider a football, shape, size and color, etc. are features of the
football.
A feature vector is a set of features that are taken together.

35. Example: In the above example of football, if all the features (shape, size,
colour etc.) taken together then the sequence is feature vector ([shape, size, colour]).
The feature vector is the sequence of features represented as an n-dimensional
column vector. In the case of speech, MFCC (Mel-frequency Cepstral Coefficient) is
the spectral features of the speech. The sequence of the first 13 features forms a
feature vector.
Features of Pattern Recognition
1. Speed and accuracy for the familiar is high
2. It can recognize an unfamiliar object
3. It has the ability to recognize different shapes and object from all angles.
4. It identifies the patterns and objects when partly hidden.
5. During analysis quickly catch the patterns with automaticity.

36. Training a Pattern Recognition system

37. Training Set:
 The training set plays an important part to train the model.
 Program process this dataset by using training rules.
 To get the better result one need to collect quite a large dataset because the
program will always give better results with a handful of training data.
 But it may not give the same results in the case of the test dataset.
 If someone is building a masked face recognizer then he/she will need a lot of
images of people wearing a mask.
 From that dataset, the necessary information will be gathered by the program.
 Generally, 80% of the total dataset is used as the training dataset.

38. Validation Set:
 Fine-tuning helps to train the model.
 If for the training dataset the accuracy is increasing then a certain portion of data
from the training dataset which is unknown to the model is selected to check that for
that dataset also the accuracy is increasing.
 If accuracy is not increasing for the validation set then the program is over fitting
the model.
 In that case, the developer needs to check the value of the parameters or he/she
may have to reconsider the model.
Test Set:
 The test set is used to take the output from the model.
 After the training, it is used to check how accurate the model is.
 The rest of the 20% of the dataset is used as a test set.

39. How does it work?

40. Applications
 Image processing, segmentation, and analysis
Pattern Recognition is efficient enough to give machines human
recognition intelligence. This is used for image processing, segmentation, and
analysis. For example, computers can detect different types of insects better
than humans.
 Computer Vision
Using a pattern recognition system one can extract important features from
the images and videos. This is helpful in computer vision which is applied in
different fields’, especially biomedical imaging.
 Seismic Analysis
Decision-theoretic and syntactic pattern recognition techniques are
employed to detect the physical anomalies (bright spots) and to recognize the
structural seismic patterns in two-dimensional seismograms. Here, decision-
theoretic methods include Bayes classification, linear and quadratic
classifications, tree classification, partitioning-method, and tree classification,
and sequential classification.

41.  Radar Signal Classification
Pattern recognition and signal processing methods are used in a large dataset
to find similar characteristics like amplitude, frequencies, type of modulation,
scanning type, pulse repetition intervals, etc. Basically, it helps to classify the
radio signals, and based upon their class the conversion to digital form is
accomplished.
 Speech Recognition
All of us have heard the names Siri, Alexa, and Cortona. These are all the
applications of speech recognition. Pattern recognition plays a huge part in this
technique.
 Fingerprint Identification
Many recognition approaches are there to perform Fingerprint Identification.
But pattern recognition system is the most used approach.
 Medical Diagnosis
Algorithms of pattern recognition deal with real data. It has been found that
pattern recognition has a huge role in today’s medical diagnosis. From breast
cancer detection to covid-19 checking algorithms are giving results with more
than 90% accuracy.