Logic Families

TTL and CMOS

Logic Families Competencies
Without references the student will state what the acronym TTL stands for with 100% accuracy.

Logic Families Competencies
Without references the student will state the voltage levels acceptable to a TTL input for a logic “0” and a logic “1” with 100% accuracy.

Without references the student will state what the acronym CMOS stands for with 100% accuracy

This paper deals with the application of artificial neural network to the transient

stability assessment of a power system. The back propagation technique is used to train

the neural networks. The use of artificial neural network for computing critical clearing

time and transient energy margin for a machine infinite bus system has been illustrated.

In our enthusiastic investigations we found the ideas here proposed for a single machine

will be the pioneer for extension of the same to the assessment of multi machine stability.

Introduction

A high degree of security for normal operation of larger inter connected power

system is required one of the requirements of reliable service in electrical power system

is to maintain the synchronous machines running in parallel with adequate capacity to

meat the low demand with the growing stress on present days power systems, the

potential impact of faults and other disturbances on their security is increasing. Protective

relays in the power system detect faults and trigger the opening of circuit to isolate the

fault. The power system can be considered to go through changes in configuration in

three stages, from a pre fault, faulted to a post faults system. The analysis required

knowing whether following a contingency, the power system will “survive” the transients

and moving to a stable operating condition is referred to as dynamic security assessment.

The transient stability assessment of power system is done to appraise the system

capability to with stand major contingencies and to suggest remedial actions i.e., means

to enhance this capability.

Conventional methods for transient stability assessment.

Several approaches for transient stability assessment of power systems such as

numerical integration second method of Lyapunov, pattern recognition.

Numerical integration:

In this approach transient stability analysis if performed by simulation. For given

operating condition and special and specified large disturbance a time solution obtained

for the generator rotor angles, speeds, terminal voltages etc., by examining the swing

curves, separation of one or more generators from the rest of the system indicating loss of

synchronism is detected. Even for small power network and simple mathematical model

possible this method is slow and cumbersome.

The second method of Lyapunov:

In this method, the integration off post fall system equation is replaced by stability

criterion. The value of a suitably designed lyapunov function v is calculated at the instant

of last switching in the system and compared with the previously determined critical

value Vcr of this function if V is smaller than Vcr the system will reach a stable

equilibrium point.

Pattern recognition:

The main objective of the pattern recognition method is transient stability

assessment is to reduce computational requirements to minimum this is done at the

expense of elaborate off line computations. The methodology of pattern recognition

consists of defining a pattern vector x whose components contain all significant variables

of the system. This vector is evaluated at many representative-operating conditions to

generate “training set”. If some component of the pattern vector or strongly correlated

with one another a process of dimensionality reduction is performing to identified

significant and hopefully uncorrelated set of components. This is process is called feature

extraction. The final step is to determine a function s (x) such that

s(x) = { >=0 for a secure x)

{<=0 for an insecure x)

this function is called a classifier, at once the classifier obtained, for sample x one can

classify the sample as stable or unstable very rapidly. The most important task in the

application of pattern recognition is the selection of primary variables because the lower

limit for the classification error depends on the primary variables.

Draw backs of conventional methods

1. The online transient stability assessment of the electrical power systems is an

extremely difficult task with the available techniques.

2. Each contingency (fault) must be treated separately.

3. Smaller time step intervals are needed to ensure numerical stability.

4. Electro motive force and mechanical power inputs are assumed constant during

the transient.

5. Very elaborate off line computations give scope to errors.

Advantages of ANN over conventional methods

In order to ever come the above draw backs recently, there has been some interest

in the application of ANN in the assessment of transient stability an integrated approach,

compressing neural networks and conventional methods has the potential to meet the on

line requirements. The application of ANN for computing critical clearing time and

transient energy margin with respect to a specific contingency.

The advantages:

1. this technique has the potential of faster transient stability assessment than

the other conventional methods.

2. This technique provides the online transient stability assessment.

About artificial neural networks

Artificial Neural Networks

Neural-networks is one of those words that is getting fashionable in the new era

of technology. Most people have heard of them, but very few actually know what they

are. This essay is designed to introduce you to all the basics of neural networks - their

function, generic structure, terminology, types and uses.

The term 'neural network' is in fact a biological term, and what we refer to as

neural networks should really be called Artificial Neural Networks (ANNs). I will use the

two terms interchangeable throughout the essay, though. A real neural network is a

collection of neurons, the tiny cells our brains are comprised of. A network can consist of

a few to a few billion neurons connected in an array of different methods. ANNs attempt

to model these biological structures both in architecture and operation. There is a small

problem: we don't quite know how biological NNs work! Therefore, the architecture of

neural networks changes greatly from type to type. What we do know is the structure of

the basic neuron

The Artificial Neuron

Just as there is a basic biological neuron, there is basic artificial neuron. Each neuron has

a certain number of inputs, each of which have a weight assigned to them. The weights

simply are an indication of how 'important' the incoming signal for that input is. The net

value of the neuron is then calculated - the net is simply the weighted sum, the sum of all

the inputs multiplied by their specific weight. Each neuron has its own unique threshold

value, and it the net is greater than the threshold, the neuron fires (or outputs a 1),

otherwise it stays quiet (outputs a 0). The output is then fed into

all the neurons it is connected to.

Design

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 networks are

complex and are the major concerns of system developers.

Designing a neural network consist of :

• Arranging neurons in various layers.

• Deciding the type of connections among neurons for different layers, as

well as among the neurons within a layer.

• Deciding the way a neuron receives input and produces output.

• Determining the strength of connection within the network by allowing the

network learn the appropriate values of connection weights by using a

training data set.

Learning

The brain basically learns from experience. Neural networks are sometimes called

machine learning algorithms, because changing of its connection weights (training)

causes the network to learn the solution to a problem. The strength of connection

between the neurons is stored as a weight-value for the specific connection. The

system learns new knowledge by adjusting these connection weights.

The learning ability of a neural network is determined by its architecture and by

the algorithmic method chosen for training.

The training method usually consists of one of three schemes:

1. Unsupervised learning

The hidden neurons must find a way to organize themselves without help from the

outside. In this approach, no sample outputs are provided to the network against

which it can measure its predictive performance for a given vector of inputs. This is

learning by doing.

2. Reinforcement learning

This method works on reinforcement from the outside. The connections among

the neurons in the hidden layer are randomly arranged, then reshuffled as the network

is told how close it is to solving the problem. Reinforcement learning is also called

supervised learning, because it requires a teacher. The teacher may be a training set of

data or an observer who grades the performance of the network results.

Both unsupervised and reinforcement suffer from relative slowness and

inefficiency relying on a random shuffling to find the proper connection weights.

3. Back propagation

This method is proven highly successful in training of multilayered neural nets.

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. A form of supervised

learning.

Off-line or On-line

One can categorize the learning methods into yet another group, off-line or online.

When the system uses input data to change its weights to learn the domain

knowledge, the system could be in training mode or learning mode. When the system is

being used as a decision aid to make recommendations, it is in the operation mode, this is

also sometimes called recall.

Logic Families

TTL and CMOS

Unit_2_Logic_Families.ppt (Size: 999 KB / Downloads: 329)

Logic Families Competencies
Without references the student will state what the acronym TTL stands for with 100% accuracy.

Logic Families Competencies
Without references the student will state the voltage levels acceptable to a TTL input for a logic “0” and a logic “1” with 100% accuracy.

Without references the student will state what the acronym CMOS stands for with 100% accuracy

This paper deals with the application of artificial neural network to the transient

stability assessment of a power system. The back propagation technique is used to train

the neural networks. The use of artificial neural network for computing critical clearing

time and transient energy margin for a machine infinite bus system has been illustrated.

In our enthusiastic investigations we found the ideas here proposed for a single machine

will be the pioneer for extension of the same to the assessment of multi machine stability.

Introduction

A high degree of security for normal operation of larger inter connected power

system is required one of the requirements of reliable service in electrical power system

is to maintain the synchronous machines running in parallel with adequate capacity to

meat the low demand with the growing stress on present days power systems, the

potential impact of faults and other disturbances on their security is increasing. Protective

relays in the power system detect faults and trigger the opening of circuit to isolate the

fault. The power system can be considered to go through changes in configuration in

three stages, from a pre fault, faulted to a post faults system. The analysis required

knowing whether following a contingency, the power system will “survive” the transients

and moving to a stable operating condition is referred to as dynamic security assessment.

The transient stability assessment of power system is done to appraise the system

capability to with stand major contingencies and to suggest remedial actions i.e., means

to enhance this capability.

Conventional methods for transient stability assessment.

Several approaches for transient stability assessment of power systems such as

numerical integration second method of Lyapunov, pattern recognition.

Numerical integration:

In this approach transient stability analysis if performed by simulation. For given

operating condition and special and specified large disturbance a time solution obtained

for the generator rotor angles, speeds, terminal voltages etc., by examining the swing

curves, separation of one or more generators from the rest of the system indicating loss of

synchronism is detected. Even for small power network and simple mathematical model

possible this method is slow and cumbersome.

The second method of Lyapunov:

In this method, the integration off post fall system equation is replaced by stability

criterion. The value of a suitably designed lyapunov function v is calculated at the instant

of last switching in the system and compared with the previously determined critical

value Vcr of this function if V is smaller than Vcr the system will reach a stable

equilibrium point.

Pattern recognition:

The main objective of the pattern recognition method is transient stability

assessment is to reduce computational requirements to minimum this is done at the

expense of elaborate off line computations. The methodology of pattern recognition

consists of defining a pattern vector x whose components contain all significant variables

of the system. This vector is evaluated at many representative-operating conditions to

generate “training set”. If some component of the pattern vector or strongly correlated

with one another a process of dimensionality reduction is performing to identified

significant and hopefully uncorrelated set of components. This is process is called feature

extraction. The final step is to determine a function s (x) such that

s(x) = { >=0 for a secure x)

{<=0 for an insecure x)

this function is called a classifier, at once the classifier obtained, for sample x one can

classify the sample as stable or unstable very rapidly. The most important task in the

application of pattern recognition is the selection of primary variables because the lower

limit for the classification error depends on the primary variables.

Draw backs of conventional methods

1. The online transient stability assessment of the electrical power systems is an

extremely difficult task with the available techniques.

2. Each contingency (fault) must be treated separately.

3. Smaller time step intervals are needed to ensure numerical stability.

4. Electro motive force and mechanical power inputs are assumed constant during

the transient.

5. Very elaborate off line computations give scope to errors.

Advantages of ANN over conventional methods

In order to ever come the above draw backs recently, there has been some interest

in the application of ANN in the assessment of transient stability an integrated approach,

compressing neural networks and conventional methods has the potential to meet the on

line requirements. The application of ANN for computing critical clearing time and

transient energy margin with respect to a specific contingency.

The advantages:

1. this technique has the potential of faster transient stability assessment than

the other conventional methods.

2. This technique provides the online transient stability assessment.

About artificial neural networks

Artificial Neural Networks

Neural-networks is one of those words that is getting fashionable in the new era

of technology. Most people have heard of them, but very few actually know what they

are. This essay is designed to introduce you to all the basics of neural networks - their

function, generic structure, terminology, types and uses.

The term 'neural network' is in fact a biological term, and what we refer to as

neural networks should really be called Artificial Neural Networks (ANNs). I will use the

two terms interchangeable throughout the essay, though. A real neural network is a

collection of neurons, the tiny cells our brains are comprised of. A network can consist of

a few to a few billion neurons connected in an array of different methods. ANNs attempt

to model these biological structures both in architecture and operation. There is a small

problem: we don't quite know how biological NNs work! Therefore, the architecture of

neural networks changes greatly from type to type. What we do know is the structure of

the basic neuron

The Artificial Neuron

Just as there is a basic biological neuron, there is basic artificial neuron. Each neuron has

a certain number of inputs, each of which have a weight assigned to them. The weights

simply are an indication of how 'important' the incoming signal for that input is. The net

value of the neuron is then calculated - the net is simply the weighted sum, the sum of all

the inputs multiplied by their specific weight. Each neuron has its own unique threshold

value, and it the net is greater than the threshold, the neuron fires (or outputs a 1),

otherwise it stays quiet (outputs a 0). The output is then fed into

all the neurons it is connected to.

Design

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 networks are

complex and are the major concerns of system developers.

Designing a neural network consist of :

• Arranging neurons in various layers.

• Deciding the type of connections among neurons for different layers, as

well as among the neurons within a layer.

• Deciding the way a neuron receives input and produces output.

• Determining the strength of connection within the network by allowing the

network learn the appropriate values of connection weights by using a

training data set.

Learning

The brain basically learns from experience. Neural networks are sometimes called

machine learning algorithms, because changing of its connection weights (training)

causes the network to learn the solution to a problem. The strength of connection

between the neurons is stored as a weight-value for the specific connection. The

system learns new knowledge by adjusting these connection weights.

The learning ability of a neural network is determined by its architecture and by

the algorithmic method chosen for training.

The training method usually consists of one of three schemes:

1. Unsupervised learning

The hidden neurons must find a way to organize themselves without help from the

outside. In this approach, no sample outputs are provided to the network against

which it can measure its predictive performance for a given vector of inputs. This is

learning by doing.

2. Reinforcement learning

This method works on reinforcement from the outside. The connections among

the neurons in the hidden layer are randomly arranged, then reshuffled as the network

is told how close it is to solving the problem. Reinforcement learning is also called

supervised learning, because it requires a teacher. The teacher may be a training set of

data or an observer who grades the performance of the network results.

Both unsupervised and reinforcement suffer from relative slowness and

inefficiency relying on a random shuffling to find the proper connection weights.

3. Back propagation

This method is proven highly successful in training of multilayered neural nets.

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. A form of supervised

learning.

Off-line or On-line

One can categorize the learning methods into yet another group, off-line or online.

When the system uses input data to change its weights to learn the domain

knowledge, the system could be in training mode or learning mode. When the system is

being used as a decision aid to make recommendations, it is in the operation mode, this is

also sometimes called recall.