ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM RESTORATOIN
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INTRODUCTION

The importance of electricity in our day to day life has reached such a stage that it is very important to protect the power system equipments from damage and to ensure maximum continuity of supply. But there are power system blackouts by which the continuous power supply is being interrupted. What is more important in the case of a blackout is the rapidity with which the service is restored. Now- a -days power system blackouts are rare. But whenever they occur, the effect on commerce, industry and everyday life of general population can be quite severe. In order to reduce the social and economic cost of power system blackouts, many of the electric utility companies have pre-established guidelines and operating procedures to restore the power system. They contain sequential restoration steps that an operator should follow in order to restore the power system. They are based on certain assumptions which may not be present in the actual case. This reduces the success rates of these procedures.

This paper mainly focuses on:
· The limitations encountered in some currently used PSR techniques.
· A proposed improvement based on ANN.



WHAT ARE ANNs?

Artificial Neural Network (ANN) is a system loosely modeled on human brain. It tries to obtain a performance similar to that of humanâ„¢s performance while solving problems. As a computational system it is made up of a large number of simple and highly interconnected processing elements which process information by its dynamic state response to external inputs. Computational elements in ANN are non-linear and so the results come out through non-linearity can be more accurate than other methods. These non-linear computational elements will be working in unison to solve specific problems. ANN is configured for specific applications such as data classification or pattern recognition through a learning process. Learning involves adjustment of synaptic connections that exist between neurons. ANN can be simulated within specialized hardware or sophisticated software. ANNs are implemented as software packages in computer or being used to incorporate Artificial Intelligence in control systems.







BIOLOGICAL NEURON

The most basic element of the human brain is a specific type of cell, which provides us with the abilities to remember, think, and apply previous experiences to our every action. These cells are known as neurons, each of these neurons can connect with up to 200000 other neurons. The power of brain comes from the numbers of these basic components and the multiple connections between them.

All natural neurons have four basic components, which are dendrites, soma, axon and synapses. Basically, a biological neuron receives inputs from other sources, combines them in some way, performs a generally non-linear operation on the result, and then output the final result. The figure below shows a simplified biological neuron and the relationship of its four components.


ARTIFICIAL NEURON

The basic unit of neural networks, the artificial neurons, simulates the four basic functions of natural neurons. Artificial neurons are much simpler than the biological neurons. The figure below shows the basic structure of an artificial neuron.


Note that various inputs to the network are represented by the mathematical symbol, x(n). Each of these inputs are multiplied by a connection weight, these weights are represented by w(n). In the simplest case, these products are simply summed, fed through a transfer function to generate a result, and then output. Even though all artificial neural networks are constructed from this basic building blocks the fundamentals may vary in these building blocks and there are differences.

NEURAL NETWORKS

Artificial neural networks emerged from the studies of how brain performs. The human brain consists of many million of individual processing elements called neurons that are highly interconnected.

ANNs are made up of simplified individual models of the biological neurons that are connected together to form a network. Information is stored in the network in the form of weights or different connection strengths associated with the synapses in the artificial neuron models.

Many different types of neural networks are available and multilayered neural network are the most popular which are extremely successful in pattern reorganization problems. An artificial neuron is shown in the figure. Each neuron input is weighted by wi. Changing the weights of an element will alter the behavior of the whole network. The output y is obtained summing the weighted inputs and passing the result through a non-linear activation function.




PROCEDURE FOR ANN SYSTEM DESIGN

In realistic application the design of ANNs is complex, usually an iterative and interactive task. The developer must go through a period of trial and error in the design decisions before coming up with a satisfactory design. The design issues in neural network are complex and are the major concerns of system developers.

Designing of a neural network consists of:
· Arranging neurons in various layers.

· Deciding the type of connection among neurons of different layers , as well as among the neurons within a layer.

· Deciding the way neurons receive input and produces output.

· Determining the strength of connection that exists within the network by allowing the neurons learn the appropriate values of connection weights by using a training data set.



The process of designing a neural network is an iterative process.
The figure below describes its basic steps.



As the figure above shows, the neurons are grouped into layers. The input layer consists of neurons that receive input from external environment. The output layer consists of neurons that communicate the output of the system to the user or external environment. There are usually a number of hidden layers between these two layers. The figure above shows a simple structure with only one hidden layer.

When the input layer receives the input , its neurons produces output, which become input to the other layers of the system. The process continues until certain condition is satisfied or until the output layer is invoked and fire their output to the external environment.

FEATURES OF ANNs

ANNS have several attractive features:
· Their ability to represent non-linear relations makes them well suited for non-linear modeling in control systems.
· Adaptation and learning in uncertain system through off line and on line weight adaptation.
· Parallel processing architecture allows fast processing for large-scale dynamic system.
· Neural network can handle large number of inputs and can have many outputs.
· ANNs can store knowledge in a distributed fashion and consequently have a high fault tolerance.





LEARNING TECHNIQUES
An ANN can been seen as a union of simple processing units, based on neurons that are linked to each other through connections similar to synapses. These connections contain the knowledge of the network and the pattern of connectivity express the objects represented in the network. The knowledge of the network is acquired through a learning process where the connections between processing elements is varied through weight changes.

Learning rules are algorithms for slowly alerting the connection weights to achieve a desired goal such as minimization of an error function. Learning algorithms used to train ANNs can be supervised or unsupervised. In supervised learning algorithms, input/output pairs are furnished and the connection weights are adjusted with respect to the error between the desired and obtained output. In unsupervised learning algorithms, the ANN will map an input set in a state space by automatically changing its weight connections. Supervised learning algorithms are commonly used in engineering processes because they can guarantee the output.

In this power system restoration scheme, a multilayered perceptron(MLP) was used and trained with a supervised learning algorithm called back-propagation. A MLP consists of several layers of processing units that compute a nonlinear function of the internal product of the weighted input patterns. These types of network can deal with nonlinear relations between the variables; however, the existence of more than one layer makes the weight adjustment process for problem solution difficult.

BACK PROPOGATION ALGORITHM
This method has proven highly successful in training of multilayered neural networks. The network is not just given reinforcement for how it is doing on a task. Information about errors is also filtered back through the system and is used to adjust the connections between the layers, thus improving performance of the network results. Back-propagation algorithm is a form of supervised learning algorithm.


CONVENTIONAL RESTORATION TECHNIQUES

VARIOUS PRICIPLES USED:

· Automated restoration: In this restoration technique, computer programs are responsible for program development and implementation. The PSR techniques based on this principle acquire data from the supervisory control and data acquisition system (SCADA) and the energy management system (EMS). Under a wide area disturbance, a PSR program installed in the EMS system will use the acquired system to develop a restoration plan for the transmission system. After developing the restoration plan, a switching sequence program, which is also a part of the EMS, will be responsible for the transmission of control signals through SCADA to circuit breakers and switches to implement the plan. In this technique, the system operator plays the role of supervisor.

· Computer aided restoration: In this technique, the PSR plan development and implementation is performed by the system operator. The PSR technique that uses this principle also acquire system data from the system local SCADA/EMS. Following a wide area disturbance, the system operator uses power system data provided by the SCADA/EMS to develop a PSR plan. The system operator can use the PSR procedure and power system analysis programs as aid to develop restoration plans. The system operator will also use the local SCADA/EMS to transmit control commands to circuit breakers and switches in order to implement the chosen PSR plan.

· Cooperative restoration: In this technique, a computer program installed at the EMS will propose a PSR plan after the occurrence of the blackout. The system operator is responsible for the implementation of PSR plan. The PSR systems that apply this technique also use power system data obtained from local SCADA/EMS. When the power system is under going a wide area disturbance, the PSR program installed in the EMS will use the system data to develop a restoration plan. With this restoration plan, the system operator can send controlling signals through local SCADA/EMS to circuit breakers and switches to implement the plan.




PROPOSED ANN BASED RESTORATION SCHEME
The proposed restoration scheme is composed of several Island Restoration Schemes(IRS). Each IRS is responsible for the development of an island restoration plan when the power system is recovering from a wide-area disturbance. The number of IRSs will be defined by off-line studies and will depend on regional load-generation balance. The division of the system into islands is a common action in large transmission systems where parallel restoration is more efficient and desired. The parallel restoration technique is commonly used in the restoration schemes applied to large transmission systems. This technique is also used in the proposed restoration scheme. The all-open switching strategy where all circuit breakers of the system are open will be used to create the islands. In order to restore a power system following a wide-area disturbance, each IRS of restoration scheme will generate local restoration plans composed of switching sequences of local circuit breakers and a forecast restoration load.

Each IRS is composed of two ANNs and a switching sequence program (SSP). The first ANN of each IRS is responsible for an island restoration load forecast. The input of this ANN will be a normalized vector composed of the pre-disturbance load. The second ANN of each IRS is responsible for the determination of the final island configuration and the associated forecast restoration load pick up percentage that will generate a feasible operational condition. The input of this ANN will be a normalized vector composed of the forecast island restoration load provided by the first ANN of the respective IRS, three elements describing possible unavailable transmission paths(because of outages) for use in the restoration plan. The final element of each IRS is the SSP. The SSP will determine the energizing sequence of transmission paths that will lead to the final configuration chosen by the second ANN. The SSP input vector is composed of the final restoration island configuration generated by the second ANN of the IRS and an energizing sequence database. The energizing sequence database of each IRS is composed of transmission path sequences connecting island generators to island loads. The following figure illustrates the functional block diagram of an IRS.





The proposed restoration scheme will present a restoration plan to the EMS operator following the occurrence of a wide area disturbance. The power system operator must apply the all open switch strategy through the EMS/SCADA or through regional control centers before the plan is implemented. The restoration plan provided by the proposed scheme will be composed of energizing sequences and restoration load percentage pick up values for all islands. As the final step of the total restoration, the closing of the tie-lines will be the responsibility of the system operator. The tie-lines should be closed when all the islands are restored and are in steady state.



RESTORATION CONSTRAINTS
In order to generate a feasible restoration plan to be used as a training pattern by the IRSs, certain operational constraints must be considered.
The various constraints considered are:

· Thermal limits of transmission lines
· Stability limits
· Number of lines used in the restoration plan
· Allowable over and under voltage
· Recognition of locked “out circuit breakers

The thermal rating of the normally designed transmission lines depends mainly on the voltage level at which they operate, the line length and reactance.

Power system stability is a subject of major concern in PSR. The restored system generated by the PSR scheme has to be able to allow for sufficiently large load and generation variations without encountering undesirable and uncontrollable behavior that could lead to instability and a recurrence of the system blackout. In order to check the stability of the restored power system, transient stability studies must be conducted.

The number of transmission lines used in the restoration plan also needs some consideration. The number of transmission lines used in the PSR plan is very important. Transmissions play a critical role in reactive power balance and over voltage control during the restoration implementation. In order to maintain a normal voltage profile and avoid the generation of excessive reactive power, it is advisable to energize the smallest possible number of transmission lines in a proper sequence during the restoration process.

Circuit breakers have the capability to go through a certain number of open-close sequences when automatic enclosing is enabled. Once the available number of open-close sequences is exhausted, the circuit breaker goes into a lock-out state. Permanent non recoverable equipment faults may also lead to circuit breaker lock-outs. A locked out circuit breaker will normally require manual resetting before it can be made available for normal operations. Clearly, the locked-out circuit breakers cannot be used for automatic restoration and should be taken into account by the PSR scheme.






CONCLUSION

PSR has become a field of growing interest. Several techniques based on artificial intelligence have been proposed to improve power system restoration. These techniques propose the use of the computer as an operator aid instead of the use of predefined operating procedures for restoration. The stressful condition following a blackout and the pressure for achieving a restoration plan in minimum time can lead to misjudgment by system operator. This paper proposes the use of ANN for service restoration plan, since it has generalization capability and high processing speed. The large number of possible faulty conditions and the need to provide a restoration plan in minimum time are arguments in favor of this technique.




REFERENCES
· IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 18,
NO. 4, OCTOBER 2003
· NEURAL NETWORKS “ CONTROL SYSTEMS ENGINEERING (THRID EDITION) BY I.J.NAGRATH & M.GOPAL

ABSTRACT

Power System Restoration (PSR) has been a subject of study for many years. In recent years many techniques were proposed to solve the limitations of predetermined restoration guidelines and procedures used by a majority of system operators to restore a system following the occurrence of a wide area disturbance. This paper discusses limitations encountered in some currently used PSR techniques and a proposed improvement based on Artificial Neural Networks (ANNs). This proposed scheme has been tested on a 162-bus transmission system and compared with a breadth search transmission system. The results indicate that, this is a feasible option that should be considered for real time applications.

Artificial Neural Networks (ANNs) are computational techniques that try to obtain a performance similar to that of humanâ„¢s performance when solving problems. The building block of ANN is Artificial Neuron, which has got structural & functional similarities with biological neurons. ANN is also an efficient alternative for problem solutions where it is possible to obtain data describing the problem behavior, but a mathematical description of the process is impossible. The proposed restoration scheme is composed of several Island Restoration Schemes (IRS). Each IRS is responsible for the development of an Island Restoration Plan when the power system is recovering from a wide area disturbance.


CONTENTS

1. INTRODUCTION 1
2. WHAT ARE ANNS? 2
3. BIOLOGICAL NEURON 3
4. ARTIFICIAL NEURON 4
5. NEURAL NETWORKS 5
6. PROCEDURE FOR ANN SYSTEM DESIGN 6
7. FEATURES OF ANN 8
8. LEARNING TECHNIQUES 9
9. CONVENTIONAL RESTORATION TECHNIQUES 11
10. PROPOSED ANN BASED RESTORATION SCHEME 13
11. RESTORATION CONSTRAINTS 16
12. CONCLUSION 18
13. REFERENCES 19
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Presented By;
Ankit

INTRODUCTION

The importance of electricity in our day to day life has reached such a stage that it is very important to protect the power system equipments from damage and to ensure maximum continuity of supply. But there are power system blackouts by which the continuous power supply is being interrupted. What is more important in the case of a blackout is the rapidity with which the service is restored. Now- a -days power system blackouts are rare. But whenever they occur, the effect on commerce, industry and everyday life of general population can be quite severe. In order to reduce the social and economic cost of power system blackouts, many of the electric utility companies have pre-established guidelines and operating procedures to restore the power system. They contain sequential restoration steps that an operator should follow in order to restore the power system. They are based on certain assumptions which may not be present in the actual case. This reduces the success rates of these procedures.
This report mainly focuses on:
¢ The limitations encountered in some currently used PSR techniques.
¢ A proposed improvement based on ANN.

WHAT ARE ANNs?

Artificial Neural Network (ANN) is a system loosely modeled on human brain. It tries to obtain a performance similar to that of humanâ„¢s performance while solving problems. As a computational system it is made up of a large number of simple and highly interconnected processing elements which process information by its dynamic state response to external inputs. Computational elements in ANN are non-linear and so the results come out through non-linearity can be more accurate than other methods. These non-linear computational elements will be working in unison to solve specific problems. ANN is configured for specific applications such as data classification or pattern recognition through a learning process. Learning involves adjustment of synaptic connections that exist between neurons. ANN can be simulated within specialized hardware or sophisticated software. ANNs are implemented as software packages in computer or being used to incorporate Artificial Intelligence in control systems.

BIOLOGICAL NEURON

The most basic element of the human brain is a specific type of cell, which provides us with the abilities to remember, think, and apply previous experiences to our every action. These cells are known as neurons, each of these neurons can connect with up to 200000 other neurons. The power of brain comes from the numbers of these basic components and the multiple connections between them. All natural neurons have four basic components, which are dendrites, soma, axon and synapses. Basically, a biological neuron receives inputs from other sources, combines them in some way, performs a generally non-linear operation on the result, and then output the final result. The figure below shows a simplified biological neuron and the relationship of its four components.


ARTIFICIAL NEURON

The basic unit of neural networks, the artificial neurons, simulates the four basic functions of natural neurons. Artificial neurons are much simpler than the biological neurons. The figure below shows the basic structure of an artificial neuron.

Note that various inputs to the network are represented by the mathematical symbol, x(n). Each of these inputs are multiplied by a connection weight, these weights are represented by w(n). In the simplest case, these products are simply summed, fed through a transfer function to generate a result, and then output. Even though all artificial neural networks are constructed from this basic building blocks the fundamentals may vary in these building blocks and there are differences.

NEURAL NETWORKS

Artificial neural networks emerged from the studies of how brain performs. The human brain consists of many million of individual processing elements called neurons that are highly interconnected.


ANNs are made up of simplified individual models of the biological neurons that are connected together to form a network. Information is stored in the network in the form of weights or different connection strengths associated with the synapses in the artificial neuron models.
Many different types of neural networks are available and multilayered neural network are the most popular which are extremely successful in pattern reorganization problems. An artificial neuron is shown in the figure. Each neuron input is weighted by wi. Changing the weights of an element will alter the behavior of the whole network. The output y is obtained summing the weighted inputs and passing the result through a non-linear activation function.

PROCEDURE FOR ANN SYSTEM DESIGN

In realistic application the design of ANNs is complex, usually an iterative and interactive task. The developer must go through a period of trial and error in the design decisions before coming up with a satisfactory design. The design issues in neural network are complex and are the major concerns of system developers.
Designing of a neural network consists of:
¢ Arranging neurons in various layers.
¢ Deciding the type of connection among neurons of different layers, as well as among the neurons within a layer.
¢ Deciding the way neurons receive input and produces output.
¢ Determining the strength of connection that exists within the network by allowing the neurons learn the appropriate values of connection weights by using a training data set.
The process of designing a neural network is an iterative process.
The figure below describes its basic steps.


As the figure above shows, the neurons are grouped into layers. The input layer consists of neurons that receive input from external environment. The output layer consists of neurons that communicate the output of the system to the user or external environment. There are usually a number of hidden layers between these two layers. The figure above shows a simple structure with only one hidden layer.
When the input layer receives the input , its neurons produces output, which become input to the other layers of the system. The process continues until certain condition is satisfied or until the output layer is invoked and fire their output to the external environment.

FEATURES OF ANNs

ANNS have several attractive features:
¢ Their ability to represent non-linear relations makes them well suited for non-linear modeling in control systems.
¢ Adaptation and learning in uncertain system through off line and on line weight adaptation.
¢ Parallel processing architecture allows fast processing for large-scale dynamic system.
¢ Neural network can handle large number of inputs and can have many outputs.
¢ ANNs can store knowledge in a distributed fashion and consequently have a high fault tolerance.

LEARNING TECHNIQUES

An ANN can been seen as a union of simple processing units, based on neurons that are linked to each other through connections similar to synapses. These connections contain the knowledge of the network and the pattern of connectivity express the objects represented in the network. The knowledge of the network is acquired through a learning process where the connections between processing elements is varied through weight changes.

Learning rules are algorithms for slowly alerting the connection weights to achieve a desired goal such as minimization of an error function. Learning algorithms used to train ANNs can be supervised or unsupervised. In supervised learning algorithms, input/output pairs are furnished and the connection weights are adjusted with respect to the error between the desired and obtained output. In unsupervised learning algorithms, the ANN will map an input set in a state space by automatically changing its weight connections. Supervised learning algorithms are commonly used in engineering processes because they can guarantee the output.
In this power system restoration scheme, a multilayered perceptron(MLP) was used and trained with a supervised learning algorithm called back-propagation. A MLP consists of several layers of processing units that compute a nonlinear function of the internal product of the weighted input patterns. These types of network can deal with nonlinear relations between the variables; however, the existence of more than one layer makes the weight adjustment process for problem solution difficult.

BACK PROPOGATION ALGORITHM

This method has proven highly successful in training of multilayered neural networks. The network is not just given reinforcement for how it is doing on a task. Information about errors is also filtered back through the system and is used to adjust the connections between the layers, thus improving performance of the network results. Back-propagation algorithm is a form of supervised learning algorithm.

CONVENTIONAL RESTORATION TECHNIQUES
VARIOUS PRICIPLES USED:-

Automated restoration: In this restoration technique, computer programs are responsible for program development and implementation. The PSR techniques based on this principle acquire data from the supervisory control and data acquisition system (SCADA) and the energy management system (EMS). Under a wide area disturbance, a PSR program installed in the EMS system will use the acquired system to develop a restoration plan for the transmission system.
After developing the restoration plan, a switching sequence program, which is also a part of the EMS, will be responsible for the transmission of control signals through SCADA to circuit breakers and switches to implement the plan. In this technique, the system operator plays the role of supervisor.
Computer aided restoration: In this technique, the PSR plan development and implementation is performed by the system operator. The PSR technique that uses this principle also acquire system data from the system local SCADA/EMS. Following a wide area disturbance, the system operator uses power system data provided by the SCADA/EMS to develop a PSR plan. The system operator can use the PSR procedure and power system analysis programs as aid to develop restoration plans. The system operator will also use the local SCADA/EMS to transmit control commands to circuit breakers and switches in order to implement the chosen PSR plan.
Cooperative restoration: In this technique, a computer program installed at the EMS will propose a PSR plan after the occurrence of the blackout. The system operator is responsible for the implementation of PSR plan.
The PSR systems that apply this technique also use power system data obtained from local SCADA/EMS. When the power system is undergoing a wide area disturbance, the PSR program installed in the EMS will use the system data to develop a restoration plan. With this restoration plan, the system operator can send controlling signals through local SCADA/EMS to circuit breakers and switches to implement the plan.

PROPOSED ANN BASED RESTORATION SCHEME

The proposed restoration scheme is composed of several Island Restoration Schemes(IRS). Each IRS is responsible for the development of an island restoration plan when the power system is recovering from a wide-area disturbance. The number of IRSs will be defined by off-line studies and will depend on regional load-generation balance. The division of the system into islands is a common action in large transmission systems where parallel restoration is more efficient and desired. The parallel restoration technique is commonly used in the restoration schemes applied to large transmission systems. This technique is also used in the proposed restoration scheme. The all-open switching strategy where all circuit breakers of the system are open will be used to create the islands. In order to restore a power system following a wide-area disturbance, each IRS of restoration scheme will generate local restoration plans composed of switching sequences of local circuit breakers and a forecast restoration load.
Each IRS is composed of two ANNs and a switching sequence program (SSP). The first ANN of each IRS is responsible for an island restoration load forecast. The input of this ANN will be a normalized vector composed of the pre-disturbance load. The second ANN of each IRS is responsible for the determination of the final island configuration and the associated forecast restoration load pick up percentage that will generate a feasible operational condition. The input of this ANN will be a normalized vector composed of the forecast island restoration load provided by the first ANN of the respective IRS, three elements describing possible unavailable transmission paths(because of outages) for use in the restoration plan. The final element of each IRS is the SSP. The SSP will determine the energizing sequence of transmission paths that will lead to the final configuration chosen by the second ANN. The SSP input vector is composed of the final restoration island configuration generated by the second ANN of the IRS and an energizing sequence database. The energizing sequence database of each IRS is composed of transmission path sequences connecting island generators to island loads. The following figure illustrates the functional block diagram of an IRS.

The proposed restoration scheme will present a restoration plan to the EMS operator following the occurrence of a wide area disturbance.
The power system operator must apply the all open switch strategy through the EMS/SCADA or through regional control centers before the plan is implemented. The restoration plan provided by the proposed scheme will be composed of energizing sequences and restoration load percentage pick up values for all islands. As the final step of the total restoration, the closing of the tie-lines will be the responsibility of the system operator. The tie-lines should be closed when all the islands are restored and are in steady state.

RESTORATION CONSTRAINTS

In order to generate a feasible restoration plan to be used as a training pattern by the IRSs, certain operational constraints must be considered.
The various constraints considered are:
¢ Thermal limits of transmission lines(The maximum amount of power a transmission line can carry without suffering heat-related deterioration of line equipment)
¢ Stability limits
¢ Number of lines used in the restoration plan
¢ Allowable over and under voltage
¢ Recognition of locked “out circuit breakers
The thermal rating of the normally designed transmission lines depends mainly on the voltage level at which they operate, the line length and reactance. Power system stability is a subject of major concern in PSR. The restored system generated by the PSR scheme has to be able to allow for sufficiently large load and generation variations without encountering undesirable and uncontrollable behavior that could lead to instability and a recurrence of the system blackout. In order to check the stability of the restored power system, transient stability studies must be conducted. The number of transmission lines used in the restoration plan also needs some consideration. The number of transmission lines used in the PSR plan is very important. Transmissions play a critical role in reactive power balance and over voltage control during the restoration implementation. In order to maintain a normal voltage profile and avoid the generation of excessive reactive power, it is advisable to energize the smallest possible number of transmission lines in a proper sequence during the restoration process.

Circuit breakers have the capability to go through a certain number of open-close sequences when automatic enclosing is enabled.Once the available number of open-close sequences is exhausted, the circuit breaker goes into a lock-out state. Permanent non recoverable equipment faults may also lead to circuit breaker lock-outs. A locked out circuit breaker will normally require manual resetting before it can be made available for normal operations. Clearly, the locked-out circuit breakers cannot be used for automatic restoration and should be taken into account by the PSR scheme.
Power System Restoration (PSR) has been a subject of study for many years. In recent years many techniques were proposed to solve the limitations of predetermined restoration guidelines and procedures used by a majority of system operators to restore a system following the occurrence of a wide area disturbance. This paper discusses limitations encountered in some currently used PSR techniques and a proposed improvement based on Artificial Neural Networks (ANNs). This proposed scheme has been tested on a 162-bus transmission system and compared with a breadth search transmission system. The results indicate that, this is a feasible option that should be considered for real time applications.
Artificial Neural Networks (ANNs) are computational techniques that try to obtain a performance similar to that of humanâ„¢s performance when solving problems. The building block of ANN is Artificial Neuron, which has got structural & functional similarities with biological neurons. ANN is also an efficient alternative for problem solutions where it is possible to obtain data describing the problem behavior, but a mathematical description of the process is impossible. The proposed restoration scheme is composed of several Island Restoration Schemes (IRS). Each IRS is responsible for the development of an Island Restoration Plan when the power system is recovering from a wide area disturbance.


Introduction:-

Nonintrusive Load Monitoring :-(NILM) Nonintrusive Appliance Load Monitoring, is a process for analyzing changes in the voltage and current going into a house and deducing what appliances are used in the house as well as their individual energy consumption. Electric meters with NILM technology are used by utility companies to survey the specific uses of electric power in different homes. NILM is considered a low cost alternative to attaching individual monitors on each appliance.
A digital AC monitor is attached to the three-phase power going into a residence. Changes in the voltage and current are measured (i.e. admittance measurement unit), normalized (Scaler) and recorded (Net Change Detector Unit). A cluster analysis is then performed to identify when different appliances are turned on and off. If a 60-watt bulb is turned on, for example, followed by a 100 watt bulb being turned on, followed by the 60 watt bulb being turned off followed by the 100 watt bulb being turned off, the NILM unit will match the on and off signals from the 60 watt bulb and the on and off signals from the 100 watt bulb to determine how much power was used by each bulb and when. The system is sufficiently sensitive that individual 60 watt bulbs can be discriminated due to the normal variations in actual power draw of bulbs with the same nominal rating. (e.g. one bulb might draw 61 watts, another 62 watts).

Figure showing how differences in reactive power can help distinguish one appliance from another. The system can measure both reactive power and real power. Hence two appliances with the same total power draw can be distinguished by differences in their complex impedance. As shown in figure 8, for example, a refrigerator electric motor and a pure resistive heater can be distinguished in part because the electric motor has significant changes in reactive power when it turns on and off, whereas the heater has almost none.

NILM systems can also identify appliances with a series of individual changes in power draw. These appliances are modeled as finite state machines. A dishwasher, for example, has heaters and motors that turn on and off during a typical dish washing cycle. These will be identified as clusters, and power draw for the entire cluster will be recorded. Hence dishwasher power draw can be identified as opposed to Resistor heating unit and Electric motor.
Applications
¢ NILM systems are used to perform surveys of both residential and commercial energy consumption.
¢ NILM algorithms have also been proposed for use in detecting drunk driving.

Detection and diagnosis of HVAC faults via electrical load monitoring.

Detection and diagnosis of faults (FDD) in HVAC equipment have typically relied on measurements of variables available to a control system, including temperatures, flows, pressures, and actuator control signals. Electrical power at the level of a fan, pump, or chiller has been generally ignored because power meters are rarely installed at individual loads. This report presents two techniques for using electrical power data for detecting and diagnosing a number of faults in air-handling units. The results from the two techniques are compared and the situation for which each is applicable is assessed. One technique relies on gray-box correlations of electrical power with such exogenous variables as airflow or motor speed. This technique has been implemented with short-term average electrical power measured by dedicated submeters. With somewhat reduced resolution, it has also been implemented with a high-speed, centralized power meter that provides component-specific power information via analysis of the step changes in power that occur when a given device turns on or off. This technique was developed to detect and diagnose a limited number of air handler faults and is shown to work well with data taken from a test building.
A detailed evaluation of the method is presented in the companion report, which documents the results of a series of semi-blind tests. The second technique relies on physical models of the electromechanical dynamics that occur immediately after a motor is turned on. This technique has been demonstrated with submetered data for a pump and for a fan. Tests showed that several faults could be successfully detected from motor startup data alone. While the method relies solely on generally stable and accurate voltage and current sensors, thereby avoiding problems with flow and temperature sensors used in other fault detection methods, it requires electrical data taken directly at the motor, downstream of variable-speed drives, where current sensors would not be installed for control or load-monitoring purposes.
This report describes a low-cost approach to obtain and analyze electrical power data that are very useful for performance monitoring and fault detection.
Goals:-
¢ Reduce the energy consumption and associated environmental degradation of commercial buildings throughout the world;
¢ Reduce energy costs.
How to meet these goals?
¢ Deployment of appropriate methods for monitoring building performance and automatically detecting and diagnosing faults in energy-consuming equipment or in building components that directly affect energy usage.
Measurements are valuable but often expensive:
¢ Can't control what cannot be measured;
Component-specific data brings into sharp focus variations in whole-building energy consumption patterns that may hint at operating problems and energy waste;
¢ Building owner and operators are naturally reluctant to invest in more sensors.
One way to move forward is to make as much use as possible from electricity- consumption data:
¢ Electricity-consumption data can be directly related to operating costs through electricity rates or bilateral purchase contracts;
¢ Detailed measurements can help detect and diagnose excessive whole-building energy usage and component-level faults.

How to keep costs down?

Researchers at MIT over the last 15 years have taken significant steps toward developing a very powerful electricity monitoring approach that can pull component-level information out of whole-building electrical service, the electricity supplied to a major building subsystem (HVAC), or other electrical systems (transportation, industry). The product based on this approach is known as a Non-Intrusive Load Monitor or, more simply, NILM. We consider several field applications to illustrate the utility of the NILM.
1. HVAC Monitoring

Measurement of electrical power at the distribution panel for a large HVAC plant serving three connected buildings:
¢ One-megawatt plant consists of multiple chillers, ventilation fans and pumps;
¢ Data averaged over one-second intervals;
¢ A 20 kW chilled-water pump was cycled on and off four times during the test period.

Looking at electricity data:

¢ The pump on-off transitions appear as very small variations in the total power (Figure by power electronics used in variable-speed drives (Figure 2);
¢ A median filter rejects the spikes but retain the step transitions [5];



¢ A signal-processing technique known as the generalized-likelihood ratio (GLR) was used to detect the on-off events [6-10]. This method searches over a sliding window for the maximum value of the ratio of probability distributions of data points about pre- and post-event mean values. If there is no step change, the ratio is small; if a motor or lamp bank or other equipment switches on, the ratio is large as the window slides through the event.
¢ Four pairs of GLR spikes mark the four on-off events (Figure 3).
Note in this case that we were able to tune to detection method to eliminate all false alarms. We are currently working to automate the tuning process in response to measured characteristics of the electrical signal.
The GLR output provides confirmation that equipment has turned on or off when scheduled by the Building Energy Management System (BEMS). The absence of such confirmation indicates a fault. While such confirmation can be provided by current transducers attached to each piece of equipment, the GLR method is able to discern the switching events from a single point, reducing sensor costs. Further, the GLR works with power rather than current. Differences in power before or after an on-off transition provides information about equipment performance, normal or faulty. We will say more in the next example about an ongoing demonstration that uses centralized power measurements for fault detection.
2. Fault Detection
Detection and diagnosis of HVAC faults:
¢ The test site is a research building run by the Iowa Energy Center and known as the Energy Resource Station. It consists of two sets of test rooms, each with a separate variable-air-volume (VAV) ventilation system, and a set of rooms occupied by research staff, served by a third VAV system.
¢ MIT and Loughborough University, UK, are currently demonstrating FDD methods, under ASHRAE sponsorship. A detailed description of this work will be publicly available when MIT and Loughborough have completed their work and ASHRAE has approved a final report.
¢ We are comparing results from analysis of two different data streams, one from traditional (and more ex pensive) submetered power measurements and the other from MIT™s latest NILM hardware platform. The hardware platform consists of a Pentium- based personal computer with an installed digital signal processor (DSP) board.
¢ The DSP board analyzes real and reactive power, at the fundamental and higher harmonics.
¢ The PC can deliver information remotely, over the web (http://nilm.mit.edu).
¢ The host and the DSP board together cost about $500.
Analysis of data measured at the electrical service entry for the entire building:
¢ Fifteen-minute average data, similar to the output of a conventional data logger, show little component-specific detail (Figure 4);
¢ Higher-speed data (10-second sampling period) shows more information and more noise (Figure 5);
¢ Data filtered with a median filter show regular, block-like oscillations that are due to the cycling of the reciprocating chiller that serves one of the air-handling units (Figure 6).
Detection or air-handler faults:
¢ Change in the cycling period, under known conditions, indicates a leaky recirculation damper or a leaky cooling-coil valve;
¢ Fan and pump power measurements are made with a second NILM attached to the motor-control center that powers all the fans and pumps in the building;
¢ Changes in supply fan power at shutdown reveal faults due to pressure sensor offsets and stuck recirculation dampers.
¢ Changes in pump power, if detectable with sufficient accuracy, can be used to detect blockages in cooling coils.
¢ Power oscillations indicate poorly tuned local-loop controllers.

2. Parameter Identification

Our third and last example focuses on wringing the most information out of the high- frequency data collected and analyzed.

¢ Focus on the start-up period of electrically powered equipment. This start-up period can vary in duration from about 0.1 second for instant-start fluorescent lamps to several minutes for variable-speed motor drives. In all cases, the transient behavior of a typical electrical load is strongly influenced by the physical task that the load performs.
¢ Measurement of real power demanded by a variable-speed fan drive in an HVAC system. The drive begins with an "open loop" spin-up to operating speed during the first 40 seconds of operation. From 100 seconds on, the drive is operating under closed loop control as it attempts to regulate the pressure in a distant duct by varying fan speed.
¢ Distinctive transient profiles like those shown in tend to appear even in loads which employ steady-state active wave shaping or power-factor correction, which tends to make reactive loads appear as purely resistive loads in steady state.

CONCLUSION :-

PSR has become a field of growing interest. Several techniques based on artificial intelligence have been proposed to improve power system restoration. These techniques propose the use of the computer as an operator aid instead of the use of predefined operating procedures for restoration. The stressful condition following a blackout and the pressure for achieving a restoration plan in minimum time can lead to misjudgment by system operator. This paper proposes the use of ANN for service restoration plan, since it has generalization capability and high processing speed. The large number of possible faulty conditions and the need to provide a restoration plan in minimum time are arguments in favor of this technique.




REFERENCES :-

1. IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 18, NO. 4, OCTOBER 2003
NEURAL NETWORKS “ CONTROL SYSTEMS ENGINEERING (THRID EDITION) BY I.J.NAGRATH & M.GOPAL
2. http://studentbank.in/report-ARTIFICIAL-...z0j0w7UVIQ
3. Hart, G.W., ``Nonintrusive Appliance Load Monitoring," Proceedings of the IEEE, December 1992, pp. 1870-1891.( file:///http/seminars/nalm2.htm)
4. Norford, L. K. and S. B. Leeb. 1996. "Nonintrusive Electrical Load Monitoring." Energy and Buildings, Vol. 24, pp. 51-64.
5. Abler, C., R. Lepard, S. Shaw, D. Luo, S. Leeb, and L. Norford. 1998. "Instrumentation for High-Performance Nonintrusive Electrical Load Monitoring." ASME J. Solar Energy Engineering.
6. Leeb, S. B. 1993. "A Conjoint Pattern Recognition Approach to Nonintrusive Load Monitoring,'' Ph.D. Dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.
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