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:
Ã‚Â· The limitations encountered in some currently used PSR techniques.
Ã‚Â· 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.
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.
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.
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:
Ã‚Â· Arranging neurons in various layers.
Ã‚Â· Deciding the type of connection among neurons of different layers , as well as among the neurons within a layer.
Ã‚Â· Deciding the way neurons receive input and produces output.
Ã‚Â· 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:
Ã‚Â· Their ability to represent non-linear relations makes them well suited for non-linear modeling in control systems.
Ã‚Â· Adaptation and learning in uncertain system through off line and on line weight adaptation.
Ã‚Â· Parallel processing architecture allows fast processing for large-scale dynamic system.
Ã‚Â· Neural network can handle large number of inputs and can have many outputs.
Ã‚Â· ANNs can store knowledge in a distributed fashion and consequently have a high fault tolerance.
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:
Ã‚Â· 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.
Ã‚Â· 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.
Ã‚Â· 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.
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:
Ã‚Â· Thermal limits of transmission lines
Ã‚Â· Stability limits
Ã‚Â· Number of lines used in the restoration plan
Ã‚Â· Allowable over and under voltage
Ã‚Â· Recognition of locked â€œout 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.
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.
Ã‚Â· IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 18,
NO. 4, OCTOBER 2003
Ã‚Â· NEURAL NETWORKS â€œ CONTROL SYSTEMS ENGINEERING (THRID EDITION) BY I.J.NAGRATH & M.GOPAL
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.
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