Artificial Neural Networks Based Power System Restoration
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21-09-2008, 09:50 AM

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 human?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..
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19-01-2009, 12:29 AM

plz do provide some info. on this topic
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29-01-2011, 10:48 AM

sir how can we apply this??
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30-01-2011, 08:30 PM

hi sir pls send information about this topic
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please go through the following thread for more on Artificial Neural Networks Based Power System Restoration.
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09-03-2012, 01:07 PM

Do you have the PPt on "artificial neural networks based power system restoration" , Please help me , Thanks in advance
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17-08-2012, 10:19 AM

Artificial Neural Networks Based Power System Restoration

.doc   ARTIFICIAL NEURAL NETWORK.doc (Size: 87.5 KB / Downloads: 22)


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.


Artificial Neural Network (ANN) is a system loosely modeled on human brain. It tries to obtain a performance similar to that of human’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.


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.


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.


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.


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.


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.


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