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Download this complete Project material titled; Power System Restoration Using Artificial Neural Networks with abstract, chapters 1-5, references, and questionnaire. Preview Abstract or chapter one below

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CHAPTER ONE

INTRODUCTION

 

  • BACKGROUND OF THE STUDY

In the past several decades, there has been a rapid growth in the power grid all over the world which eventually led to the installation of a huge number of new transmission and distribution lines. Moreover, the introduction of new marketing concepts such as deregulation has increased the need for reliable and uninterrupted supply of electric power to the end users who are very sensitive to power outages [1].

 

One of the most important factors that hinder the continuous supply of electricity and power is a fault in the power system [2]. Any abnormal flow of current in a power system’s components can lead to a fault in the power system. These faults cannot be completely avoided since a portion of these faults also occur due to natural reasons which are always beyond the control of mankind. Hence, it is very important to have a well-coordinated protection system that detects any kind of abnormal flow of current in the power system, identifies the type of fault and then accurately locates the position of the fault in the power system. The faults are usually taken care of by devices that detect the occurrence of a fault and eventually isolate the faulted section from the rest of the power system.

Hence some of the important challenges for the incessant supply of power are detection, classification and location of faults [3]. Faults can be of various types namely transient, persistent, symmetric or asymmetric faults and the fault detection process for each of these faults is distinctly unique in the sense, there is no one universal fault location technique for all these kinds of faults.

 

The High Voltage Transmission Lines (that transmit the power generated at the generating plant to the high voltage substations) are more prone to the occurrence of a fault than the local distribution lines (that transmit the power from the substation to the commercial and residential customers) because there is no insulation around the transmission line cables unlike the distribution lines. The reason for the occurrence of a fault on a transmission line can be due to several reasons such as a momentary tree contact, a bird or an animal contact or due to other natural reasons such as thunderstorms or lightning. Most of the research done in the field of protective relaying of power systems concentrates on transmission line fault protection due to the fact that transmission lines are relatively very long and can run through various geographical terrain and hence it can take anything from a few minutes to several hours to physically check the line for faults [4].

 

The automatic location of faults can greatly enhance the systems reliability because the faster we restore power, the more money and valuable time we save. Hence, many utilities are implementing fault locating devices in their power quality monitoring systems that are equipped with Global Information Systems for easy location of these faults. Fault location techniques can be broadly classified into the following categories

[5]:

  • Impedance measurement based methods
  • Travelling-wave phenomenon based methods
  • High-frequency components of currents and voltages generated by faults based methods
  • Intelligence based methods

From quite a few years, intelligent based methods are being used in the process of fault detection and location. Three major artificial intelligence based techniques that have been widely used in the power and automation industry are [6]:

  • Expert System Techniques
  • Artificial Neural Networks
  • Fuzzy Logic Systems

Among these available techniques, Artificial Neural Networks (ANN) has been used extensively in this thesis for fault location on electric power transmission lines. These ANN based methods do not require a knowledge base for the location of faults unlike the other artificial intelligence based methods [7].

 

Therefore the application of artificial neural network to power system restoration is to make sure of a steady supply of electric power and fault diagnosis in power systems because the importance of electricity in our day to day life has reached such a stage that it is very necessary to protect the power system equipment from damage and to ensure maximum continuity of power supply.

 

1.2       STATEMENT OF PROBLEM

 

Power system blackout is a major problem we face in the country. When they occur, the effects on commerce, industry and everyday life of the general population can be quite severe. Since it is a major part of any successful economic system and development at large, it is important to reduce the economic and social cost of any power system blackout.

 

1.3       OBJECTIVE OF THE STUDY

 

The goal of this thesis is to propose an integrated method to perform each of these tasks using artificial neural networks. A back-propagation based neural network has been used for the purpose of fault detection and another similar one for the purpose of fault classification in transmission lines. To achieve this, we need to design, develop, test and implement a complete strategy for the fault diagnosis in order to restore transmission lines back to service. The first step in the process is fault detection. Once we know that a fault has occurred on the transmission line, the next step is to classify the fault into one of the different categories based on the phases that are faulted. Then, the third step is to pin-point the position of the fault on the transmission line.

 

1.4       SIGNIFICANCE OF THE STUDY

 

With respect to the objectives of this thesis, it will benefitthe Power Holding Company of Nigeriainaspect of effective fault location and restoration of power system transmission lines.

 

1.5       SCOPE OF THE STUDY

 

The bulk of this work is centered on transmission linesof the Nigerian power system. 

1.6       DEFINITION OF TERMS:

 

NEURONS: These are large number of simple processing units.

 

DISTURBANCE: A disturbance is a change in the operating condition or an operating parameter of the system.

 

WEIGHTS: these are the values which are multiplied to each neuron through the process of giving a desired output

CAPACITY:  IT IS RELATED TO THE AMOUNT OF INFORMATION THAT CAN BE STORED IN THE NETWORK AND TO THE NOTION OF COMPLEXITY.

TRANSMISSION LINES: a transmission line is the material medium or structure that forms all or part of a path from one place to another for directing the transmission of energy, such as electromagnetic waves or acoustic waves, as well as electric power transmission. And its components are wires, coaxial cables, dielectric slabs, optical fibers, electric power lines.

 

STEADY STATE STABILITY: this is the ability of systems to maintain synchronism among its various generating stations during the desired range of system loading when there is no periodic disturbance in the system.

 

TRANSIENT STABILITY: the transient stability limit is always below the steady state stability limit because it may have many different values depending upon the nature and the magnitude of the disturbance.

 

LEARNING: is using a set of observations to find a function which solves the task in some optimal sense. Or Learning involves adjustment of synaptic connections that exist between neurons.

 

COST:It is frequently defined as a statistic to which only approximations can be made.

 

SYNAPSE: is a connection between two nerve cells.

 

RESTORATION: to return a thing back to its normal state or condition. In this context of power system restoration, is returning power back to the system after a faulty disturbance.

 

 

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