28-12-2010, 03:54 PM
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INTRODUCTION:-
The power system often comes across some situations when there is insulation failure of equipment or flashover of lines initiated by a lightening stroke or through accidental faulty operation. The system must be protected against flow of heavy currents (which can cause permanent damage to the major equipment) by disconnecting the faulty part of the system by means of the circuit breakers operated by protective relaying. A power system comprises synchronous generators, transformers, lines and loads. So, it is the responsibility of the each and every worker or engineer to detect and repair the faults without damage to the power system. The fault diagnosis of a power system provides an effective means to get information about system restoration and maintenance of the power system. Artificial intelligence has been successfully implemented on fault diagnosis and system monitoring. Expert systems are used by defining rules, for a fault diagnosis. In the present work particularly a new method of “AI” namely “Artificial Neural Network” is used as diagnosing to power system faults.
A study has been made by taking a sample model of power systems. The all possible faults of the system were diagnosed and predicted with the help of “Auto-Configuring Artificial Neural Network” namely “Radial Basis Function Network” and the comprehensive study reveals that the proposed method is more efficient, faster and reliable than any other method used for fault diagnosis of power systems.
Over the last two decades, fault diagnosis (FD) has become an issue of primary importance in modern process automation as it provides a basis for the reliable and safe fundamental design features of many complex engineering systems. A fault diagnosis is required to avoid power loss
in different systems or even loss of human lives. Fault diagnosis aims to provide information for time and location of faults that occur in the supervised process. A system is called a healthy system when it runs free of faults, on the contrary, a faulty system is that having deviations from the normal behaviour of the system or its instrumentations. Fault diagnosis process includes the following tasks fault detection, which indicates that something is going wrong in the system, fault isolation which determines the exact location of a fault and fault identification, which determines the magnitude of a fault severity. Faults are diagnosed by processing the multiple measurements using spectrum analyses or using the logic reasoning approach or comparing the measurement to preset limit values, limit checking approach. The other approach is to develop an intelligent system based on expert knowledge and learning of the power system behaviours. Current trends in the field of Fault Diagnosis of power systems apply Artificial Neural Networks (ANNs) to diagnose faults of some system components such as transmission lines, transformers, synchronous generators, or any other component related to the system. The artificial neural networks (ANNs) have the capability to perform pattern recognition and diagnosis that are difficult to describe in terms of analytical diagnosis algorithms since they can learn input patterns by themselves. Learning can be viewed as an automatic, incremental synthesis of functional mappings that represent a fault function. Unlike adaptation methods, where the emphasis is on approximating temporal properties, learning systems employ networks with large memory for approximating the spatial dependence of the fault function. Therefore, learning methods can be used not only for fault detection but also for identification of characteristics of the fault through approximation of its functional relation to the measurable state and input variables.
WHY ARTIFICIAL NEURAL NETWORK?
Artificial intelligence (AI) is simply the way of making the computer think intelligently. It there by provides a simple, structured approach to designing complex decision-making programs. While designing an AI system, the goal of the system must be kept in mind. There exists a more sophisticated system; which guides the selection of a proper response to a specific situation. This process is known as “Pruning”, as its name suggests eliminates path way of thoughts that are not relevant to the immediate objective of reaching a goal.
Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry. Even simple animal brains are capable of functions that are currently impossible for computers. Computers do rote things well, like keeping ledgers or performing complex math. But computers have trouble recognizing even simple patterns much less generalizing those patterns of the past into actions of the future. Now, advances in biological research promise an initial understanding of the natural thinking mechanism. This research shows that brains store information as patterns. Some of these patterns are very complicated and allow us the ability to recognize individual faces from many different angles. This process of storing information as patterns, utilizing those patterns, and then solving problems encompasses a new field in computing. This field, as mentioned before, does not utilize traditional programming but involves the creation of massively parallel networks and the training of those networks to solve specific problems. This field also utilizes words very different from traditional computing, words like behave, react, self-organize, learn, generalize, and forget.
AI has made a significant impact on power system research. Power system engineers have applied successfully AI methods to power system research problems like energy control, alarm processing, fault diagnosis, system restoration, voltage/var control, etc. for the last couple of years a new AI method namely Artificial Neural Network (ANN) has been used extensively in power system research. In comparison to the AI method, which tries to mimic mental process that takes place in human reasoning, ANN on the other hand tries to stimulate the neural activity that takes place in the human brain. ANN has been successfully applied to economic load dispatch, shot term load forecasting, security analysis, alarm processing, capacitor installation and EMTP problems. An attempt has been made here to solve the fault diagnosis problem in power systems using ANN.
The principal functions of these diagnosis systems are:
1) Detection of fault occurrence
2) Identification of faulted sections
3) Classification of faults into types:
A)HIFs (high impedance faults) or
B)LIFs(low impedance faults)
This has been achieved through a cascade, multilayered ANN structure. Using these FDS accurately identifies HIFs, which are relatively difficult to identify in the other methods.
