الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Partial discharge is a natural phenomenon occurring in electrical insulation systems, and gives rise to a variety complex shapes and surfaces. Partial discharge is the commencement of failure of insulation of electrical devices used at high voltage. If such problems are not detected and repaired, the strength and frequency of PDs increases and eventually leads to the catastrophic failure in the power system equipment, which can cause external equipment damage, fires and loss of revenue due to an unscheduled outage. Basically partial discharge is the cause of failure of insulation in electrical equipment. If PD is found in insulating systems, then, in many cases, it is important to identify its character, i.e., internal discharges, surface discharges, Corona, etc. Such information is vital for the manufacturer, the test institute, or the user of electrical equipment. Partial Discharge (PD) diagnosis is a recognized technique to detect defects within high voltage insulation in power system equipment. A variety of methods exist to capture the signals that are emitted during PD. Partial discharge (PD) pattern recognition is an important tool in HV insulation diagnosis. For many years recognition was performed by eye, i.e. by observation of PD patterns on an oscilloscope screen. Different display techniques appeared to support the evaluation of PD measurements, the popularly used method is the 3-D representation (n-¢- q) of the relationship between the number of discharge, the phase angle of discharges event, and the discharge magnitude. Number of approaches and classification methods has gradually appeared for the automation of discharge recognition: (expert systems, hidden Markov models, neural networks, statistical parameters, fractal features). The purpose of this work is the discriminating between different sources of partial discharge. These PD sources are due to artificially introduced defects within carefully designed insulation models. Fractals have been very successfully used to address the problem of modeling and to provide a description of naturally occurring phenomena and shapes. In recent years, this technique has attracted increased attention for classification of textures and objects present in images and natural scenes, and for modeling complex physical processes. PD also is a natural phenomenon occurring in electrical insulation systems, which invariably contain tiny defects and non-uniformities, and gives rise to a variety of complex shapes and surfaces, both in a physical sense as well as in the shape of 3-D PD patterns acquired using digital PD detector. This complex nature of the PD pattern shapes and the ability of fractal geometry to model complex shapes is the main reason which encouraged the authors to make an attempt to study its feasibility for PD pattern interpretation. A PD pattern recognition approach of artificial partial discharge sources based on neural networks is proposed in this work. A commercial PD detector is used to measure the three-dimension PD patterns. Two fractal features (fractal dimension and lacunarity) were extracted from the raw PD patterns The box counting technique is used to generate the fractal features. Fractal dimension is a constant value for each pattern. On the other hand, each pattern has several values for lacunarity depends upon the size of the box. Therefore each PD pattern is characterized by several quantities. The performance of neural networks based recognition system depends upon the number of input features as well as upon their contents of information This work is an attempt to improve the performance of PD recognition systems by minimizing number of input features. This can be done through selecting the value of lacunarity which has the maximum ability to classify different PD sources. A wide range of lacunarity values were tested. The obtained results show that a very narrow range of lacunarity values has the maximum ability for PD classification.