الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 150 |  |  |
| | | from | 150 | | |
Abstract
Power transformers are the most expensive and important equipment in a high voltage electric power system due to its power transformation functions. They serve as a vital link between power plants and the end of distribution electric power system. Therefore, any failure of power transformers affects the stability and reliability of energy delivery of the whole power system and would lead to widespread blackout which increases the deterioration of economic and personal losses if left unnoticed. Hence, condition assessment of power transformers is a necessary task. It is a well-accepted fact that the remnant useful life of the transformer paper insulation determines its useful operational life. Therefore, the diagnosis of the incipient fault types of the transformer is closely related to the condition assessment of the transformer’s insulation. Thus, reliable and economic transformer’s insulation condition monitoring and diagnostic techniques are necessary to conduct a comprehensive and efficient transformer condition assessment for appropriate operation and maintenance decisions.
Dissolved Gas Analysis (DGA) is one of the most effective methods in condition monitoring and fault diagnosis of oil immersed transformers. It provides useful information about the condition of the oil and helps to identify the incipient fault types within the transformer. It uses the concentrations of various dissolved gases in the transformer oil due to decomposition of the oil and paper insulation. DGA has gained worldwide acceptance as a technique for the detection of incipient faults in transformers. Due to the thermal and electrical stresses that the insulation of operating transformers experience, paper and oil decomposition occurs, generating gases that dissolve in the oil and reduce its dielectric strength. Gases generated through oil decomposition include hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), and ethane (C2H6). On the other hand, carbon monoxide (CO) and carbon dioxide (CO2) are generated as a result of paper decomposition. Through the analysis of the type, amount and gassing rate of generated gases, various techniques for interpretation of DGA data have been developed, e.g., the key gas technique, Doernenburg technique, Rogers technique, IEC technique, Duval triangle technique and artificial intelligence based techniques to diagnose incipient faults based on DGA.
The conventional ratio techniques use specific ratios of dissolved gas concentrations to diagnose the incipient faults according to specific codes. The ratio techniques were first proposed by Doernenburg and then were modified by Rogers before they were revised in IEC standard 60599. The codes are generated by calculating ratios of gas concentrations and comparing the ratios with predefined values derived by experience and continually modified. Diagnosis is done when a code combination matches with the code pattern of the fault type. The major drawback of ratio techniques is the problem of no decision associated with some cases that lie out of the specified codes. On the other hand, Duval triangle technique is characterized by simplicity in application and accuracy in evaluating the fault types and their severity; but the essential drawback of Duval is that it uses only three hydrocarbon gases (methane, ethylene, acetylene) and did not consider the concentrations of hydrogen (H2) and ethane (C2H6) in spite of their importance in diagnosing certain fault types especially low-temperature thermal and partial discharge faults. On the other hand, in recent years, artificial intelligence (AI) based techniques have been extensively studied by many researchers for transformer fault diagnosis. These techniques include expert system, fuzzy logic, artificial neural network or hybrid system. AI-based techniques are constructed either upon a knowledge base or upon a training from DGA data. However, these techniques are too complicated for practical implementation on a wide range. In addition, they are highly dependent on the training data set.
To mitigate these drawbacks, this thesis proposes three diagnostic techniques; heptagon graph technique, three ratios technique (TRT) and composite triangle technique (CTT), for analyzing the concentration of dissolved gases and interpreting their results in detecting and evaluating the incipient fault condition of oil-immersed transformers. The overall accuracy, fault type zone boundaries and each gas ratio development of these proposed techniques have been determined based on a large number of cases visually inspected in transformers over the last 30 years as reported by EEHC, IEC TC 10 and related databases surveyed from actual incident cases of the mineral oil–filled transformers. These dissolved gas analysis databases have been tested to confirm or re-adjust slightly these boundaries of each fault zone with maximum accuracy whenever possible. The classification performance of these proposed DGA techniques is verified in comparison with the other conventional DGA techniques. This comparison showed that the proposed DGA techniques have good diagnostic accuracy. It is found that the proposed TRT ratio technique has 99.21% accuracy, the proposed CTT triangle technique have close accuracy percentage of 99.06% and the heptagon graph technique has the lowest accuracy of the proposed DGA techniques 90.63% as compared with the conventional DGA techniques 81.52% for Duval, 75.61% for Doernenburg, 48.91% for IEC and 40.12% for Rogers when testing 700 cases. As well, the overall percentage of successful predictions of the proposed techniques as compared with conventional DGA techniques based on main faults; Heptagon graph, TRT ratio, and CTT triangle techniques have close accuracy percentages of 99.75%, 99.01% and 98.52% respectively for 700 cases while Duval triangle technique has 82.95% which is the maximum accuracy of the conventional techniques.