| The reliability of power transformers, as the major equipment in power systems, directly affects the safety and stability of power system operation. An important measure to improve the operation reliability is the translation from time- maintenance to status maintenance for transformers. The status maintenance of the power transformers is put a important station at home and abroad. Moreover, on-line fault diagnosis for transformer is one of the precondition to carry status maintenance out.In accordance with the technological difficulties encountered in the process of insulation supervision based on the Dissolved Gases Analysis (DGA) , several kinds of method are presented to improve the reliability and precision of fault diagnosis of the power transformer.There are many methods applied to study the faults inside the power transformers, such as the neural network (NN) method, the fuzzy set theory method, the expert system (ES) method, the application, of synthetical artificial intelligence (AI) technique, the transformer fault on-line monitoring technique, etc. However, in order to detect the faults precisely and timely, only the on-line monitoring technique can be used, starting from the study of Dissolved Gases Analysis (DGA), synthesising various artificial intelligence (AI) technique and electric testing parameters.The characteristic gases , such as hydrogen (H2) , methane (CH4) , hexane (C2H6), ethene(C2H4), ethine(C2H2), carbon monoxide (CO), carbon dioxide (C02), etc are produced, when partial heat or discharge takes place in the transformers.The more serious hidden faults become, the faster these gases areproduced. The contents and constituents of oil-dissolved gas can be considered as the character parameter for diagnosing transformer faults. So transformer inner faults can be detected with dissolved gases chromatographic analysis. A amount of carbon monoxide (CO) and carbon dioxide (C02) can give out with solid insulating materials' partial overheat. A quantity of ethene(C2H4) and methane (CRi) can be produced with the oil partial overheat. The main characteristic gases are hydrogen (H2) and ethine(C2H2) for electric arc discharge. Generally, the content of ethine(C2H2) is 20~70% of that of total hydrocarbon, the content of hydrogen (H2) is 30~90% of that of total hydrogen and hydrocarbon. In general, ethine(C2H2) is much more than methane(CH4) .The main characteristic gases are also hydrogen (H2) and ethine(C2H2) for sparkle discharge. Generally, the content of ethine(C2H2) of total hydrocarbon is little.Whichever discharge is , carbon monoxide (CO) and dioxide (C02) can be produced for solid insulating materials faults.The characteristic gas methods determine the fault types with the characteristic gases produced by various transformer faults. The three-ratio methods determine the fault types with three ratioes of the gases content measured with the dissolved gases chromatographic analysis. The four-ratio methods determine the fault types with four ratioes (CH4/H2, C2H6/CH4, C2H4/C2H6, C2H2/C2H4) of five kinds of different gases content.Starting from the study of occurring and resolving theory for gases in transformer oil, the relationships between oil-dissolved gases and the transformer fault types are further analyzed, all of which contribute to the final conclusion that the contents and constituents of oil-dissolved gas can be considered as the character parameter for diagnosing transformer faults. By deeply studying the common transformer faults diagnosing methods, such as character gas methods and three-ratio methods, the diagnosing precision ismuch high, while several shortcomings such as uncertainness judgment when the fault reasons, phenomenon and principles come out together while can not consistent to each other etc. the classical methods can not fully meet the need to engineering practical application.The expert system , artificial neural network, synthetical artificial intelligence (AI) technique are applied to diagnose the transformer faults, Expert system s... |
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