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Diagnosing an Abnormal Transformer conditions by Using Dissolved Gas AnalysisCheng, Chin-Chen 24 June 2000 (has links)
The main purpose of this thesis is to utilize transformer oil dissolved gas for abnormal conditions diagnose. The thesis also develops an analysis method which based on three different existing theories to obtain fast and accurate new diagnosis procedures. The procedure by using transformer oil dissolved gas can define the types and locations of transformer abnormal parts.
The sample oil from an on-line transformer can give variable and useful data which can be analyzed the total amount of flammable gas in the laboratory. The type of faults of transformer can be diagnosed easily from the data. Also the over-heat uncovered metal parts of the transformer can be identified. Furthermore, the displacement of silicon sheet core caused by vibration can be obtained after disassembling the transformer. This displacement will cause over-heating phenomenon due to eddy current circulation. After improvement of silicon steel structure, the over-heating phenomenon is disappear and the amount of sample oil dissolved gas keep almost constant. The method which is proposed in the thesis improves the fault identification significantly.
A practical transformer rated at 336 MVA combined by three single 23.75kV/345kV transformers have been selected to support the diagnosis program. Periodic collecting sample oil and analysis the total quantity of dissolved gas can diagnose abnormal conditions primarily. The diagnosis program can identify the types and locations of the faults with the diagnosis information. The transformer then can be stripped down for repair and maintenance.
The diagnosis analysis program from the dissolved gas can identify the transformer faults efficiently. The data also can make right decision whether the transformer operating normally or not. To obtain the best maintenance conditions, periodic collecting oil sample and carrying on the analysis in the laboratory is most efficiently method. The method which proposed in the thesis can offer the best maintenance data to secure the transformer operating reliability and safety.
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