No abstract

Dissolved Gas Analysis (DGA) of liquid insulation is an effective means for diagnosing power transformers. The gas contents in insulating oil can be gathered on-line and off-line to indicate the health condition of the transformers, thereafter there are many interpretations of the gas contents. In this work, Seven-fuzzy interpretation modules are individually established, tested and lately combined to monitor power transformers' health. The developed method incorporates trending of the concentration of the dissolved gases over the operating life. The approach processes current and/or historical DGA data, using the 7developed logic modules, to determine the current state of a transformer, provide information regarding the fault type, fault probability, fault severity and recommended future sampling interval in addition to operating procedure, consistent with industry standards. The developed diagnosis system has been validated using 1290 samples from fresh and previously tested mineral oil filled transformers. The proposed system is proved, based on field data, to be 99% accurate in identifying transformers being in normal or abnormal operation. For the cases where a transformer fault was known, the proposed technique has less than 2% inaccuracy in recognizing the fault's type in comparison to other approaches discussed in literature.

Transformers are an important part of any electrical power system network, warning of fault before failure is vital. The application of on-line fault detection reduces the cost of outages and the possibility of unpredicted failures. This paper outlines an electrically based on-line condition monitoring and short circuit detection method for power transformers. This method uses measurements of voltage and current in both sides of a transformer to classify its health and to identify fault conditions while in operation. Simulation results, relating to a number of short circuit winding faults, are analyzed and discussed. The results confirm that it is possible to identify a variety of short circuit conditions in primary and secondary windings from externally measurable parameters, i.e. measurement of voltages and currents in a transformer's windings can, based on available winding data, identify changes inside the transformer. The effect of load variation on the magnitude of the measured voltages and currents is also considered in this analysis.

Transformers play a vital role in the electrical power system network, loss of a power transformer has severe consequences on both the utility and customers depending on how long time it is out-of-service. Internal faults are said to be the most likely cause of disruption in transformer's performance and, consequently, interruption of power supplied. Developing an on-line method to monitor and investigate the health of a transformer will help asset managers to assess infrastructure while it is operational, leading to reduced running costs and increased component life. This research develops methods of on-line and on-going condition monitoring of power transformers based on monitoring the transformer signatures (voltage and current on both sides of the transformer), winding and core temperature. An experimental study has been conducted to investigate the behaviour of a transformer under healthy and unhealthy conditions. A transformer designed to provide access to sections/turns terminals was used to generate short circuit test in different locations during operational time, thermocouples are distributed across the transformer windings' discs in order to record their temperature during normal and abnormal operation. The results confirm the possibility of, in real time, detecting the presence of a short circuit fault, indicating its location and its severity. The correlation between the measured variables indicate the magnitude of the circulating current, thus the fault severity can be classified. The transformer temperature may also indicate the faulty windings and moreover which disc in each winding is short circuited.

Loss of a power transformer in a utility, generation plant or process can cost many millions of pounds, depending on how long it is out-of-service. Internal faults are said to be the most likely cause of disruption in transformer performance and interruption of power supply. Improving understanding of the relationship between types of problem in transformers and their indicators will help to identify internal faults and their locations. Developing an on-line method to monitor and investigate the health conditions of the transformer will help asset managers assess plant while it is operational, leading to reduced running costs and increased life. The proposed method is based on measuring electrical parameters at both sides of a transformer to differentiate between healthy and faulty conditions while it is still in service. In this study, two windings of five sections each have been simulated. The values for each section’s resistance and impedance are calculated based on copper windings, interleaved construction and insulation between sections. The simulated power transformer is connected to an inductive load. Comparison of simulated input and output voltages and currents has been conducted to identify indicators of the transformer’s health status. Developed faults at different locations in the transformer windings are used to study the transformer performance and to recognise the fault indicators. The simulation results show very clearly that there are trends in the measured parameters that are attributed to the type of fault within the transformer. Hence a simple logic comparator can be easily deployed to identify the fault location in relation to the transformer health status.