Inter-turn fault detection in power transformer using fuzzy logic

One of the main causes of power transformer failures is due to inter turn short circuit faults. It is a challenging problem to the power engineers to detect these faults at an early stage. If these incipient faults are not detected at their inception, they would develop into more severe faults that may result in damage to the transformer. In this paper a physical model of a multiwinding power transformer of 100 MVA, 138/13.8 KV is simulated in a power system using MATLAB/SIMULINK software. Different percentages of turns such as 1%, 3%, 5%, 10%, 15%, and 25% are shorted on primary and secondary sides of the multiwinding transformer to measure the terminals current. The change in the terminals current during fault incidence (inter turn fault) is negligibly small. In order to experience significant changes, negative sequence currents are extracted using symmetrical component approach. The percentage changes in magnitudes of negative sequence currents (%MAG) and the corresponding phase shifts (PS) that occur in the transformer during fault incidence period are evaluated and they are fed as inputs to fuzzy logic. Here fuzzy logic is employed not only to monitor the condition of the transformer but also to improve the sensitivity of the proposed scheme. The two variables (%MAG& PS) are fed as inputs to fuzzy logic. Depending on the data obtained, the inputs are assigned with three membership functions each namely low, medium, and high. A fuzzy inference engine is built with nine fuzzy rules based on the knowledge gained from the system behavior. Here the process of defuzzification is carried out by using centroid calculation method. The output variable is named as transformer condition (TC), Which is assigned with three membership functions such as Incipient fault (IF), Minor fault (MF), and Severe fault (SF).