Discrimination of Inter-turn Faults from Magnetizing Inrush Currents in Transformers : A Wavelet Transform Approach (original) (raw)
Related papers
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/application-of-signal-analysis-for-fault-diagnosis-in-transformer-by-discrete-wavelet-transform https://www.ijert.org/research/application-of-signal-analysis-for-fault-diagnosis-in-transformer-by-discrete-wavelet-transform-IJERTV2IS2333.pdf Traditional protection gives only terminal condition on the basis of protection of transformer. Discrimination between an internal fault and a magnetizing inrush current has long been recognized as a challenge for power transformer protection. To characterize and discriminate the transient arising from magnetization and inter-turn faults are presented here. This characterization will give value added information for improving protection algorithm.. The detection method can provide information to predict fault ahead in time so as that necessary corrective actions are taken to prevent outages and reduce down time. The data is taken from different test results like normal (magnetization) and abnormal (inter-turn fault) in this work, Discrete Wavelet Transform concept is used. Feature extraction and method of discrimination between transformer magnetization and fault current is derived by Discrete Wavelet Transform (DWT) Tests are performed on 2KVA, 230/230Volt custom built single phase transformer. The results are found using Discrete and conclusion presented. Index Terms-Inrush current, internal fault, second harmonic component power transformer, wavelet transform.
Transmission of the power is the major challenge as utilities located at the remote places, hence proper functioning of power transformer is to be maintained. Many times, transformer encounters with no-fault condition such as inrush current, which results in unwanted tripping of transformer. So, it is necessary to discriminate fault and no-fault condition on transformer, so that unwanted tripping of circuit breaker can be avoided and uninterrupted service can be maintained. This paper, present a technique based on the Continuous Wavelet Transform (CWT) to differentiate among faulted and un-faulted condition on power transformer. Transformer modeling and simulation of faulty and healthy condition are carried out using MATLAB/Simulink software. By employing CWT, suitable features are extracted as the waveform resulted from the faulty condition, and healthy condition are different in nature, after first zero crossing of fault current and Inrush current.
Detection and localization of turn to turn fault of a transformer using wavelets
2017
Transformers have become an important and efficient element of the power system. A fault or damage in the transformer windings will result in outage and heavy loss, as a result it needs to be monitored continuously. Due to the adaptation of various advanced techniques in monitoring and diagnostics of power system it has contributed a major hand in the research. The most common fault in the transformer (or in any electrical machine in general), is turn to turn fault in windings of transformer, and it is a type of incipient fault which cannot be detected easily as the magnitude of fault current is very nominal. This paper presents an approach to simulated model of turn to turn fault and its detection in a transformer by analyzing the neutral current of transformer after subjecting it to a standard impulse wave and subsequently applying wavelet transform. The identification of faults by using frequency or time domain based method is difficult. The Wavelet transform is most suitable met...
2014
This paper presents the characterization of fault transient in transformer using Continuous wavelet transform (CWT). This characterization will add the diagnostic of internal fault in transformer. The detection method can provide information of internal turns to turns fault in winding. CWT analysis provides discrimination of inrush current and fault from terminal parameter. This will add advance concept of on line monitoring of transformer from terminal
ADRRI JOURNAL OF ENGINEERING AND TECHNOLOGY, 2021
This paper presents a technique for detecting and locating transformer inter-turn faults. The technique uses transformer secondary voltages and currents as inputs. It employs continuous wavelet transform (CWT) for input data processing and a trained convolutional neural network (CNN) as a decision tool. The processing of input data using the CWT results in six scalogram images. The six scalogram images are normalized and concatenated into a single image. The concatenated image is then fed into a trained CNN which indicates the occurrence or otherwise of an inter-turn fault. When an inter-turn fault is detected, the magnitude/severity of the fault (defined by the percentage turns affected) as well as the affected phase is determined. The technique was tested using simulations carried out on a 630kVA, 10.5kV/0.4kV, three-phase transformer which was modelled using MATLAB-Simulink. Test results show that the technique accurately detects and locates inter-turn faults. Furthermore, it can be integrated into existing numerical relays, for enhanced protection of transformers.