Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults

Abstract

Abstract:Monitoring of winding faults is the most important item used to determine the maintenance status of a transformer. Commonly used methods for winding-fault diagnosis require the transformer to exit operation before testing and an external exciting signal, whether the transformer is malfunctioning or not. However, if an overvoltage signal can be regarded as a broadband excitation source for fault diagnosis, then the interference caused by signal injection can be eliminated without the need for additional pulse or impulse signals. In this paper, a tapped transformer is designed and test platforms are built to compare winding diagnoses using the impulse wave and sweep frequency response analysis methods by recording voltage responses on both the high-and low-voltage sides and calculating the respective transfer functions. Based on comparison of statistical indicators, it is found that the sensitivities of both methods are similar for detecting conditions of winding-ground and winding-interlayer short circuits. It is concluded that it is feasible to use a transient overvoltage monitoring system for winding-fault diagnosis.

Partial Discharge Analysis in High-Frequency Transformer Based on High-Frequency Current Transducer

“…Insulating cardboard coordination has been adopted as the insulation for transformers [27], and the hot spots (the most likely point of suspension discharge) were in the winding of HF transformers [28]. Therefore, a suspension discharge model under insulating cardboard was designed to imitate the suspended discharge of a high-frequency transformer winding [29], as shown in Figure 2. In the test platform, voltage output was provided to suspend the discharge model in series with a resistance of 10 MΩ by the power supply (CTP2000, Suman, Nanjing, China).…”

“…EMD is based on the Hilbert-Huang transform. The Hilbert-Huang transform assumes that all data contain different simple internal oscillation modes called intrinsic mode functions (IMFs) [29]. In this way, complex data are superimposed by many different IMFs whose amplitude and frequency vary as a function of time.…”

Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

“…The transformer manufacturer also produced some windings with variable deformations, which are used to replace the middle 10-disk windings to simulate the winding radial deformation (RD) faults. Despite the fact that the RD windings are not the windings in which the deformation is directly produced in the replaced healthy In the online IFRA test, both the excitation nanosecond pulse signals and the response pulse signals of windings are simultaneously recorded to construct the IFRA signature of a transformer to estimate the status of windings, as expressed in Equations (1)-(3) [28][29][30].…”

“…More detailed information about the diagnostic system and the practical application can be found in [19,27]. In the online IFRA test, both the excitation nanosecond pulse signals and the response pulse signals of windings are simultaneously recorded to construct the IFRA signature of a transformer to estimate the status of windings, as expressed in Equations (1)-(3) [28][29][30]. where Vin(n) is the N points sampling signal of the time domain excitation voltage, Vin(k) is the fast Fourier transformation of Vin(n); Rout(n) is the N points sampling signal of the time domain response voltage/current; Rout(k) is the fast Fourier transformation of Rout(n); the H(f) is the online impulse frequency response signature (amplitude versus frequency).…”

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

“…To analyze the characteristics of different PD types, PD signals of different models are extracted in the laboratory. According to the inner insulation structure of power transformers [28,29], there are four possible different PD types, including floating discharge (FD), needle-plate discharge (ND), surface discharge (SD), and air-gap discharge (AD). PD models are shown in Figure 2.…”