Broadband responses under random excitations can reflect the modal characteristics of a structure. This paper investigates the applicability of operational modal analysis (OMA) to extract modal parameters of transformer windings under random excitations. Firstly, random vibration signals are extracted from measured vibration signals by estimating an accurate power frequency and eliminating vibration harmonics. Then, modal characteristics are obtained by applying OMA to the extracted random vibration signals. The numeric study on a winding axial vibration model demonstrates the effectiveness of the proposed method in obtaining its modal characteristics. Laboratory experiments show that the natural frequencies calculated by OMA are a subset of the actual natural frequencies of windings and the tank. The field test results of a 110 kV power transformer show that the number of natural frequencies increase when winding deformation occurs. OMA of transformer windings may pave a way for online winding condition monitoring.

Arc faults threaten the safe operation of photovoltaic (PV) systems. An arc fault detection and localization approach using parallel capacitors is proposed. A PV system has been analyzed and tested with five capacitors paralleled with the branches in the system. Series and parallel arc faults at nine locations have been tested in the system. When an arc occurred, current pulses were generated in the capacitors and their amplitudes and polarities were obtained through Hall current sensors. Discrete wavelet transformation was performed on the capacitor currents and the distributions of their amplitudes, frequency spectrums, and polarities are here reported. The results indicate that the distributions are unique under different fault types and locations, which could be used to detect and localize arc faults in PV systems. Moreover, the amplitudes of the capacitor currents can also help to localize a series arc fault within a PV string. Finally, the proposed approach is validated by a double-fault test.

The large current flowing through transformer windings during external short-circuit event causes strong vibration and the tank emits multi-frequency sound. This paper investigates the transient acoustic signal theoretically and experimentally, and proposes to monitor the winding condition with the spectrum of the acoustic signal. A two-wire model is first built as a basic unit of winding structure. The dynamic equation of the model is constructed based on principle of least action. The solution shows that the spectrum of the winding vibration contains half power frequency and its multiples in some cases. If the winding is loosened and deformed, the motion space and vibration amplitude would be enlarged and the natural frequency of winding would decrease. As a result, the vibration spectrum would contain half frequency and its odd multiples. Then the acoustic signal of a 110 kV transformer is measured on a short-circuit test platform. The acoustic time-frequency spectrums are consistent with the theoretical analysis. The half frequency ratio (HFR) of the acoustic signal is proposed to characterize the winding condition. The results prove that HFR of acoustic signal is a more sensitive indicator than the short-circuit reactance, which can characterize the cumulative process of short-circuit on the winding.

Operating deflection shapes (ODSs) are used for visualizing the vibration patterns of a structure. In this study, ODSs are applied to diagnose the mechanical condition of power transformer windings. First, a synchronous calibration measuring system is developed to simultaneously acquire tank vibration signals and excitation voltage signals. To perform the synchronous calibration, the phase of the excitation voltage signals is set as the reference. Next, the no-load test, load test, and variable load test are conducted on a 50-kVA transformer. The histogram of oriented gradient (HOG) and Pearson correlation coefficient (CC) are proposed to describe the differences between the different ODSs. Experimental results show that the ODSs are insensitive to the operating voltage and current, but sensitive to the clamping pressure of the windings. Field results further verify the significant change in the ODS after winding deformation. Finally, the partial ODS, which is suitable for practical transformers, is proposed to locate winding mechanical defects in power transformers. The proposed method can improve the efficiency and accuracy of the vibration-based mechanical condition monitoring of power transformers.

Winding vibration leads to the oscillation of coils position and further affects winding's nonlinear stiffness as well as electromagnetic forces (EMFs). This paper establishes a winding Mechanical-Electromagnetic Coupling Model (MECM) where the coupling between stiffness coefficient and vibration, magnetic field and vibration are considered, and deploy it in the vibration-based condition monitoring. In this model, EMFs are obtained through the superposition of leakage magnetic field generated by each coil. A novel numerical analysis method is further proposed, which is capable of calculating the vibration response containing nonlinearity and electromechanical coupling, and the accuracy of the method is verified in experiments. Then, the numerical analysis method is used to quantitatively analyze the effects of Nonlinearity and ElectroMechanical Coupling (NEMC) on the vibration characteristics. The study demonstrates the law that NEMC intensity increases with vibration, and the NEMC is obvious especially under mechanical fault. To extract the vibration patterns, Nonlinearity-ElectroMechanical Coupling Coefficient (NEMCC) with amplifying NEMC effects is proposed to monitor the winding mechanical condition. The analytical method predicts the variation trend of the winding looseness condition curve, and laboratory experiments prove that the Mean Absolute Percentage Error (MAPE) is able to successfully identify different degrees of winding looseness faults. Compared with the short-circuit impedance (SCI) method, the MAPE value based on NEMCC has a stronger detection capability and higher sensitivity for winding mechanical faults. This paper lays a foundation for the numerical solution of vibration response and pave a way for winding mechanical condition monitoring.

This article presents an intelligent approach for monitoring various thermal degradations in insulated gate bipolar transistor (IGBT) modules. Thermal degradation at the baseplate solder and thermal interface material (TIM) both induce changes in the case temperature distribution, offering insights into the corresponding degradation type and level. To capture this information, a strategically placed thermocouple array forms a temperature matrix on the case surface. The normalization of the case temperature matrix effectively mitigates the influence of working conditions, such as load current or heatsink temperature. Furthermore, a model augmented with a convolutional neural network is utilized to classify the degradation type and level based on temperature matrixes. To generate datasets under diverse operational conditions, a finite-element model of a conventional IGBT module is developed. The model includes various degradation locations (baseplate solder fatigue, TIM degradation, baseplate solder, and TIM degradation) and levels (health, early degradation, severe degradation, failure). Gaussian noise is introduced to the simulation data to enhance both training and prediction accuracy. Additionally, experimental data from a two-level inverter is incorporated into the training dataset to further refine accuracy. The results affirm that this approach excels in accurately distinguishing the degradation type and evaluating the degradation level.