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This paper proposes an inductively active filtering (IAF) method to comprehensively improve the power quality (PQ) for not only the distribution network (public grid) but also the power-supply system (nonlinear load) connected to the network. At first, a new main-circuit topology for implementing the IAF method is presented, which consists of an inductively filtered transformer and a fully tuned branch controlled by an inverter. Its operating principle and technical features are introduced by comparing with the traditional active filtering method. Based on this, the equivalent circuit is established and by means of mathematical modeling, the unique filtering mechanism of the IAF method is revealed in theory. Further, the control strategy of the FT branch and the impedance coordination for the inductively filtered transformer are designed based on theoretical analysis. A case study is investigated in detail to illustrate the operating characteristics of the IAF method. Both the theoretical and the case studies show that the IAF method can effectively prevent harmonic components from flowing into the primary (grid) winding of the transformer. Since the harmonic components are suppressed near the harmonic source, it is good for the power-supply system and especially good for the converter transformer. Besides, since the harmonic flow is limited to near the harmonic source, the PQ of the public network can be guaranteed completely. [ABSTRACT FROM AUTHOR]

The power transformer is a key element in a power system and its condition needs to be monitored and evaluated. However, subject to electrical, thermal, and mechanical stresses, the condition of a power transformer can eventually deteriorate causing the loss of the transformer's useful life. Utilizing various condition monitoring data of the transformer, this paper applies a state-space model method to the transformer's remaining useful life estimation. In the state-space model, a state dynamic equation considering the transformer aging mechanism is developed. Three measurement equations using different types of condition monitoring data are established. To solve the nonlinear state-space model, a particle filtering approach is applied. The posterior probability density function of the state variable obtained from the particle filtering is used to determine the transformer's remaining useful life. A number of case studies are carried out to demonstrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Vibro-acoustic measurement on a transformer's on-load tap changer (OLTC) can provide indications on its mechanical condition. Recently, a joint vibro-acoustic and arcing measurement system has been proposed, which can correlate the vibro-acoustic signal to mechanical events of OLTC's operation. However, there are still considerable difficulties in extracting useful information from both vibro-acoustic signal and arcing signal in a synchronized manner without any distortions in the extracted signals. In this paper, Savitzky–Golay filter is introduced to process the signals acquired from a joint vibro-acoustic and arcing measurement system installed on in-service OLTCs. It proves that the Savitzky–Golay filter can process both vibro-acoustic and arcing signals induced by OLTC, extract essential information without any time delay from both types of signals, and retrieve voltage phase information from the arcing signal. The methodologies developed in this paper can improve the visibility of OLTC's mechanical operation for an effective online condition monitoring. [ABSTRACT FROM PUBLISHER]

Vibration measurement has been adopted in some utilities in Australia, Canada, and several European countries for online condition monitoring of the power transformer's onload tap changer (OLTC). By comparing measured vibration signals at different stages, it might be possible to assess changes of OLTC's condition. However, there are still considerable difficulties in correlating vibration signals to the events of OLTC operation, which may impair the capability of vibration measurement for OLTC condition monitoring. Therefore, arcing measurement is proposed in this paper to complement the vibration measurement. Arcing is provoked when the OLTC switching contact closes at a fixed tap position and can lead to electromagnetic signals flowing through transformer windings, finally reaching earth. The arcing measurement is achieved by using a high-frequency current transducer (HFCT) clamping on the transformer's grounding cable. The joint vibration and arcing measurement can provide a better means for interpreting events involved in OLTC operation and facilitating improved OLTC condition monitoring. Since HFCT measured arcing signals can be coupled with noise, a probabilistic wavelet transform is thus employed in this paper to extract arcing signals from noise. Field measurements on two different types of OLTCs are performed using the joint vibration and arcing measurement system to validate the proposed methodology. [ABSTRACT FROM PUBLISHER]

Partial discharge (PD) signals acquired from on-line measurements of power transformers are easily overwhelmed by various interference and noise. This paper proposes an automatic blind equalization (BE) and morphological thresholding method for PD signal de-noising. Firstly, BE automatically selects an equalized signal that reveals PD impulses from an acquired noise-corrupted signal. Then, automatic morphological thresholding (AMT) is adopted for determining thresholds on the equalized signal. After de-noising with BE and AMT, phase-resolved pulse sequence (PRPS) is constructed and used for analyzing the types of insulation defects that cause discharges. To verify the proposed method, PD measurements on experimental PD models and a distribution transformer have been conducted. The results show that PD impulses can be extracted from severely noise-corrupted signals by using the proposed method. Also, PRPS constructed from de-noised signals can achieve consistency in revealing the types of insulation defects even different types of PD sensors and measurement systems are used. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]