Transformer Fault Detection Research Articles

Modeling Transformers with Internal Incipient Faults

Incipient fault detection in transformers can provide early warning of electrical failure and could prevent catastrophic losses. To develop the transformer incipient fault detection technique, a transformer model is required, to simulate internal incipient faults. This paper presents a methodology to model internal incipient winding faults in distribution transformers. These models were implemented by combining deteriorating insulation models with an internal short-circuit fault model. The internal short-circuit fault model was developed using finite element analysis. The deteriorating insulation model, including an aging model and an arcing model connected in parallel, was developed based on the physical behavior of aging insulation and the arcing phenomena occurring when the insulation was severely damaged. The characteristics of the incipient faults from the simulation were compared with those from some potential experimental incipient fault cases. The comparison showed that the experimentally obtained characteristics of terminal behaviors of the faulted transformer were similar to the simulation results from the incipient fault models.

Abductive network model-based diagnosis system for power transformer incipient fault detection

An abductive network model (ANM)-based diagnosis system for power transformers fault detection is presented that enhances the diagnostic accuracy of the power transformer incipient fault. The ANM formulates the diagnosis problem into a hierarchical architecture with several layers of function nodes of simple low-order polynomials. The ANM links the complicated and numerical knowledge relationships of diverse dissolved gas contents in the transformer oil with their corresponding fault types. The proposed ANM has been tested on the Taipower company diagnostic records and compared with the previous fuzzy diagnosis system, artificial neural networks as well as with the conventional method. The test results confirm that the ANM possesses far superior diagnosis accuracy and requires less effort to develop.

A novel fuzzy logic approach to transformer fault diagnosis

Dissolved gas in oil analysis is an well established in-service technique for incipient fault detection in oil-insulated power transformers. A great deal of experience and data in dissolved gas in oil analysis (DGA) is now available within the utilities. Actually, diagnostic interpretations were solely done by human experts using past knowledge and standard techniques such as the ratio method. In this paper, a novel fuzzy logic approach is adopted to develop a computer based intelligent interpretation of transformer faults using Visual basic and C/sup ++/ programming. The proposed fuzzy logic based software as been tested and tuned using over 800 dissolved gas in oil analysis (DGA) case histories. This highly reliable tool has then been utilized in detection and verification of 20 transformer faults. The proposed diagnostic tool is very useful to both expert and novice engineers in DGA result interpretation.

Identification of power transformer models from frequency response data: A case study

A recent frequency-domain, subspace-based algorithm as well as the well-known nonlinear least-squares algorithm are used in the identification of a power transformer whose frequency response has a dynamic range of 1 MHz. When the model complexity is not restricted, both the algorithms produce highly accurate models. Low-complexity models are extracted from the high-order identified ones via the method of balanced truncation. It is observed that this two-step procedure yields more accurate results than an approach of direct identification of a low-order model. The utility of identified models for the purpose of transformer fault detection is also briefly discussed.

Short-time Fourier and wavelet transforms for fault detection in power transformers during impulse tests

A novel approach using the short-time Fourier transform and wavelet transform (time–frequency analysis tools) for fault detection during impulse testing of power transformers is described. The neutral and/or capacitively transferred currents which are recorded during an impulse test can be directly analysed with this approach. These currents are considered to be evolving in time, i.e. as nonstationary signals, especially when there is a fault. Results from simulation studies are presented wherein the fault condition is modelled as a fast decaying transient superposed on the neutral current. A comparison of the two transforms is made to assess their ability to detect as small a fault as possible and other implemenational issues. Advantages of these methods over the conventional transfer-function method are demonstrated, and it appears that the wavelet transform is better suited for this task.

Multiresolution signal decomposition: a new tool for fault detection in power transformers during impulse tests

Detection of major faults in power transformers during impulse tests has never been an issue, but is rather difficult when only a minor fault, say a sparkover between adjacent coils or turns and lasting for a few microseconds, occurs. However, detection of such a type of fault is very important to avoid any catastrophic situation. In this paper, the authors propose a new and powerful method capable of detecting minor incipient faults. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given faulty neutral current response into other signals which represent a smoothed and detailed version of the original. The decomposition is performed by the multiresolution signal decomposition technique. Preliminary simulation work demonstrated here shows that the proposed method is robust and far superior to other existing methods to resolve such types of faults.

Analysis of integral method for fault detection in transformers

Test results obtained from using the integral method in transformer differential protection against internal fault currents are presented. The effect of various factors on the transient waveforms is considered, and conditions to predict the magnetizing inrush current and a faulty system are digitally simulated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>