S. Santhi

Bio: S. Santhi is an academic researcher from Annamalai University. The author has contributed to research in topics: Short-circuit test & Transformer. The author has an hindex of 5, co-authored 20 publications receiving 83 citations. Previous affiliations of S. Santhi include Indian Institute of Technology Madras & Indian Institutes of Technology.

Real-Time Techniques to Measure Winding Displacement in Transformers During Short-Circuit Tests

Abstract: The ability of a transformer to withstand the dynamic effects of a short circuit test is conventionally evaluated by a measurement of short circuit reactance before and after the test. We propose new techniques for fault detection based on continual assessment during the test. The methods are based on the comparative null method for accuracy measurement in instrument transformers. They include voltage comparison, current comparison and real/reactive power measurements. High resolution sampling based acquisition systems are used to compute changes on a cycle-to-cycle basis. The proposed methods are validated on a number of models including a voltage transformer and a distribution transformer winding. They would be useful for designers in assessing events leading to failure in a more transparent manner.

Continual Assessment of Winding Deformation during a Short Circuit Test

Abstract: A new approach for the continual assessment of winding deformation during short circuit test is proposed. The method is validated through FEM analysis of axially unbalanced axisymmetric configurations. Simulation results are shown to validate the proposed method.

Detection of Winding Deformations During Short Time Current Tests

Abstract: Transformers are required to demonstrate the ability to withstand short circuit currents. Over currents due to short circuit can cause winding deformation. A new method of testing and analyzing winding deformation online without untanking is presented in this paper. A study of alternate excitations to detect winding deformation is made. A comparison of Fourier transform and Wigner-Ville distribution methods of analysis is carried out

Measurement of axial leakage flux for the detection of winding deformation in transformer

Abstract: This paper aims at describing a method for the detection of winding deformation in transformer based on the measurement of axial leakage flux. Theoretical background for the proposed method is presented from the fundamental principles. The method is validated through experiments conducted on a specially designed jumping ring model that demonstrates winding deformation in a simple and an effective manner.

Time Frequency Analysis Method for the Detection of Winding Deformation in Transformer

Abstract: An approach for the use of Wigner-Ville Distribution (WVD), a time frequency analysis method, for the detection of winding deformation during short circuit test of a transformer is presented. The method is validated through experiments conducted on a specially designed 6.6 kV voltage transformer. The results of time frequency analysis approach using WVD and time scale analysis using Wavelet Transform (WT) are compared to the results obtained with the frequency response analysis method to identify an optimal signal analysis tool possessing improved sensitivity for the detection of winding deformation in transformers during short circuit tests.

Advanced transformer winding deformation diagnosis: moving from off-line to on-line

Abstract: On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in last decade. This study has concentrated on issues arising while on-line transformer winding deformation diagnosis is going to be applied on transformers with various kinds of techniques. From technical perspective, before replacing off-line methods by on-line methods and eventually by intelligent approaches, practical challenges must be addressed and overcome. Hence, available off-line transformer winding deformation diagnosis methods are discussed precisely. Mathematical calculation in on-line short circuit impedance measurement is investigated. On-line transformer transfer function measurement setup is presented. A profound insight to the problems pertaining on-line transformer winding deformation recognition methods, characterizes existing online methods, explains the concepts behind online measurements and striving to open the discussion doors towards challenges are discussed. In the end a 400 MVA step up transformer has been taken as a case in order to clarify the capability of Frequency Response Analysis (FRA) method in fault detection while short circuit impedance could only demonstrate some rough understanding about transformer condition.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Abstract: Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

Real-Time Techniques to Measure Winding Displacement in Transformers During Short-Circuit Tests

Abstract: The ability of a transformer to withstand the dynamic effects of a short circuit test is conventionally evaluated by a measurement of short circuit reactance before and after the test. We propose new techniques for fault detection based on continual assessment during the test. The methods are based on the comparative null method for accuracy measurement in instrument transformers. They include voltage comparison, current comparison and real/reactive power measurements. High resolution sampling based acquisition systems are used to compute changes on a cycle-to-cycle basis. The proposed methods are validated on a number of models including a voltage transformer and a distribution transformer winding. They would be useful for designers in assessing events leading to failure in a more transparent manner.

Transformer Fault Diagnosis Using Self-Powered RFID Sensor and Deep Learning Approach

Abstract: This paper introduces a novel fault diagnosis approach for transformer based on self-powered radio-frequency identification (RFID) sensor and deep learning technique. The exploited RFID sensor tag with functionalities of signal collection, data storage, and wireless transmission employs surrounding electromagnetic field as power source. A customized power management circuit, including ac–dc converter, supercapacitor, and its corresponding charging circuit, is presented to guarantee constant dc power for the sensor tag. The measured vibration signal contains miscellaneous noises and is characterized as nonlinearity and nonstationarity, so it is difficult to extract robust and useful features by using traditional feature extraction approaches. As one of the deep learning techniques, stacked denoising autoencoder (SDA) shows satisfactory performance in learning robust features from complex signal. Hence, in this paper, SDA approach is employed to learn robust and discriminative features from measured signals. The experimental results show that the presented power supply can generate 2.5-V dc voltage, which is the rated operating voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.3 m in the test environment. Furthermore, the SDA approach shows satisfactory performance in learning robust and discriminative features. Experimental results indicate that the presented approach is effective and time-saving in terms of fault diagnosis for transformer winding and core.

Flux-based method to diagnose and identify the location of turn-to-turn faults in transformers

Abstract: Internal turn-to-turn faults (TTF) are considered as a common cause of transformer failures. Such defects (especially minor TTFs) may result in minor changes in terminal currents/voltages as these are undetectable by conventional current and/or voltage-based methods. In this study, a simple, sensitive and robust method is proposed to identify the TTF locations in power transformers by using core flux based technique. In this approach, a few turns must be wrapped around the transformer core legs to sense and detect the related core flux. Passing asymmetrical flux through a transformer core leg (due to a TTF) induces different voltages in the related sensors, which are located at different places. Variation of the core flux in the corresponding sensors indicates the faulty phase and the TTF location on that phase as well. The proposed technique is verified by some simulations and experimental tests. The obtained results show that the proposed technique successfully (i) detects all the TTFs, (ii) identifies the faulty phase, and (iii) specifies the faulty region on the related phase. Although the proposed method requires a few search coils to be installed on the transformer core legs, installing these thin wires will not change the transformer design significantly.