V. Jeyabalan

Bio: V. Jeyabalan is an academic researcher. The author has contributed to research in topics: Partial discharge & Current transformer. The author has an hindex of 1, co-authored 1 publications receiving 17 citations.

Statistical Techniques for Partial-Discharge Location in Transformer Windings

Abstract: To locate the partial discharge in transformer windings, statistical techniques are proposed. The experimental studies are performed on a 22-kV prototype interleaved winding to prove the feasibility of the methods.

Partial Discharge Localization Inside Transformer Windings via Fiber-Optic Acoustic Sensor Array

Abstract: Partial discharge (PD) is the main sign of the insulation deterioration; therefore, the online PD localization of the power transformers can provide the equipment degradation characteristics and has great value for the power grid. Because of some unique merits, acoustic PD detection is one of the best options for such applications. However, when discharges happen inside the windings, precise PD localization by acoustic method becomes very challenging. As the acoustic PD signal can be reverberated and distorted along its propagation, the type and number of sensors, with their installation, should be carefully studied and selected. In this paper, the acoustic wave propagation inside the transformer is explored via numerical simulation. Based on the understanding of wave propagation, a novel-structure fiber-optic acoustic sensor array is properly designed and installed into one phase winding, which is from a real 35 kV transformer. Experimental verification shows that, by using this proposed design, PD localization with less than 5-cm error can be achieved. Therefore, the novel sensor array developed in this paper, together with the installation and localization method, unveils a novel access to accurate online PD localization, especially for those PDs which happen inside transformer windings.

Transformer Winding Fault Diagnosis Using Vibration Image and Deep Learning

Abstract: Winding condition assessment is an essential task for operating transformers, and the vibration method provides a low-cost and non-intrusive approach. In this paper, a novel feature extraction method based on vibration analysis is proposed, which converts the vibration monitoring data with load information into a vibration image. Then, a deep learning approach based on convolutional neural network (CNN) is used to classify the images belong to different classes. In the laboratory experiment, free vibration tests are performed on an on-load winding model, which are used to verify the relationship between the natural frequency and the electromagnetic force under different clamping forces. During the field experiment, transformers are divided into three categories, including normal, degraded and anomalous, and the proposed scheme is trained and tested by using the vibration samples acquired from more than 100 operating transformers. The performance of the CNN classifier under different input sizes is investigated, which achieves 98.3% overall accuracy. Besides, the confusion matrices obtained by other methods are compared, such as artificial neural network (ANN), support vector machine (SVM) and naive Bayes classifier (NBC). The results show that the proposed scheme including the vibration image extraction method and the CNN classifier offers superior performance in winding fault diagnosis.

Partial Discharge Localization in a Power Transformer: Methods, Trends, and Future Research

Abstract: This paper presents a comprehensive review of various techniques of partial discharge (PD) localization. The technologies reviewed are acoustic, ultra-high frequency (UHF), optical, and electrical according to their chronological order of development. The paper outlines the distinctive acoustic and UHF sensors, along with the algorithm for the localization of PD source. Various digital signal processing and statistical techniques applied are likewise discussed. A substantial effort has been given to electrical methods since they represent the most active and current field of PD research. In this paper, the techniques of PD localization used so far are compared, and their advantages and limitations are pointed out. The challenges and trends in future research in PD localizations are also discussed. The review given in this paper will be useful to develop the future technologies for PD localization thereby avoiding insulation damage. Thus, this paper is intended to serve as a guide for the research ...

Localization of Partial Discharges Inside a Transformer Winding Using a Ladder Network Constructed From Terminal Measurements

Abstract: Precise localization of partial discharge (PD) inside a power transformer winding is a challenging task. Previously, researchers have used internal calibration or reference signals to locate the PD source. The location of any arbitrary (test) PD source is ascertained from the maximum correlation between reference and test signals. However, in practical transformer windings, internal tappings or design details are usually unavailable to generate reference signals. The proposed work employs terminal measurements to construct a physically realizable ladder network. Simulated responses obtained from the ladder network for signals of known pulse-widths at all locations are used as reference data. The terminal responses of the test PD signals are obtained from a laboratory-scale winding by applying signals of arbitrary pulse-widths and shapes at various locations. The PD test signals are generated using a function generator, a PD calibrator, and real discharges. To predict the location of the PD source, the simulated reference data are then correlated with the test data. The position corresponding to the maximum correlation indicates the PD location. The proposed methodology is verified using experimental investigations carried out on two different laboratory-scale transformer windings.

Optimum acoustic sensor placement for partial discharge allocation in transformers

Abstract: Partial discharges (PDs) are a cause of degradation of insulation system and its permanent activity monitoring is used as a tool for insulation condition assessment in power transformers. Information on PD position can be used by plant monitors to diagnose the cause of PD and can help to identify type and severity of an insulation fault for maintenance strategies. The acoustic PD detection system, with great advantages relative to other methods, contains inaccuracies in PD localisation, such as the multipath interferences due to reflections within transformer tank, improper acoustic coupling between sensor and tank surface, mechanical vibrations and inherent sensor inaccuracies, which can severely limit the accuracy of the positioning system and make locating the exact positions of PD difficult. In this study, a method has been proposed for calculation of position measurement sensitivity for different PD origins within transformer tank. Different possible cases have been considered for placement of acoustic emission (AE) sensors. For each case, sensitivity of PD location to the time measurement errors has been calculated. The optimum arrangement of AE sensors has been chosen as the configuration yielding lowest error in the PD localisation. A test setup has been organised and the experimental results have justified the theoretical analysis.