Ferroresonance Detection Research Articles

Verification tests of a novel ferroresonance detection technique

Ferroresonance is a complex electromagnetic phenomenon that affects voltage transformers. The appearance of the ferroresonant phenomenon provokes important oscillations that are responsible for serious thermal and dielectric overstresses, which may lead to catastrophic failures in the electrical power system. This paper analyzes the behaviour of a novel ferroresonance detection technique, based on artificial neural networks, through the results obtained in several verification tests conducted in a real voltage transformer.

Detection of ferroresonance occurrence in inductive voltage transformers through vibration analysis

Steady state ferroresonances in isolated neutral electrical systems result in sustained abnormal system oscillations, which can damage inductive voltage transformers (IVTs) and other system elements. During this phenomenon, the IVT becomes saturated; consequently, the dimensions of the ferromagnetic material of the IVT change owing to magnetostriction. Because of this inherently nonlinear behaviour, the natural vibration modes of the IVT are excited. The aim of this study is to take advantage of these anomalous vibration modes to reveal the occurrence of ferroresonance in IVTs. The methodology for ferroresonance detection in IVTs through vibration analysis is described and demonstrated experimentally.

Ferroresonance Signal Analysis with Wavelet Transform on 500 kV Transmission Lines Capacitive Voltage Transformers

This paper deals with ferroresonance detection on an transmission line utilizing wavelet transform. The ferroresonance involving linear capacitor and non-linear inductor was triggered by the disturbances of power system, such as lightning strike, switching operation and ground fault. The primary voltage of capacitive voltage divider (CVT) was observed as response of the triggered ferroresonance. The voltage signal was processed using wavelet daubechies (db5) until 9 levels. The cyclic cumulative energy for each condition was then calculated and compared. The result shows that the cyclic cumulative energy ratio and duration of period of cumulative energy deviation can be used as ferroresonance detection parameters.

Continuous wavelet transform for ferroresonance detection in power systems

Wavelet transforms, such as Continuous Wavelet Transform (CWT) create numerous signals based on scale coefficients after decomposition. This entire signal set and their subsets with regard to a chosen function can actually be used to reconstruct redundant representations of the real signal in such a manner that different signal characteristics like anomalies can be detected. This study aims to provide another perspective to wavelet transform studies in terms of the redundancy feature which comes from wavelet type and scale number as well as transform type itself. Consequently, a partial feature construction (PFC) method has been introduced to select the appropriate signal subset which will reconstruct redundant signals for anomaly detection. As a real-world application, ferroresonance data generated by a MATLAB-Simulink model have been used. PFC method has been applied to a subset of decomposed ferroresonance data from CWT, and it has been investigated with different function scenarios. As a result, redundant signals reconstructed by specific functions in PFC have not only allowed detecting ferroresonance phenomenon, but they also made it clearer to observe physical ferroresonance behavior.

Applying Wavelet Transform to Ferroresonance Detection and Protection

Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method. Keywords—Ferroresonance, Wavelet Transform, Intelligent Electronic Device, Transformer.

Research on Ferroresonance Detection in Small Current Neutral Grounding System

A novel method to detect ferroresonance was proposed based on the drastic characteristics of the electromagnetic voltage transformer (PT) current rate. This scheme takes current rate which is greater than a threshold value as starting signal and judges the occurrence of resonance by detecting duty ratio of the peaks and interval angles. Then, it identifies the periods and patterns established on the interval time of spikes. Through simulation testing of MATLAB, it can distinguish the occurrence and types of ferroresonance accurately, which verifies the validity of the detection method.

Identification of ferroresonance based on S-transform and support vector machine

The aim of this paper is to develop a method based on S-transform and Support Vector Machine (SVM) for ferroresonance detection. Using this method ferroresonance can be discriminate from other transients such as capacitor switching, load switching and transformer switching. S-transform (ST) is used for decomposition of signals, feature selection is done by Kernel Principal Component Analysis (KPCA). SVM is used for classification. Ferroresonance data and other transients were obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying ferroresonance from other events.

Identification of Ferroresonance based on wavelet transform and artificial neural network

AbstractA novel method for Ferroresonance detection is presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching and transformer switching. Wavelet transform is used for decomposition of signals and Learning Vector Quantizer(LVQ) neural network used for classification. Ferroresonance data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events. Copyright © 2008 John Wiley & Sons, Ltd.