Novel filter based ANN approach for short-circuit faults detection, classification and location in power transmission lines

Existing techniques for recognizing and identifying disturbed signals waveforms are primarily based on visual inspection. This paper proposes a wavelet packet based technique to perform a feature extraction from the disturbance signal and a classification of the extracted features in order to identify the possible causes of the disturbance. The same wavelet based procedure is applied to signal compression and denoising in order to make the EMC analysis of the data easier.

Wavelet packet-based EMI signal processing and source identification

Variable numerous types of equipment/subsystems may exist in complex systems, which makes it difficult to analyze and identify electromagnetic emission sources. A theory named “basic emission waveform theory” is presented in this paper to solve this problem. This innovation theory characterizes emission with four basic waveforms, including square wave, sine wave, damped oscillation, and spike wave. Then, the effectiveness of the theory is discussed in both theoretical and engineering ways. In particular, the basic waveforms reflect the physical characteristics of the equipment. Furthermore, the basic waveforms generally do not vary with the locations or loading conditions. Therefore, the identification and analysis of an emission source can be realized by analyzing the basic waveforms from the emission of a complex system. Finally, the applications of damped oscillation and square wave in emission source identification field are used as validations of the proposed theory. Moreover, an example of emission source identification for an electric vehicle is also presented. The examples show that the proposed theory can be used to simply and accurately identify and analyze the emission sources of complex systems.

Basic Emission Waveform Theory: A Novel Interpretation and Source Identification Method for Electromagnetic Emission of Complex Systems

In this paper, we present a new approach to evaluation of signal integrity that is based on signal energy density as a function of time and frequency, represented by its wavelet scalogram. Using signal integrity ratio and cumulative energy ratio, we illustrate signal integrity analysis with simulated examples, followed by the demonstration of their usefulness through analysis of experimental data of a real audio amplifier. These figures of merit represent the extent to which the integrity of a signal is diminished by the electromagnetic interference effects and/or nonlinear processes.

https://upcommons.upc.edu/bitstream/handle/2117/22719/13434528.pdf;sequence=1

Wavelet-Based Approach to Evaluation of Signal Integrity

Faced with the continual increase in complexity of wiring networks, their reliability, in particular in embedded fields (such as automotive and aerospace industries) becomes a major issue In wiring diagnostics, reflectometry methods are commonly used They are quite efficient for detecting important damage (hard faults) such as short-or open-circuits These faults can have heavy material (eg: fires) and economical consequences Then detecting them before they happen would be very interesting and useful Unfortunately no current method is efficient enough for addressing soft faults This article proposes an original approach to overcome this problem: the Cluster Time-Frequency Domain Reflectometry (CTFDR) Based on the fact that a wire is most often in a bundle with several other wires, this method takes advantage of the resultant near end crosstalk signals to get more information about the state of the wires in the bundle To make the detection of incipient faults easier, a normalized time-frequency cross correlation function has also been used

Soft fault detection in cables using the cluster time-frequency domain reflectometry

This paper describes a technique that allows one to identify the faulty condition (open or short circuit) at a termination of a multiconductor transmission line structure by measuring the induced voltage at the other end. The wavelet theory is used to filter out from the signal the components due to unwanted sources, and to decompose it to obtain the fault's signature. The comparison (or matching) algorithm is substituted by an artificial neural network. Two differently designed neural networks are used to validate the results and the overall procedure is also tested on an experimental set-up.

A. Orlandi

Papers

Diagnosing transmission line termination faults by means of wavelet based crosstalk signature recognition