Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods

A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm

In this work a new approach based on cross-wavelet transform towards identification of noisy Partial Discharge (PD) patterns has been proposed. Different partial discharge patterns are recorded from the various samples prepared with known defects. A novel cross-wavelet transform based technique is used for feature extraction from raw noisy partial discharge signals. Noise is a significant problem in PD detection. The proposed method eliminates the requirement of denoising prior to processing and therefore it can be used to develop an automated and intelligent PD detector that requires minimal human expertise during its operation and analysis. A rough-set theory (RST) based classifier is used to classify the extracted features. Results show that the partial discharge patterns can be classified properly from the noisy waveforms. The effectiveness of the feature extraction methodology has also been verified with two other commonly used classification techniques: Artificial Neural Network (ANN) based classifier and Fuzzy classifier. It is found that the type of defect within insulation can be classified efficiently with the features extracted from cross-wavelet spectra of PD waveforms by all of these methods with a reasonable degree of accuracy.

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Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses

This research aims at enhancing the accuracy of land vehicular navigation systems by integrating GPS and Micro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). This comprises improving the MEMS-based inertial output signals as well as investigating the limitations of a conventional Kalman Filtering (KF) solution for MEMS-IMU/GPS integration. These limitations are due to two main reasons. The first is that a KF suppresses the effect of inertial sensor noise using GPS-derived position and velocity as updates but within a limited band of frequency. The second reason is that a KF only works well under certain predefined dynamic models and convenient input data that fit these models, which are not attainable with the utilization of MEMS-based inertial technology. Therefore, if the GPS reference solutions are lost, the accuracy of standalone MEMS-IMU navigation will drastically degrade over time. The Wavelet Multi-Resolution Analysis (WMRA) technique is proposed in this thesis as an efficient pre-filter for MEMS-based inertial sensors outputs. Applying this prefiltering process successfully improves the sensors’ signal-to-noise ratios, removes shortterm errors mixed with motion dynamics, and provides more reliable data to the KFbased MEMS-INS/GPS integration module. The results of experimental validation show the effectiveness of the proposed WMRA method in improving the accuracy of KF estimated navigation states particularly position. Moreover, the Adaptive-Neuro-Fuzzyinference-system (ANFIS)-based algorithm is suggested and assessed to model the variations of the MEMS sensors’ performance characteristics with temperature. The

Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by ''marrying'' the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

This paper presents a wavelet-based filtering approach to the recovery of a signal contaminated with sinusoidal interference of a single frequency component or its harmonics. A key step in the approach is to threshold the detail coefficients of the signal by using a level-dependent threshold. Unlike the use of notch and comb-shaped filters, the new approach does not result in notable attenuation to the signal at interference frequencies and is especially suited for the removal of sinusoidal interference that is located within the signal spectrum. Evaluation of the approach has been conducted using a simulated piecewise smooth signal and a real, rapidly changing pressure signal. Results obtained from the studies demonstrate that both the sinusoidal interference and the white noise in the signal can be removed effectively without excessive attenuation to the signal at the interference frequencies.

https://iopscience.iop.org/article/10.1088/0957-0233/15/9/015/pdf

Wavelet-based removal of sinusoidal interference from a signal

Study of partial discharge (PD) behavior in electrical apparatus is important to know the degradation of insulating materials. The characterization of these pulses in the form of cavity discharge, corona discharge and surface discharge is important to identify the faulty location and to quantify the degree of deterioration. A laboratory study is done by making the models of these discharges. Both time and frequency domain measurements were done. The necessary interfacing electronics to minimize the 50 Hz and harmonics from the laboratory power supply is developed. Wavelet signal processing is used to recover the PD signal by eliminating the noises of many natures. Furthermore, different wavelet filters and windowing techniques are used to improve the efficiency of PD signal extraction. With the fabricated models to create only a type of discharge, the statistical characteristic of that type of discharge is identified. A significant number of indicators are got to identify the type of discharge.

S. Birlasekaran

Papers

Characterization of partial discharge signals using wavelet and statistical techniques