Fault localization on power cables using time delay estimation of partial discharge signals

Precise localization of partial discharge (PD) sources on power cables is vital to prevent power line failures that can lead to significant economic losses for electrical suppliers. This study proposes four methods to estimate the time delay of PD signals under electromagnetic interference, including white Gaussian noise (WGN) and discrete sinusoidal interference (DSI), using denoised PD signals with signal-to-noise ratios ranging from 10.6 to -7.02 dB. The maximum peak detection (MPD) and cross-correlation (CC) approaches, as well as two new techniques, interpolation cross-correlation (ICC) and envelope cross-correlation (ECC), are evaluated for their effectiveness in PD source localization. The researchers employ the time difference of arrival (TDoA) algorithm to compute PD location using the double-end PD location algorithm, where the PD location precision serves as an indicator of the accuracy of the time delay estimation methods. The study concludes that CC and ICC are the most suitable methods for estimating the time delay of PD signals in the PD location algorithm, as they exhibit the lowest error rates. These results suggest that CC and ICC can be used effectively for precise PD source localization under electromagnetic interference on power cables

Impulsive noise cancellation of acoustic emission signal based on iterative mathematical morphology filter

This paper aims to propose an iterative mathematical morphology (IMM) filter methodology to de-noise the acoustic emission (AE) signal with impulsive noise. To develop the principle of IMM filter, a simulation signal is used to be de-noised by the conventional MM filter. Moreover, a novel approach is introduced to eliminate the end effect of MM filter by connecting the initial point with the end point of the time series. Therefore, the IMM filter can be realized based on the operations of MM filter and the elimination method of end effect. The noise elimination of a simulation signal indicates that the IMM filter can remove the impulsive noise more effectively than the MM filter and maintain useful information as much as possible. Two AE signals acquired from rock compression experiment, which are polluted by electromagnetic impulsive noise, are de-noised by the IMM filter, the conventional digital filter and the wavelet filter respectively. Compared with the other two methods, the IMM filter can preserve the essential information contained in AE signal better, especially the arrival time. These two experiments manifest the effectiveness of the IMM filter in de-noising issues of AE signals polluted by impulsive noise

Partial Discharge Location Technique for Covered-Conductor Overhead Distribution Lines

In Finland, covered-conductor (CC) overhead lines are commonly used in medium voltage (MV) networks because the loads are widely distributed in the forested terrain. Such parts of the network are exposed to leaning trees which produce partial discharges (PDs) in CC lines. This thesis presents a technique to locate the PD source on CC overhead distribution line networks. The algorithm is developed and tested using a simulated study and experimental measurements. The Electromagnetic Transient Program-Alternative Transient Program (EMTP-ATP) is used to simulate and analyze a three-phase PD monitoring system, while MATLAB is used for post-processing of the high frequency signals which were measured. A Rogowski coil is used as the measuring sensor. A multi-end correlation-based technique for PD location is implemented using the theory of maximum correlation factor in order to find the time difference of arrival (TDOA) between signal arrivals at three synchronized measuring points. The three stages of signal analysis used are: 1) denoising  by applying discrete wavelet transform (DWT); 2) extracting the PD features using the absolute or windowed standard deviation (STD) and; 3) locating the PD point. The advantage of this technique is the ability to locate the PD source without the need to know the first arrival time and the propagation velocity of the signals. In addition, the faulty section of the CC line between three measuring points can also be identified based on the degrees of correlation. An experimental analysis is performed to evaluate the PD measurement system performance for PD location on CC overhead lines. The measuring set-up is arranged in a high voltage (HV) laboratory. A multi-end measuring method is chosen as a technique to locate the PD source point on the line. A power transformer 110/20 kV was used to energize the AC voltage up to 11.5 kV/phase (20 kV system). The tests were designed to cover different conditions such as offline and online measurements. The thesis evaluates the possibility of using a Rogowski coil for locating faults in MV distribution lines and a test bench of a 20 kV distribution network is developed. Different fault scenarios are simulated including earth and phase faults, arcing faults and faults caused by leaning trees. Results favourably show the possibility of using a Rogowski coil for locating faults in distribution networks.  

Visible Light Positioning and Navigation Using Noise Measurement and Mitigation

Visible Light Positioning (VLP) has become an essential candidate for high-accurate positioning; however, its positioning accuracy is usually degraded by the noise in the VLP system. To solve this problem, a novel scheme of noise measurement and mitigation is proposed for VLPbased on the noise measurement from Allan Varianceand the noise mitigation from positioning algorithms such asAdaptive Least Squares (ALSQ)andExtended Kalman Filter (EKF). In this scheme, Allan Varianceis introduced for noise analysis in VLPfor the first time, which provides an efficient method for measuring the white noise in the VLPsystems. Meanwhile, we evaluate our noise reduction method under static testusing ALSQ and dynamic test using EKF. Furthermore, this article carefully discusses the relationship between positioning accuracy and Dilution of Precision (DOP) values. The preliminary field static tests demonstrate that the proposed scheme improves thepositioning accuracy by 16.5% and achieves the accuracy of 137mmwhile dynamic tests show an improvement of 60.4% and achieve the mean positioning accuracyof 153 mm

Evaluation and optimization of wavelet technique to enhance the mapping accuracy of lightning VHF interferometry

Despite the significant progress in the understanding of the phenomenon of lightning and the physics behind it, locating and mapping its occurrence remain a challenge. Such localization and mapping of very high frequency (VHF) lightning radiation sources provide a foundation for the subsequent research on predicting lightning, saving lives, and protecting valuable assets. A major technical challenge in attempting to map the sources of lightning is mapping accuracy. Several methods have been proposed for estimating the real pattern of the temporal location and spatial map of the lightning strikes. However, due to the complexity of lightning signals and the noise accompanying its recording, providing accurate lightning maps estimation remains a challenging task. To advance the lightning mapping it is vital to improve how lightning signals are pre-processed and how noise is filtered. Most existing studies of lightning mapping make use of the VHF interferometer (ITF) alongside crosscorrelation in time and frequency domain and phase difference of arrival techniques. These methods involve selecting a set of parameters which usually fail to accommodate all types of lightning flashes, discarding information that could be beneficial for further improvement of lightning mapping accuracy. In this thesis, a wavelet-based cross-correlation (CCWD) is proposed for a reliable lightning mapping estimation through means of signal enhancement and noise reduction, providing a better time- frequency resolution. Interpolation techniques were introduced to smoothen the correlation peaks for more accurate lightning localization. To confirm the effectiveness of the proposed method, a simulation of lightning signals was created, and the mapping results were verified. Moreover, a comparative study to investigate the effectiveness of different processing techniques was carried out. The benchmark environment involved the use of different filtering and cross-correlation techniques, introducing new processing methods such as Kalman filter and wavelet-based crosscorrelation. In addition, a particle swarm optimization technique is used to optimize the trajectory of the CCWD-based lightning maps by finding the optimal sliding window of the cross-correlation. The CCWD-PSO technique was further enhanced through the introduction of a novel lightning event extraction method that enables faster processing of the lightning mapping. Six positive narrow bipolar events were analyzed, and the results indicate that a good estimation of the lightning radiation sources was achieved using wavelet de-noising and CCWD with a minimal error of 3.46°. The results were further improved with the use of CCWD-PSO technique with Euclidean distance of 0.6243 at 300 iterations. The investigations carried out in this study confirm that the ITF mapping system could effectively map the lightning VHF radiation source, which makes the combination of ITF and the CCWD a potential candidate for lightning mapping technology

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Indoor Localization Using Channel State Information with Regression Artificial Neural Network

RÉSUMÉ Dans cette recherche, les informations sur l'état du canal (CSI) sont utilisées pour localiser les stations mobiles dans un environnement intérieur. À cette fin, deux ordinateurs portables équipés de la carte Intel Wireless Wi-Fi Wireless Link 5300 disponible dans le commerce sont utilisés. Les informations CSI sont collectées en établissant une connexion sans fil entre deux machines de plus de 200, 70 et 52 points de référence (RP) aux sixième, cinquième et troisième étages respectivement, dans l’immeuble Lassonde de Polytechnique Montréal servant de banc d’essai expérimental. Différentes approches de localisation sont étudiées et comparées les unes aux autres en termes de précision de localisation. Dans la première approche, les CSI collectés alimentent directement le réseau de neurones artificiels (RNA) en tant que caractéristiques d’entrée et le RNA appris est utilisé en tant qu’algorithme de correspondance du modèle afin de prédire la position de l’utilisateur. La deuxième approche consiste à appliquer à l’entrée de RNA les paramètres pertinents du canal extrait représentant le nombre réduit d’entités à l’entrée de RNA. Enfin, une exploration est effectuée pour trouver la meilleure configuration de couches cachées et de facteurs d'étalement pour les réseaux Perceptron multicouche (MLP) et Réseaux de neurones à régression générale (GRNN), respectivement.----------ABSTRACT In this research, the Channel State Information (CSI) is leveraged to locate mobile stations in an indoor environment. For this purpose, two laptops equipped with the off-the-shelf Intel Wi-Fi Wireless Link 5300 (NIC card) are used. CSI information is collected by establishing a wireless connection between two machines over 200, 70 and 52 reference points (RP) on sixth, fifth, and third floors respectively, in Lassonde building of Polytechnique Montreal as the experimental testbed. Different geolocation approaches are investigated and compared with each other in terms of location accuracy and precision. In the first approach, the collected CSIs are directly fed to the artificial neural network (ANN) as input features and the learned ANN is used as the patternmatching algorithm in order to predict the user’s location. The second approach consists in applying at the input of the ANN the extracted channel relevant parameters representing the reduced number of features at the input of ANN. Finally, exploration is performed to find the best configuration of hidden layers and spread factors for Multilayer Perceptron (MLPs) and General Regression Neural Networks (GRNNs), respectively

Sensor fusion for tangible acoustic interfaces for human computer intreraction

This thesis presents the development of tangible acoustic interfaces for human computer interaction. The method adopted was to position sensors on the surface of a solid object to detect acoustic waves generated during an interaction, process the sensor signals and estimate either the location of a discrete impact or the trajectory of a moving point of contact on the surface. Higher accuracy and reliability were achieved by employing sensor fusion to combine the information collected from redundant sensors electively positioned on the solid object. Two different localisation approaches are proposed in the thesis. The learning-based approach is employed to detect discrete impact positions. With this approach, a signature vector representation of time-series patterns from a single sensor is matched with database signatures for known impact locations. For improved reliability, a criterion is proposed to extract the location signature from two vectors. The other approach is based on the Time Difference of Arrival (TDOA) of a source signal captured by a spatially distributed array of sensors. Enhanced positioning algorithms that consider near-field scenario, dispersion, optimisation and filtration are proposed to tackle the problems of passive acoustic localisation in solid objects. A computationally efficient algorithm for tracking a continuously moving source is presented. Spatial filtering of the estimated trajectory has been performed using Kalman filtering with automated initialisation