PCA-enhanced methodology for the identification of partial discharge locations

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) network is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using an IoT Data set, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks

Improving RF-based partial discharge localization via machine learning ensemble method

Partial discharge (PD) is regarded as a precursor to plant failure and therefore, an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency-based technique uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The regression tree algorithm, bootstrap aggregating method, and regression random forest are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-random forest PD localization scheme produced superior performance as a result of its robustness against noise

Machine learning enhanced radio location of partial discharge

Partial Discharge (PD) is a well-known indicator of plant failure in electricity facilities. A considerable proportion of assets including transformers, switch gears, and power lines are susceptible to PD due to incipient weakness of their dielectric components. These discharges may cause further degradation of the insulation, which in turn may lead to subsequent catastrophic failure. The damage that results from PD activity is worth millions of pounds and endangers the lives of personnel. PD emits electrical pulses in the form of Radio Frequency (RF) signals which propagate as a travelling wave in the vicinity of the discharge site and can be detected using dedicated sensors. This has motivated the use of an enhanced radio-based technique to detect its occurrence at early stage. Early detection of PD helps utility operators to initiate an emergency maintenance outside the scheduled times when it is most cost-effective and before the equipment loses performance or suffers catastrophic failure, hence improving asset management. Therefore this thesis presents an investigation of an enhanced machine learning approach to continuous PD localisation using a network of radio sensors. The approach being investigated relies on location dependent parameters which will be extracted from PD measurements. This thesis demonstrates RF-based fingerprinting technique for locating PD sources using Received Signal Strength (RSS). Furthermore, Signal Strength Ratios (SSR) between pairs of sensor nodes are used as robust fingerprints given that the energy emitted by each PD event may be different due to progressive nature of PD severity as deterioration continues and the fact that different types of PD occur in nature. Sophisticated machine learning techniques are investigated and used to develop PD localisation models. This work also investigates the plausibility of using other PD received signal parameters for locating PD sources. It has been found that the statistical characterisation of the received RF signals produces manifold PD features beside RSS. The developed localisation approach based on the analysis of these statistical features assumes that PDs generate unique RF spatial patterns due to the complexities and non-linearities of RF propagation. This approach exploits two distinct frequency bands which hold different PD information. PD location features are extracted from the main PD signal and the two sub-band signals. These features are then used to infer PD location. Moreover, due to the increased dimensionality of data that may result from PD feature generation, feature selection algorithm; Correlation Based Feature Selection (CFS) is employed for feature selection and dimensionality reduction. The use of statistical PD features improves localisation accuracy. This study further presents a novel method for RF-based PD localisation. The technique uses Wavelet Packet Transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) version of wavelet packet and analysed in order to identify localised PD signal patterns. The Regression Tree algorithm, Bootstrap Aggregating method and Regression Random Forest (RRF) are used to develop PD localisation models based on the wavelet PD features. The proposed PD localisation scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the developed PD localisation system has been validated using a separate test dataset. This approach is based on purely practical reasons, given the enormity of separate experiments to be carried out. The data required is collated over an extended time period. The results of the investigation presented in this thesis show that an autonomous andefficient substation-wide RF-based continuous PD localisation system is possible.Partial Discharge (PD) is a well-known indicator of plant failure in electricity facilities. A considerable proportion of assets including transformers, switch gears, and power lines are susceptible to PD due to incipient weakness of their dielectric components. These discharges may cause further degradation of the insulation, which in turn may lead to subsequent catastrophic failure. The damage that results from PD activity is worth millions of pounds and endangers the lives of personnel. PD emits electrical pulses in the form of Radio Frequency (RF) signals which propagate as a travelling wave in the vicinity of the discharge site and can be detected using dedicated sensors. This has motivated the use of an enhanced radio-based technique to detect its occurrence at early stage. Early detection of PD helps utility operators to initiate an emergency maintenance outside the scheduled times when it is most cost-effective and before the equipment loses performance or suffers catastrophic failure, hence improving asset management. Therefore this thesis presents an investigation of an enhanced machine learning approach to continuous PD localisation using a network of radio sensors. The approach being investigated relies on location dependent parameters which will be extracted from PD measurements. This thesis demonstrates RF-based fingerprinting technique for locating PD sources using Received Signal Strength (RSS). Furthermore, Signal Strength Ratios (SSR) between pairs of sensor nodes are used as robust fingerprints given that the energy emitted by each PD event may be different due to progressive nature of PD severity as deterioration continues and the fact that different types of PD occur in nature. Sophisticated machine learning techniques are investigated and used to develop PD localisation models. This work also investigates the plausibility of using other PD received signal parameters for locating PD sources. It has been found that the statistical characterisation of the received RF signals produces manifold PD features beside RSS. The developed localisation approach based on the analysis of these statistical features assumes that PDs generate unique RF spatial patterns due to the complexities and non-linearities of RF propagation. This approach exploits two distinct frequency bands which hold different PD information. PD location features are extracted from the main PD signal and the two sub-band signals. These features are then used to infer PD location. Moreover, due to the increased dimensionality of data that may result from PD feature generation, feature selection algorithm; Correlation Based Feature Selection (CFS) is employed for feature selection and dimensionality reduction. The use of statistical PD features improves localisation accuracy. This study further presents a novel method for RF-based PD localisation. The technique uses Wavelet Packet Transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) version of wavelet packet and analysed in order to identify localised PD signal patterns. The Regression Tree algorithm, Bootstrap Aggregating method and Regression Random Forest (RRF) are used to develop PD localisation models based on the wavelet PD features. The proposed PD localisation scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the developed PD localisation system has been validated using a separate test dataset. This approach is based on purely practical reasons, given the enormity of separate experiments to be carried out. The data required is collated over an extended time period. The results of the investigation presented in this thesis show that an autonomous andefficient substation-wide RF-based continuous PD localisation system is possible