Classification of multiple electromagnetic interference events in high-voltage power plant

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert’s knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events

A frequency-based RF partial discharge detector for low-power wireless sensing

Partial discharge (PD) monitoring has been the subject of significant research in recent years, which has given rise to a range of well-established PD detection and measurement techniques, such as acoustic and RF, on which condition monitoring systems for highvoltage equipment have been based. This paper presents a novel approach to partial discharge monitoring by using a low-cost, low-power RF detector. The detector employs a frequency-based technique that can distinguish between multiple partial discharge events and other impulsive noise sources within a substation, tracking defect severity over time and providing information pertaining to plant health. The detector is designed to operate as part of a wireless condition monitoring network, removing the need for additional wiring to be installed into substations whilst still gaining the benefits of the RF technique. This novel approach to PD detection not only provides a low-cost solution to on-line partial discharge monitoring, but also presents a means to deploy wide-scale RF monitoring without the associated costs of wide-band monitoring systems

Partial Discharge Location within a Transformer Winding using Principal Component Analysis

Partial discharge (PD) may occur in a transformer winding due to ageing processes, operational over stressing or defects introduced during manufacture. The presence of PD does not necessarily indicate imminent failure of the transformer but it will lead to serious degradation and ageing mechanisms which can be considered as a precursor of transformer failure. A necessary step is required in order to prevent degradation due to PD activity which may ultimately lead to failure. PD might occur anywhere along the transformer winding, the discharge signal can propagate along the winding to the bushing and neutral to earth connections. As far as maintenance and replacement processes are concerned, it is important to identify the location of PD activity so any repair or replace decision is assured to be cost effective. Therefore, identification of a PD source as well as its location along the transformer winding is of great interest to both manufacturers and system operators. The proposed method for locating PD sources in windings is based on wavelet filtering and principal component analysis. An experiment has been developed based on a high voltage winding section that has been used to produce PD measurement data and to investigate the feasibility of the proposed approach

Partial Discharge in Electronic Equipments

Tato disertační práce se věnuje studiu částečných výbojů (PD) způsobených poklesem spolehlivosti a životnosti elektronických zařízení a systémů. Diagnostika PD je dnes známá metoda pro vysoké napětí u vysoko-výkonných zařízení. V případě elektronických zařízení PD testování není ale běžně používáná metoda, přestože je zde také potenciál pro vysoké elektrické zatížení vzhledem k velmi krátké vzdálenosti. Tato práce je zaměřena na vyšetřování PD činnosti u elektronických zařízení. Bylo navrženo a provedeno pracoviště pro diagnostiku PD v elektronických zařízeních. Pracovní frekvence se pohybuje od několika stovek Hz až 100 kHz. Maximální amplituda PD testovaného napětí je vyšší než 10 kV. Navzdory jednoduché konstrukci toto zařízení přináší vysokou spolehlivost měření. Více než 300 PD testů bylo provedeno na různých elektronických zařízeních a elektronických součástí,např. na planárních transformátorech a elektronických komponentách používaných při vysoko-napěťových měničíchThis dissertation thesis is devoted to study of partial discharge (PD) caused decrease of reliability and lifetime of electronic equipments and systems. PD diagnostic is nowadays well known method for high voltage high power equipments but in case of electronic devices PD testing it is not used routinely despite that there is also a potential for high electric load due to extremely short distances. The risk of PD caused failure is here extremely high because of high working frequency and consequently high repetition rate of PD events. Therefore, this work is focused on investigation of PD activity in electronic equipments. The workplace for PD diagnostic in electronic devices based on switched power supply was designed and made. Working frequency ranges from several hundreds of Hertz up to 100 kHz. The maximal amplitude of PD testing voltage is higher than 10 kV. Despite the simple design this equipment brings high repeatability and reliability of measurement. More than 300 PD tests were made on different electronic devices and electronic components, on planar transformers, and on components for voltage gate drivers for use in high voltage power converters. Possibilities of PD tools in investigation and engineering ofd insulation systems were demonstrated.

Time domain analysis of switching transient fields in high voltage substations

Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Fault location and diagnosis in a medium voltage EPR power cable

This paper presents a case study on fault location, characterization and diagnosis in a length of shielded 11 kV medium voltage ethylene-propylene rubber (EPR) power cable. The defect was identified on-site as a low resistance fault occurring between the sheath and the core. A 43 m section was removed for further analysis. The fault resistance was characterized and the location of the defect pinpointed to within a few cm using a combination of time-difference-of-arrival location and infra-red imaging. A combination of X-ray computed tomography, scanning electron microscopy and energy dispersive X-ray spectroscopy were then applied to characterize any abnormalities in the dielectric surrounding the breakdown region. A significant number of high density contaminants were found to be embedded in the dielectric layer, having an average diameter of the order of 100 um, a maximum diameter of 310 um and an average density of 1 particle per 2.28 mm3 . Scanning electron microscopy and energy-dispersive X-ray spectroscopy were used to determine the geometry and elemental composition of some initial contaminant samples. It was concluded that contamination of the EPR layer, combined with an observed eccentricity of the cable’s core and sheath resulting in a reduced insulation gap, may have led to an electric field concentration in the region of the defect sufficient to initiate breakdown. Preventative strategies are discussed for similar families of cables, including more stringent dielectric testing requirements at the manufacturing stage and PD monitoring to detect incipient failure

Online Monitoring Technical Basis and Analysis Framework for Large Power Transformers; Interim Report for FY 2012

The Light Water Reactor Sustainability program at Idaho National Laboratory (INL) is actively conducting research to develop and demonstrate online monitoring (OLM) capabilities for active components in existing Nuclear Power Plants. A pilot project is currently underway to apply OLM to Generator Step-Up Transformers (GSUs) and Emergency Diesel Generators (EDGs). INL and the Electric Power Research Institute (EPRI) are working jointly to implement the pilot project. The EPRI Fleet-Wide Prognostic and Health Management (FW-PHM) Software Suite will be used to implement monitoring in conjunction with utility partners: the Shearon Harris Nuclear Generating Station (owned by Duke Energy for GSUs, and Braidwood Generating Station (owned by Exelon Corporation) for EDGs. This report presents monitoring techniques, fault signatures, and diagnostic and prognostic models for GSUs. GSUs are main transformers that are directly connected to generators, stepping up the voltage from the generator output voltage to the highest transmission voltages for supplying electricity to the transmission grid. Technical experts from Shearon Harris are assisting INL and EPRI in identifying critical faults and defining fault signatures associated with each fault. The resulting diagnostic models will be implemented in the FW-PHM Software Suite and tested using data from Shearon-Harris. Parallel research on EDGs is being conducted, and will be reported in an interim report during the first quarter of fiscal year 2013

An enhanced feature selection technique for classification of group-based holy quran verses

This thesis is about proposing an enhanced feature selection technique for text classification applications. Text classification problem is primarily applied in document labeling. However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. Therefore, in this study, a hybrid feature selection technique is proposed. The proposed FS technique is a combination of JUter-based information gain (JG) and wrapper-based CFS algorithms. The purpose of combining these two FS algorithms is to achieve both high classification accuracy perfonnance (wrapper) at lower computational runtime (filter). The study also developed a group-based Quran dataset to improve on the understanding and analysis of the textual data (Quranic verses). The group-based dataset is a combination of Holy Quran translation and commentary (tafsir). The Quranic verses were selected from two chapters, Surah Al­Baqarah and Surah Al-Anaam. The verses are classified into three categories: Faith, Worship, and Etiquette. In the experiment, six feature selection algorithms were applied: In.formation Gain (JG), Chi-square (CH), Pearson Correlation Coefficient (PCC), RelieJF, Correlation-based (CFS), and the proposed JG-CFS algorithms. The textual data (Quranic verses) were preprocessed using StringtoWordVector with weighted Term Frequency-Inverse Document Frequency (IF-IDF). Meanwhile, the classification phase has involved four algorithms: Nai've Bayes (NB), k-Nearest Neighbor (k-NN), Support Vector Machine (LibSVM), and Decision Trees (148). The experiment results were evaluated based on two established perfonnance metrics in text classification: Accuracy and Area under Receiver Operating Characteristics (ROC) curve (A UC). The proposed hybrid feature selection technique has shown promising results in tenns of Accuracy and Area under Receiver Operating Characteristics (ROC) curve (A UC) by achieving at a lower computational runtime (3.89secs) Accuracy of94.5% and AUC of0.944 with the group-based Quran dataset