Processing and inferential methods to improve shaft-voltage-based condition monitoring of synchronous generators

This thesis focuses on improving shaft-voltage-based condition monitoring of synchronous generators. The work presents theory for describing and modelling shaft voltages using fundamental electromagnetic principles. A modern framework is adopted in developing an online, automated and intelligent fault-diagnosis system. Novel processing and inferential methods are used by the system to provide accurate and reliable incipient-fault detection and diagnosis. The literature shows that shaft-voltage analysis is recognised as a technique with potential for use in condition monitoring. However, deficiencies in the fundamental theory and the inadequacy of methods for extracting useful information has limited its widespread application. This work extends the knowledge of shaft voltages, validates the merits of its use for fault diagnosis, and provides methods for practical application. Validation of the model is completed using an experimental synchronous generator, and results indicate that simulated shaft voltages compare well with the measurements - i.e. total average error of the model combined with experimental uncertainty is below 16%. The fault detection and diagnosis components are tested separately and together as a complete shaft-voltage-based conditionmonitoring system in an experimental setting. Results indicate that the system can accurately diagnose faults and it represents a unique and valuable contribution to shaft-voltage-based condition monitoring. Additionally, techniques such as optimal measurement selection, multivariate model monitoring, and fault inference developed for the investigations and system presented in this thesis, will assist engineers and researchers working in the field of condition monitoring of electrical rotating machines

Performance analysis for a photovoltaic system with solar tracking

Abstract: Solar tracking is used on photovoltaic systems to minimize the incident angle between the panels and incoming sunlight. This offers the advantage of improved harvesting of solar energy. However, there are no extensive qualitative and quantitative data from studies to elucidate the extent of this improvement. This paper presents the performance analysis for a 11.52 kW PV system with solar tracking installed as a pilot project in Limpopo, South Africa. The data collected from the site are analysed in this paper and results for the energy yield and system efficiency are presented. These results indicate that solar tracking improves the daily energy yield of the PV system by 18% on average, over a 10-month period and over 30% during summer

Exploring the effects of compression via principal components analysis on X-ray image classification

Abstract: Image compression in medical applications implores careful consideration of the effects on data veracity. The inexorable challenge of assessing the volume-veracity trade-off is becoming more prevalent in this critical application area, and particularly when machine learning is used for the purpose of assisted diagnostics. This paper investigates the impact of compressing X-ray images on the accuracy of fracture diagnostics. The accuracy of the classification system is assessed for X-ray images of both healthy and fracture bones when subjected to different levels of compression. Compression is achieved using principal components analysis. Results indicate that accuracy is only marginally affected under a level one compression but begins to deteriorate under level two compression. These results are potentially useful as the level one compression yields gains up to 94% with less than a 2% drop in classification accuracy

Conceptualising the knower for a new engineering technology curriculum

Abstract: Engineering technology education in South Africa has undergone a number of significant alterations in the past three decades. The most recent of these is the establishment of a new degree qualification – Bachelor of Engineering Technology – to replace the qualification for engineering technologists and decouple it from the existing engineering technician qualification. However, the new qualification standards alone do not give a clear distinction between knowers in the engineering technician and engineering technologist categories. This lack of clarity about what knower the new programme is intended to produce is a stumbling block to educators who need to plan, develop and implement the new curriculum. It is only through understanding the knower who should be developed that questions pertaining to what kinds of knowledge should be encountered and the encounters themselves can be answered. In this paper, the intended knower dispositions is conceptualised for the new programme by carrying out a comparative analysis of the current and new exit-level outcomes. Bloom’s taxonomy and Luckett’s knowledge plane are used as lenses to perform the analysis and draw a distinction between knowers in the engineering technician and engineering technologist categories. The analysis suggests that the engineering technologist category exhibits a relative shift towards subjective and theoretical “ways of knowing”. How this shift could influence the new curriculum particularly with regard to developing effective scaffolding for engineering technology students is also fleshed out

Reliability analysis of low-voltage metal-oxide surge arresters using accelerated failure time model

Abstract: Failure progression of metal oxide-based surge arresters in response to exposure to harmonics is modelled using the accelerated failure time model. Multi-stress accelerated ageing tests, with and without AC distorted voltage stress, are conducted on arrester samples sourced from different suppliers to obtain baseline and harmonic-factor logarithmic life and reliability functions. A maximum likelihood approach is taken to estimating the accelerated failure time model parameters based on the failure time data. The modelled harmonic-factor reliabilities prove MOV life acceleration and show multiplicative failure progression of up to 41 times faster for a THD content of 8.52% in the applied voltage stress

A novel nature-inspired picogrid for flexible PV application in rural electrification systems

Abstract: The Picogrid is a response to the need for electrification in off-grid rural areas in Sub-Saharan Africa. The concept lends itself to the inclusion of renewable energy technologies such as solar photovoltaics and allows for a robust, resilient solution for rural applications. The biomimetic or nature-inspired design allows for uncomplicated scaling of the operational core system. Additionally, the system is fault tolerant and exhibits self-healing properties

Methods for condition monitoring and fault diagnosis on a synchronous 2 pole generator

In order to increase the integrity of existing generation systems, it is essential to discover problems well in advance. An investigation into methods for diagnosing multiple incipient faults on a 2-pole synchronous generator is presented. Simulation of the generator on a finite element analysis (FEA) software package is used to predict the effects of these faults. Experimental analysis of the generator under fault conditions is then conducted and confirms the predicted behaviour. The investigation utilises search coils and shaft brushes as condition monitoring tools. Results of the investigation indicate definitive relationships between the faults and specific harmonics of the output signals from the condition monitoring tools. The presented techniques are viable and future work can utilise these results in the design of a fault diagnosis system

A Neural Network Based Response Model for High Voltage Circuit-Breaker Testing

Innovative test methods for circuit breakers are constantly sought after to reduce maintenance time and costs, yet still provide accurate assessment of this critical substation equipment. This paper proposes a novel method for response modelling of high voltage SF6 circuit breakers, based on artificial neural networks, to provide a means of assessing its condition. The proposed method enables a timing response model of the circuit breaker to be developed using trip command parameters. In this paper, an experimental setup is used to perform trip response testing of a three-phase 75 kV circuit breaker. The obtained data is then used to train, validate and test a Bayesian regularised artificial neural network that can predict response times of the breaker for a given set of trip command parameters

A prior selection of measurement modality for eccentricity detection in synchronous machines using a Cramér-Rao lower bound technique

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