1,721,305 research outputs found
Iterative Learning Control
No full textIterative Learning Control (ILC) was first proposed in 1984 by Arimoto, Miyazaki and Kawamura (1) and allows process tools such as robot arms and production machinery to repeat actions or synchronise to differing demands in production processes, such as those in the food industry
Two Dimensional Numerical Model to Predict the Thermal-Chemical Degradation of a piece of Carbon Fibre Composite (CFC) due to Laser Ablation
No full textThere is a growing interest in using carbon fibre composites (CFC) as a high tech construction material. The reason for this is that CFCs have similar mechanical performance to that of the more traditionally used materials like aluminium alloys, whilst being considerable lighter. The benefits of using a lighter material are vast. However whilst CFC have similar structural properties to that of aluminium its electrical and thermal properties are very different. This becomes important if CFCs are placed in an environment where the pieces of CFC could be struck by lightning as this interaction will damage the panels [1]. Previous studies published by N. Jennings and C. J. Hardwick [2] and F Lago et. al. [3] have attempted to model the damage caused to a piece of CFC due to a lightning strike. However these models have only considered very simple degradation methods and also did not include gas transport. The study presented here is an expansion of what has been discussed previously [4]. A two dimensional numerical model has been built which is designed to predict the damaged caused to a piece of CFC due to a lightning strike. Initial verification of the model is conducted by decoupling the thermal physics from the electrical effects and damaging the pieces of CFC by using laser ablation. The two dimensional numerical model (2D) includes thermal chemical degradation of the polymer via pyrolysis, the resultant gas transport through the decomposing material and carbon fibre vaporisation. An image of the x-ray tomography results of the laser ablated CFC samples are shown in figure 1. The predictions from the 2D model provide a reasonable agreement with the experimental results. Although further expansion of the model, into three dimensions, is required before a true validation of the numerical predictions can be achieved
High Voltage Insulation System Condition Monitoring: Ensuring Future Smart Grids are Resilient and Reliable
No full textThe changes faced by existing traditional electrical power transmission and distribution networks over the next 30 years or more are immense. Technological developments include ‘Smart’ grid technology, HVDC interconnectors and bootstraps as well as replacement of conventional generation with significant levels of renewable generation. The impact of these changes on existing (and often aged) high voltage plant will be significant and in the UK, potential future areas of research have been identified through a road-mapping exercise. A key area is that of condition monitoring (CM) of high voltage plant. CM strategies need to develop so that they are ‘real-time’ diagnostic and prognostic in nature. This represents a real challenge for the dielectrics community and this paper discusses the identified potential areas of research as well as summarizing some of the recent developments in this area
Phase Resolved Partial Discharge Identification using a Support Vector Machine
No full textPartial discharge (PD) has a significant effect on the insulation performance of power apparatus in both transmission and distribution networks of power systems. Insulation performance and properties can be influenced by different types of PD activity. Therefore, PD source identification and diagnosis is of interest to both power equipment Manufacturers and utilities. With developments in measurement techniques, sensors and signal processing techniques, interpretation of the measured PD data and PD source identification are gaining more interest. Over the last two decades, research into computer-aided automatic PD source discrimination has attracted great attention. A number of papers have been published based on the use of artificial intelligence algorithms such as artificial neural networks, genetic algorithms and fuzzy logic. This paper investigates the application of a machine learning technique, namely the support vector machine (SVM) on PD source identification using phase resolved discharge distribution information (' – average q). PD data obtained from a conventional PD detector and a non-conventional radio frequency current transducer were used to assess the performance of the use of a phase resolved parameter for identification
Partial discharges in bulk oil and at oil-pressboard interface under negative lightning impulse voltages
No full textThe presence of oil-pressboard interface has been associated with creepage discharge in large power transformers. Creeping discharge or surface discharge is considered as a serious fault condition that can lead to catastrophic failure under normal operating conditions. The studies on this fault condition are normally on the inception and extinction voltage under AC voltages. Recently, there are interest on the white marks formation and leakage current during the surface discharges. On the other hand, most studies related to creepage discharges under lightning impulse (LI) voltages focused on the pre-breakdown streamer propagation. The use of LI voltages is important in standard material breakdown test such as multiple-level method in IEC 60060-1. This paper investigates the partial discharge (PD) behaviour in the bulk oil and at the oil-pressboard interface from accumulative effect of breakdown test by using different configurations of electrodes. A point-plane electrode is used to study the PD behaviour in the oil bulk, whilst two setup of needle-bar electrode, i.e. needle placed in parallel with the pressboard surface and needle place at an angle close to the horizontal of pressboard are used to study the creepage discharge behaviour at the oil-pressboard interface. In addition, the PD behaviour under certain level of negative LI voltage that less than the statistical withstand voltage (SWV) is also studied by measuring the PD current using radio frequency current transformer (RFCT)
An Investigation into Multi-Source Partial Discharge Discrimination within a Power Transformer Model
No full textThis paper investigates a new multi-PD-source discrimination method using machine learning technique, namely support vector machine (SVM). A bushing-tap-RFCT (Radio Frequency Current Transducer) system is used to simulate a power transformer and coupling sensor. Some artificial PD sources are used to generate multi-PD signals. Obtained experimental results from different PD sources are processed using accepted approaches such as ?-q-n patterns, pulse sequence analysis and wavelet transform. The processed data are also used as the input vector of the SVM. Initial results indicate that, by using appropriate feature parameters, the automatic identification results obtained by combining the SVM technique and time domain information are very encouraging
Frequency Domain Modeling of High Voltage Transformers Using a Nonlinear Least-Square Estimation Technique
No full textThe development of transformer models can be achieved based on experimental frequency response measurements providing access to the windings is available and the physical dimensions are known in order to calculate R, L and C parameter values. Of interest are methods that allow the generation of a suitable model if the R, L and C parameters are unknown and access is restricted to the external terminals of the winding only. Using a lumped parameter model and the measured frequency response across the whole winding, it is possible to estimate the intermediary winding responses. Knowledge of the intermediary winding frequency responses facilitates the development of condition monitoring tools that are capable of locating the source of partial discharge activity or winding deformation within a faulty transformer [1]. This paper includes a full description of the developed modeling technique, along with experimental results from a model winding system that validates the proposed approach
Degradation Processes of Voids in Silicone Rubber under Applied AC Fields
No full textThis paper is concerned with an experimental study into the degradation processes that occur when voids in solid dielectric materials experience high applied electric fields. A method has been developed for manufacturing 2mm thick samples of silicone resin that contain a single void of 1mm diameter . Five Samples are simultaneously electrically stressed under an applied ac sinusoidal voltage of 12kV for 6 hours that is then increased to 15kV until a sample fails. During the stressing period, PD data is regularly acquired [1]. The remaining 4 samples are then inspected for signs of degradation. A typical result of a degraded sample is shown in Figure 1. The experiment is repeatable and the obtained degraded samples have been analysed using Raman spectroscopy to identify the chemical content of the degraded areas at the void /silicone rubber interface. Initial results indicate that the degradation is a pre-cursor to the development of a bow-tie electrical tree [2] , although further research is required to confirm this
Lumped Parameter Model for High Frequency Partial Discharge Estimation in High Voltage Transformer
No full textThe lumped parameter model is well known in solving high frequency signal propagation in high voltage transformer windings. At the University of Southampton, a modelling technique has been developed and employed to locate partial discharges (PD) that occur inside transformer windings. Firstly, a lumped capacitive parameter model was considered and secondly a transmission line lumped parameter approach developed. A technique of split winding analysis is introduced for both types of model. The derivation of the capacitive network considers the source location of a PD by defining the PD signal propagation in two directions. At the source, the currents are equal in magnitude and are attenuated as they flow in each direction. This provides information for a fixed distribution model equation. Under transmission line lumped parameter models, split winding analysis explains the development of accumulated harmonic waveforms of the PD propagation signal towards the neutral and bushing tappoint. Results indicate that this approach can also estimate the magnitude of PDs that occur within transformer windings
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