Current transients in the small salient-pole alternator caused by sudden short-circuit and synchronisation events

Small salient-pole machines, in the range 30 kVA to 2 MVA, are often used in distributed generators, which in turn are likely to form the major constituent of power generation in power system islanding schemes or microgrids. In addition to power system faults, such as short-circuits, islanding contains an inherent risk of out-of-synchronism re-closure onto the main power system. To understand more fully the effect of these phenomena on a small salient-pole alternator, the armature and field currents from tests conducted on a 31.5 kVA machine are analysed. This study demonstrates that by resolving the voltage difference between the machine terminals and bus into direct and quadrature axis components, interesting properties of the transient currents are revealed. The presence of saliency and short time-constants cause intriguing differences between machine events such as out-of-phase synchronisations and sudden three-phase short-circuits.

Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

Micro-Environmental monitoring of temperature and moisture changes in building stone

The monitoring of temperature and moisture changes in response to different micro-environment of building stones is essential to understand the material behaviour and the degradation mechanisms. From a practical point of view, having a continuous and detailed understanding of micro-environmental changes in building stones helps to assist in their maintenance and repair strategies. Temperature within the stone is usually monitored by means of thermistors, whereas wide ranges of techniques are available for monitoring the moisture. In the case of concrete an electrical resistance method has previously been used as an inexpensive tool for monitoring moisture changes. This paper describes the adaptation of this technique and describes its further development for monitoring moisture movement in building stones.
In this study a block of limestone was subjected to intermittent infrared radiation with programmed cycles of ambient temperature, rainfall and wind conditions in an automated climatic chamber. The temperature and moisture changes at different depths within the stone were monitored by means of bead thermistors and electrical resistance sensors. This experiment has helped to understand the thermal conductivity and moisture transport from surface into deeper parts of the stone at different simulated extreme climatic conditions. Results indicated that variations in external ambient conditions could substantially affect the moisture transport and temperature profile within the micro-environment of building stones and hence they could have a significant impact on stone decay.

Introducing Automated Management Through Iteratively Increased Automation and Indicators

Introducing automation into a managed environment includes significant initial overhead and abstraction, creating a disconnect between the administrator and the system. In order to facilitate the transition to automated management, this paper proposes an approach whereby automation increases gradually, gathering data from the task deployment process. This stored data is analysed to determine the task outcome status and can then be used for comparison against future deployments of the same task and alerting the administrator to deviations from the expected outcome. Using a machinelearning
approach, the automation tool can learn from the administrator's reaction to task failures and eventually react to faults autonomously.