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    A comprehensive simulation platform for switched reluctance generator system
    (World Scientific and Engineering Academy and Society, 2012) Arifin A; Al-Bahadly I; Mukhopadhyay SC; Topalis, FV; Dias, EM
    A Switched Reluctance Generator (SRG) system normally encompasses three main components: SR machine, controller and converter. On-going research on simulation and modelling of SRG system has focused on one component. There is a lack of a more comprehensive approach which integrates all three components into one simulation platform. We have developed a simulation model comprising SR machine, control and converter using MATLAB/Simulink. The main advantage of a simulation model is to reduce time and cost by having to perform changes on the prototype machine. In this paper, the work is focused on developing the optimal control algorithm for the platform. Optimal parameters are identified and characterized in terms of highest percentage of power generated. From simulation, the most influential parameters affecting the power generated are the firing angles and voltage level. So, a function relating the optimized parameter with machine performance was developed. The proposed control technique will provide easy implementation and ensure high machine performance. The effectiveness of the proposed method is demonstrated by simulation results. The work will aid in development of SRG by providing a platform to determine the best generating operation before real implementation, reducing manufacturing time and cost.
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    Decision-making for foot-and-mouth disease control: Objectives matter.
    (2016-06) Probert WJM; Shea K; Fonnesbeck CJ; Runge MC; Carpenter TE; Dürr S; Garner MG; Harvey N; Stevenson MA; Webb CT; Werkman M; Tildesley MJ; Ferrari MJ
    Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.