The Power Systems Blockset has been used by consultants at Cambridge Control Ltd to develop models of marine power generation and distribution systems. This is particularly topical at the present time, as the UK Navy is rapidly moving in the direction of adopting full electric propulsion (FEP) for its new vessels. Future US Navy vessels will also adopt this configuration.
FEP systems offer the potential for significant savings to be made in purchase and running costs, particularly the latter. Benefits in the form of increased survivability and stealth characteristiucs can also be delivered. FEP also raises a number of possible risks, not least the simple fact that the technology is relatively new. The larger and more complex nature of the electrical power system raises the possibility of more complex interactions arising between systems. Risk reduction and careful systems integration is therefore particularly important for FEP warships.
Simulation provides the key to early risk reduction in systems integration studies. This in turn requires that validated models of the power system be available. The models must be sufficiently flexible that they can be connected together in many different ways, to investigate the behaviour of a variety of alternative power system configurations.
Power system models have been constructed that include:
Some, but by no means all, of these system models benefit greatly from the use of PSB. The great strength of Simulink is the capability it provides for mixed domain modelling. We can combine electrical machine models with power electronic models, with ship dynamic models. Control systems can be included for individual systems, and supervisory systems can be modelled, reproducing the action of the Platform Management System that enables these complex vessels to operate successfully, using smart systems and automation to ease the workload on the Ship's complement.
In some instances, particularly with detailed models of electro-mechanical machines such as generators and motors, it can be difficult to validate these models, i.e. for a given combination of dynamics, to know with great confidence what parameters should be specified. To this end, we have created a tool called PIPS (Parameter Identification for Power Systems), utilising advance optimisation techniques to tune model parameters to achieve an optimal fit between measured and simulated data.
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