symbolic dynamic models for highly varying power system loads

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equipment. It simply identifies the equipment population having higher probability of
failure. This technique is useful for gateway cables. Gateway cables are several hundreds
feet long, and the testing of these cables requires them to be out of service during the test.
Because of the required outage, it becomes expensive and inconvenient to test these
cables.
Using a Markov chain model, the authors defined various possible states such as
equipment population, failure, repair and testing. Transition between these states was
characterized probabilistically. The Markov based testing program was able to identify a
subpopulation that has a much higher probability of failure.
In 1993, Csenki used a Markov model for the reliability analysis of the recovery
blocks [19]. The technique of the recovery blocks is a well-known fault tolerant software
method in which many alternative modules are used for the same problem. Finally the
results are verified by an acceptance test. Experiments show that the failure points in the
input domain of the software are not isolated but form clusters, each one attributable to a
common software fault. The author has described a reliability model for a recovery block.
This model consists of a primary module, an acceptance test and two alternate modules.
A Markov model is developed to obtain first and second moments of the successful input
points for recovery blocks and thus it is used to study reliability of recovery blocks when
the series of input values traverse nested clusters of failure points in the input domain.