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Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints

机译：通过使用非周期性干预频率并考虑季节限制的遗传算法优化监视测试策略

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摘要

In order to maximize systems average availability during a given period of time, it has recently been developed a non-periodic surveillance test optimization methodology based on genetic algorithms (GA). The fact of allowing non-periodic tests turns the solution space much more flexible and schedules can be better adjusted, providing gains in the overall system average availability, when compared to those obtained by an optimized periodic test scheme. This approach, however, turns the optimization problem more complex. Hence, the use of a powerful optimization technique, such as GA, is required. Considering that some particular features of certain systems can turn it advisable to introduce other specific constraints in the optimization problem, this work investigates the application of seasonal constraints for the set of the Emergency Diesel Generation of a typical four-loop pressurized water reactor in order to planning and optimizing its surveillance test policy. In this analysis, the growth of the blackout accident probability during summer, due to electrical power demand increases, was considered. Here, the used model penalizes surveillance test interventions when the blackout probability is higher. Results demonstrate the ability of the method in adapting the surveillance test policy to seasonal constraints. The knowledge acquired by the GA during the searching process has lead to test schedules that drastically minimize test interventions at periods of high blackout probability. It is compensated by more frequent redistributed tests through the periods of low blackout probability in order to improve on the overall average availability at the system level.
机译：为了在给定的时间内最大化系统的平均可用性，最近已经开发了一种基于遗传算法（GA）的非定期监视测试优化方法。与通过优化的定期测试方案获得的结果相比，允许非定期测试的事实使解决方案空间更加灵活，可以更好地调整计划，从而提高了整体系统的平均可用性。但是，这种方法使优化问题变得更加复杂。因此，需要使用强大的优化技术，例如GA。考虑到某些系统的某些特殊功能可能会在优化问题中引入其他特定约束，因此本研究调查了季节性约束在典型的四回路加压水反应堆应急柴油机组中的应用。规划和优化其监视测试策略。在此分析中，考虑了由于电力需求增加而导致的夏季停电事故概率的增长。在此，当停电概率较高时，使用的模型会惩罚监视测试干预措施。结果证明了该方法使监视测试策略适应季节限制的能力。遗传算法在搜索过程中获得的知识已导致制定测试计划，从而在停电概率很高的时期极大地减少了测试干预。为了降低系统级别的总体平均可用性，可以在中断概率较低的时期内通过更频繁地重新分配测试来弥补这一缺陷。

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