Reliability and Availability Optimization of Smart Microgrid Using Specific Configuration of Renewable Resources and Considering Subcomponent Faults

In this paper, renewable resources, namely photovoltaic panels (PV), are placed in a specific configuration to obtain the maximum reliability and availability of a microgrid and study the subcomponent-level reliability and availability. The reliability of components can be increased by trying different configurations of the components. We identify the preferred configuration used for the PV panels as bridged linked. The overall reliability of the microgrid is increased when component-wise reliability is considered. Even components are further divided into subcomponents, and the multiple faults of each component are considered. The method used for the reliability evaluation and availability study is Markov state transition modeling. The microgrid's reliability and availability are plotted concerning time using Matlab. The optimization of reliability and availability is conducted through optimization techniques such as the genetic algorithm (GA) and artificial neural networks (ANN). The results are compared and validated for the optimal values of mean time to failure (MTTF) and mean time to repair (MTTR). Using a genetic algorithm, there is a 96% of improvement in the reliability, and after applying the neural networks, a significant improvement of 97% along with quick results is achieved.