In-service Restoration (SR), the healthy section of the feeder can be reenergized by finding the optimal path for power flow. Through conventional methods which are mainly deterministic in nature, the computational burden is very high. Therefore, researchers have proposed various meta-heuristic based methods to solve the SR problem. But since, these methods are probability based; one single algorithm cannot guarantee optimal solution for all scenarios. Hence, the authors have proposed a Machine Learning (ML) based framework, which can predict the best SR scheme for a particular fault scenario among the SR solutions obtained through various meta-heuristic algorithms. The supervised ML model is developed using the fault features as input values and the best performing meta-heuristic algorithm as the target value. To check the validity of the ML framework, the authors have taken four different meta-heuristic algorithms, which are, Enhanced Integer Coded Particle Swarm Optimization (EICPSO), Shuffled Frog Leaping Algorithm (SFLA), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Ant Colony Optimization (ACO) algorithm. The ML model can be extended for

This study examines the conceptual features of Fault Detection, Isolation, and Restoration (FDIR) following an outage in an electric distribution system.This paper starts with a discussion of the premise for distribution automation, including its features and the different challenges associated with its implementation in a smart grid paradigm. Then, this article explores various concepts, control schemes, and approaches related to FDIR. Service restoration is one of the main strategies for such distribution automation, through which the healthy section of the power distribution network is re-energized by changing the topology of the network. In a smart grid paradigm, the presence of intelligent electronic devices can facilitate the automatic implementation of the service restoration scheme. The concepts of service restoration and various approaches are thoroughly presented in this article. A comparison is made among various significant approaches reported for distribution automation. The outcome of our literature survey and scope for future research concludes this review.

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