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.

Intermittency in the grid creates operational issues for power system operators (PSO). One such intermittent parameter is load. Accurate prediction of the load is the key to proper planning of the power system. This paper uses regression analyses for short-term load forecasting (STLF). Assumed load data are first analyzed and outliers are identified and treated. The cleaned data are fed to regression methods involving Linear Regression, Decision Trees (DT), Support Vector Machine (SVM), Ensemble, Gaussian Process Regression (GPR), and Neural Networks. The best method is identified based on statistical analyses using parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), $R^2$, and Prediction Speed. The best method is further optimized with the objective of reducing MSE by tuning hyperparameters using Bayesian Optimization, Grid Search, and Random Search. The algorithms are implemented in Python and Matlab Platforms. It is observed that the best methods obtained for regression analysis and hyperparameter tuning for an assumed data set are Decision Trees and Grid Search, respectively. It is also observed that, due to hyperparameter tuning, the MSE is reduced by 12.98%.

Energy Sources with high intermittencies have made the grid complex. These sources utilize renewable resources to generate clean energy, but power management and power delivery are badly affected. Stochastic forecasting and optimized power scheduling techniques can help to solve the issue of reliable power delivery and reduce the complexity of the distribution system. In this paper, load and intermittency forecasting of DER (Distributed Energy Resources) is done by stochastic techniques. Using the obtained forecasted parameters, power is scheduled to minimize the day’s operating cost with constraints modeled from DER and power flow of the system. The DER used are MG (Microgrid), DG (Distributed Generation), and DS (Distribution Storage). The problem is formulated using YALMIP and solved using GUROBI in the MATLAB platform. A 33-bus system is used as a test system to validate the algorithm. The proposed method results in a 20.79% reduction in operating cost when compared to nonforecasted intermittency and load, nonoptimized scheduling conditions. Also, an 8.96% reduction in operating cost is obtained compared to optimized scheduling and nonforecasted intermittency and load conditions.