This paper summarizes a review of the distribution system state estimation (DSSE) methods, techniques, and their applications in power systems. In recent years, the implementation of a distributed generation has affected the behavior of the distribution networks. In order to improve the performance of the distribution networks, it is necessary to implement state estimation methods. As transmission networks and distribution networks are not similar due to variations in line parameters, buses, and measuring instruments, transmission state estimation cannot be implemented in distribution state estimation. So, some aspects, such as accuracy, computational time, and efficiency, should be taken into account when designing distribution state estimation methods. In this paper, the traditional methods are reviewed and analyzed with data-driven techniques in order to present the advantages and disadvantages of the various methods.

Large and complicated datasets may now be generated utilising device reading machine learning approaches, which can subsequently be used to model and study substances in a variety of ways, along with people who require robotics and automation. For data analysis, there was a delay in implementing device learning methodologies since nanomaterials have not yet achieved the overall benefits of automation. There has been an explosion in the number of tools available for learning about nanomaterials, but there are still significant roadblocks in the way of actually putting those tools to use in a practical way. The homes of nanoparticles can be examined and anticipated with the help of system learning algorithms, and this painting shows how classic and deep system mastery techniques may be done to preserve nanomaterials. Among the topics covered are the history of nanoprotection, as well as a forecast for the future of artificial intelligence’s (AI) role in the field in the near future.