Multiple Data Sources in Reliability Assessment of Distribution Networks

Abstract

Since distribution networks provide the regular and secure transmission of power to consumers, its dependability is essential to the smooth operation of modern society. Conventional techniques for evaluating these networks' dependability have depended on a single data source, such as historical outage statistics. The capacity to combine many data sources into dependability evaluation has been possible due to technological improvements and the abundance of available data. This allows for a more thorough and precise understanding of network performance. This technical abstract proposes a methodology for incorporating multiple data sources in the reliability assessment of distribution networks. It is to identify and collect various data sources such as weather data, customer complaints, equipment maintenance records, and outage data. These data sources are then integrated and analyzed using statistical and machine learning algorithms to identify patterns and correlations that can provide insights into the network's reliability.