The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.

No abstract

No abstract

The advent of smart metering infrastructure has paved the way to enhance the accuracy of conventional distribution system models. However, the size and complexity of the smart meter data and information systems present new issues, including the cumbersome process of extracting and analyzing data from various data sources. The focus of this paper is to create a highly accurate distribution system model using Big Data from smart meters. It presents details on how traditional modeling can be improved by smart meter data as well as practical methods to make the data functional for analysis.

No abstract