February 26, 2022
Journal Article

Admittance Matrix Validation for Power Distribution System Models Using a Networked Equipment Model Framework

Abstract

The evolving nature of electric power distribution systems is motivating the development of advanced applications for utility planning, operation, management, and control. These model-based applications interact with multiple subsystems within software platforms referred to as advanced distribution management systems (ADMS). Recently, CIM-based models have been used to enable data model standardization across several entities. However, distribution system models are error-prone and model validation is challenging due to: (1) the need for considerable effort and time to maintain the network models, (2) delays in model updating due to the evolving nature of the real-world systems that models are derived from, and (3) inevitable human errors in the process which are difficult to detect. In this paper, we extend a model validation application from our prior work referred to as the Model Validator (MV) to derive and validate a system admittance matrix from a CIM model. First, different modules are introduced for computing device-level primitive admittance matrices from a CIM model. These individual primitive admittance matrices are then validated against the appropriate entries in the full system admittance matrix. In addition, we also validate the overall system admittance matrix for checking the gaps in coverage or extra elements that are not expected. The MV application is developed and integrated with an open-source standards-based platform for ADMS application development, GridAPPS-D. The effectiveness of the proposed application is demonstrated on six different test cases: four IEEE feeders, an EPRI circuit, and a PNNL taxonomy feeder.

Published: February 26, 2022

Citation

Poudel S., G.D. Black, E.G. Stephan, and A.P. Reiman. 2022. Admittance Matrix Validation for Power Distribution System Models Using a Networked Equipment Model Framework. IEEE Access 10. PNNL-SA-166952. doi:10.1109/ACCESS.2022.3144124

Research topics