The monitoring of distribution systems via ad hoc state estimation techniques is essential to provide system awareness to distribution system operators and to enable advanced control functionalities. When estimating the operating conditions of the system, it is important not only to identify the state of the grid, but also to determine the confidence interval around the obtained estimates. The final accuracy of the state estimation results depends on the uncertainties associated to both the input measurements and the underlying grid model. This paper presents a branch-current-based state estimation formulation that includes the network parameters as additional state variables to be estimated in the estimation process. Available data associated with network parameters are treated as generic inputs with an associated uncertainty and their knowledge can be refined via the state estimation procedure. In this way, the proposed framework allows considering the accuracy characteristics of both measurements and grid model when extracting the output uncertainty of the estimates. Simulations on a sample distribution grid prove the validity of the proposed model and show the importance of considering the grid parameters uncertainties for determining the final state estimates together with their uncertainty.
An Augmented Branch Current Formulation for State Estimation in Distribution Systems
Pegoraro P. A.;Sulis S.;Muscas C.
2021-01-01
Abstract
The monitoring of distribution systems via ad hoc state estimation techniques is essential to provide system awareness to distribution system operators and to enable advanced control functionalities. When estimating the operating conditions of the system, it is important not only to identify the state of the grid, but also to determine the confidence interval around the obtained estimates. The final accuracy of the state estimation results depends on the uncertainties associated to both the input measurements and the underlying grid model. This paper presents a branch-current-based state estimation formulation that includes the network parameters as additional state variables to be estimated in the estimation process. Available data associated with network parameters are treated as generic inputs with an associated uncertainty and their knowledge can be refined via the state estimation procedure. In this way, the proposed framework allows considering the accuracy characteristics of both measurements and grid model when extracting the output uncertainty of the estimates. Simulations on a sample distribution grid prove the validity of the proposed model and show the importance of considering the grid parameters uncertainties for determining the final state estimates together with their uncertainty.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.