μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

Abstract

Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing μPMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using μPMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work together to analyze bad data for both measurements and parameters.

WAMs Based Eigenvalue Space Model for High Impedance Fault Detection

“…Many techniques have been developed for dynamic state state estimation, most of them are based on the Kalman Filter [23][24][25]. Subspace estimation, used in this work, is now introduced.…”

“…However, due to increased integration of synchrophasor technology into the grid, solutions based on Wide Area Measurements (WAMS) made possible by Phasor Measurement Units (PMUs) are gaining momentum. Even at the distribution level, smaller and cheaper units called Micro-PMUs are being added to the smart grid [24].…”

“…During each iteration, the line is faulted twenty four (24) times to evaluate all the cluster zones and to account for dynamic loading conditions. • Clusters are built using historical loading data.…”

A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation

“…The model can represent dynamic phasors at fundamental, harmonic, and inter-harmonic frequencies with a high degree of accuracy, but the complexity of the TFM model makes CSTFM computationally demanding. Generally, the CSTFM algorithm significantly exceeds the current harmonic phasor specifications in accuracy, which makes the CSTFM method a potential alternative for harmonic or inter-harmonic phasor analysis in distribution networks [ 34 ].…”