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  1. 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of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data.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 workmore »together to analyze bad data for both measurements and parameters.« less
    Free, publicly-accessible full text available December 1, 2022
  2. In prior work, the Distributed Gradient Projection (DGP) algorithm was proposed to allow loads or load aggregators to provide contingency service to the grid using local frequency measurements. The DGP algorithm was shown to perform well in linear simulations. The goal of this work is to evaluate the performance of the DGP algorithm in more realistic scenarios and its robustness to issues of practical implementation, such as time delay, model mismatch, measurement noise, and stochastic disturbance. Simulation results from the IEEE 39-bus system indicate that the DGP algorithm performs well in mitigating the effects of contingencies and that it is robust to issues of practical implementation.