Key operational and protection functions of power systems (e.g., optimal power flow scheduling and control, state estimation (SE), protection, and fault location) rely on the availability of models to represent the system’s behavior under different operating conditions. Power system models require knowledge of the components’ electrical parameters and the system topology. However, these data may be inaccurate for several reasons (e.g., inaccurate information of components datasheets and/or outdated topological information). The deployment of time synchronization in phasor measurement units (PMUs) and remote terminal units (RTUs) enables the collection of large datasets of synchronized measurements to infer power system models and learn associated power flow constraints. Within this context, this article presents a comprehensive review of measurement-based estimation methods for power flow models using time-synchronized measurements. It begins by exploring advancements in time dissemination technologies and the characterization of uncertainties in PMUs and instrument transformers (ITs), along with their implications for parameter estimation. This article then examines the power system parameter estimation problem, highlighting key techniques and methodologies. In the following, this article focuses on measurement models for state-independent power flow model estimation, including line parameters, admittance matrices, topology, and joint state-parameter estimation. Finally, this article discusses recent approaches for estimating state-dependent power flow models, with particular reference to linearized power flow approximations because of their large use in control applications.

A hybrid control system for LC network filters is proposed in this paper, which enables coupling energy storage systems with the main grid suitably. The proposed control system is developed by combining both linear and predictive control approaches: particularly, the outer grid current loop is designed by means of PI regulators, whereas the intermediate capacitor voltage and the inner inductor current loops are designed in a deadbeat predictive control fashion. As a result, three nested loops have been achieved, whose performance can be set properly through dynamic decoupling and multi-rate sampling. The effectiveness of the proposed solution is verified through numerical simulations, which refer to different grid operating conditions in terms of active/reactive power exchanges.

This paper presents an IoT cloud-based state estimation system for distribution networks in which the PMUs (Phasor Measurement Units) are virtualized with respect to the physical devices. In the considered system only application level entities are put in the cloud, whereas virtualized PMUs are running in the communication network edge (i.e. closer to the physical objects) in order to have a certain degree of local logic, which allows to implement a bandwidth-efficient and smart data transmission to the involved applications in the cloud. The major contributions of the paper are the following: we demonstrate that a cloud-based architecture is capable of achieving the QoS level required by the specific state estimation application; we show that implementing a certain local logic for data transmission in the cloud, the result of the state estimation is not degraded with respect to the case of an estimation that takes place frequently at fixed intervals; we show the results in terms of latency and reduced network load for a reference smart grid network.