Data-driven voltage regulation in smart grids using distribution phasor measurement units insights

Abstract

The increasing penetration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, is rapidly transforming distribution networks (DNs) from passive systems into active, complex environments. This shift introduces challenges related to voltage regulation, harmonic distortion, system observability, and control. The variability and unpredictability of renewable generation further intensify the need for novel strategies to ensure grid stability and efficient operation. This thesis addresses these challenges by proposing and validating advanced measurement and control techniques tailored for modern distribution networks.First, to improve network observability, a Fully Informative Distribution Phasor Measurement Unit (FI-DPMU) capable of simultaneously estimating phasor dynamics and harmonic content isintroduced. Unlike traditional D-PMUs, which discard harmonic information, the FI-DPMU provides a comprehensive view of the network’s electrical characteristics, functioning both as a synchrophasor device and a power quality analyser. The device’s effectiveness is validated through hardware-in-the-loop simulations, demonstrating compliance with the IEC/IEEE 60255-118-1 standard and superior performance under conditions of frequency variability and harmonic distortion. Its low computational requirements and compatibility with low-cost microcontrollers support wide-scale deployment in practical settings.Building on this improved monitoring, the second contribution of the thesis presents a data- driven voltage regulation approach that leverages D-PMU measurements to coordinate dis- tributed energy resources and D-STATCOMs across DN. By computing a voltage performance index in real time using only voltage and reactive power data, the proposed control algorithm effectively regulates node voltages without relying on detailed system models. This approach ensures fast, accurate, and scalable Volt/Var control even under dynamic conditions such as solar intermittency, grid faults, and topology changes. Moreover, it lays the groundwork for digital twin applications and enhances system identification capabilities. Finally, recognizing the need for localized, scalable solutions in high-DER environments, a decentralized voltage control strategy for clustered PV inverters is introduced. By organizing inverters into logical groups and applying adaptive, droop-based reactive power control at the point of common coupling, the method maintains voltage stability while prioritizing units with the largest voltage deviations. Entirely data-driven and communication-decentralized among inverters, this approach reduce coordination overhead and facilitates the integration of high PV penetration in low-voltage networks.Together, these results form a comprehensive framework for enhancing observability, stability, and control in distribution networks. By combining advanced measurement techniques with data-driven control methods, this thesis offers a robust and practical toolkit for managing the technical complexities associated with the modern power systems.