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Maximizing Grid Efficiency: Exploring Advanced Techniques for State Estimation and Monitoring

Maximizing Grid Efficiency: Exploring Advanced Techniques for State Estimation and Monitoring

Smart grid

Optimal micro-PMU placement and virtualization for distribution network changing topologies

In this blog post, we will discuss a novel approach to optimize the placement and virtualization of micro-phasor measurement units (micro-PMUs) in distribution networks with changing topologies. Micro-PMUs are devices that can measure the voltage and current phasors at high sampling rates, providing accurate and synchronized information about the power system state. Micro-PMUs can enable various applications such as fault detection, state estimation, voltage control, and power quality monitoring.

However, micro-PMUs are also expensive and limited in number, so their placement should be carefully planned to maximize the observability of the network. Moreover, distribution networks are often subject to topology changes due to switching operations, load variations, distributed generation, and faults. These changes can affect the observability and performance of the micro-PMU-based applications. Therefore, it is desirable to have a flexible and adaptive micro-PMU placement scheme that can cope with the dynamic nature of distribution networks.

One possible solution is to use micro-PMU virtualization, which is a technique that allows creating virtual micro-PMUs at locations where there are no physical devices installed. Virtual micro-PMUs can be obtained by combining the measurements from multiple physical micro-PMUs using linear transformations. Virtualization can increase the observability of the network and reduce the number of required physical devices.

However, virtualization also introduces some challenges, such as how to select the optimal locations for both physical and virtual micro-PMUs, how to determine the optimal linear transformations for virtualization, and how to update the placement and virtualization scheme when the network topology changes. In this blog post, we will present a mathematical formulation and a solution algorithm for these problems, based on mixed-integer linear programming (MILP) and graph theory. We will also show some numerical results to demonstrate the effectiveness of our approach.

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