arXiv subjects

Milos Katanic

Publications and source records attributed to Milos Katanic.

4 recordsLinked to original sources

arXiv2025

Optimal PMU Placement for Kalman Filtering of DAE Power System Models

Optimal sensor placement is essential for minimizing costs and ensuring accurate state estimation in power systems. This paper introduces a novel method for optimal sensor placement for dynamic state estimation of power systems modeled by differential-algebraic equations. The method identifies optimal sensor locations by minimizing the steady-state covariance matrix of the Kalman filter, thus minimizing the error of joint differential and algebraic state estimation. The problem is reformulated as a mixed-integer semidefinite program and effectively solved using off-the-shelf numerical solvers. Numerical results demonstrate the merits of the proposed approach by benchmarking its performance in phasor measurement unit placement in comparison to greedy algorithms.

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arXiv2024

A Proportional-Integral Model for Fractional Voltage Tripping of Distributed Energy Resources

In regions with high shares of distributed energy resources (DERs), massive disconnection of small-scale DERs in low-voltage distribution grids during disturbances poses a serious threat to power system security. However, modeling this effect in a computationally efficient way remains challenging. This paper proposes a novel proportional-integral aggregate model for predicting the fraction of tripped DERs based on the voltage at the substation connection point. The model effectively captures the cumulative behavior of the system, is simple to implement, and includes seven parameters for undervoltage tripping and seven for overvoltage tripping behavior, each with a distinct physical meaning. We further propose an optimization-based approach to tune the model parameters. Simulation results show significantly more accurate predictions compared to the DER\_A model -- a standard dynamic model for aggregate DER behavior -- even when the latter is optimized, with only a minor increase in model complexity.

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arXiv2023

Recursive Dynamic State Estimation for Power Systems with an Incomplete Nonlinear DAE Model

Power systems are highly complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This paper proposes a novel, centralized dynamic state estimator for power systems that lack models of some components. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, we apply the least squares criterion to estimate all dynamic and algebraic states recursively. The approach results in an algorithm that generalizes the iterated extended Kalman filter and does not require static network observability. We further derive a graph theoretic condition for placing phasor measurement units that guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature within computational times that are feasible for real-time application.

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arXiv2022

Moving-Horizon State Estimation for Power Networks and Synchronous Generators

Power network and generators state estimation are usually tackled as separate problems. We propose a dynamic scheme for the simultaneous estimation of the network and the generator states. The estimation is formulated as an optimization problem on a moving-horizon of past observations. The framework is a generalization of static state estimation; it can handle incomplete model knowledge and does not require static network observability by PMUs. The numerical results show an improved estimation accuracy compared to static state estimation. Moreover, accurate estimation of the internal states of generators without PMUs on their terminals can be achieved. Finally, we highlight the capability of the proposed estimator to detect and identify bad data.

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