Idenntification of Parameters for Power System Stability Analysis Using Kalman Filter

Namba, Masayuki; Nishiwaki, Tadao; Yokokawa, S.; Ohtsuka, Kei; Ueki, Yoshiteru

doi:10.1109/tpas.1981.316671

Cited by 41 publications

(2 citation statements)

References 6 publications

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“…Numerous approaches for estimating power system inertia can be found in the literature, including model-based and measurement-based methods [6]. Model-based inertia estimation includes a single synchronous generator (SG) estimation [7] and regional system inertia estimation [8]. Ambient data-based inertia estimation [9] and large disturbancebased estimation methods [10,11] belong to measurement-based inertia estimation approaches.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Power System Inertia with the Integration of Converter-Interfaced Generation via MEMD during a Large Disturbance

Muftić Dedović,

Mujezinović,

Dautbašić

et al. 2024

Applied Sciences

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The decrease in overall inertia in power systems due to the shift from synchronous generator production to renewable energy sources (RESs) presents a significant challenge. This transition affects the system’s stable frequency response, making it highly sensitive to imbalances between production and consumption, particularly during large disturbances. To address this issue, this paper introduces a novel approach using Multivariate Empirical Mode Decomposition (MEMD) for the accurate estimation of power system inertia. This approach involves applying MEMD, a complex signal processing technique, to power system frequency signals. The study utilizes PMU (Phasor Measurement Unit) data and simulated disturbances in the IEEE 39 bus test system to conduct this analysis. MEMD offers substantial advantages in analyzing multivariate data and frequency signals during disturbances, providing accurate estimations of system inertia. This approach enhances the understanding of power system dynamics in the context of renewable energy integration. However, the complexity of this methodology and the requirement for precise data collection are challenges that need to be addressed. The results from this approach show high accuracy in estimating the rate of change of frequency (RoCoF) and system inertia, with minimal deviation from actual values. The findings highlight the significant impact of renewable energy integration on system inertia and emphasize the necessity of accurate inertia estimation in modern power systems.

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Section: Introductionmentioning

confidence: 99%

Estimation of Power System Inertia with the Integration of Converter-Interfaced Generation via MEMD during a Large Disturbance

Muftić Dedović,

Mujezinović,

Dautbašić

et al. 2024

Applied Sciences

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show abstract

“…Our "PG-agnostic" approach should be contrasted with "PG-informed" studies, some dated back to the 1980s [2], [3], [4], [5], reconstructing parameters in the corresponding system of DAEs. Complexity of the DAEs makes the reconstruction task difficult and thus computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Learning a Generator Model from Terminal Bus Data

Stulov,

Sobajic,

Maximov

et al. 2019

Preprint

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In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques. The goal is to develop an emulator which is trained online and is capable of fast predictive computations. The training is illustrated on synthetic data generated based on available open-source dynamical generator model. Two ML techniques were developed and tested: (a) standard vector auto-regressive (VAR) model; and (b) novel customized long short-term memory (LSTM) deep learning model. Trade-offs in reconstruction ability between computationally light but linear AR model and powerful but computationally demanding LSTM model are established and analyzed.

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Synchronous machine parameters estimation using a new genetic-based algorithm

El-Naggar

Youssef

1999

Int. J. Energy Res.

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Idenntification of Parameters for Power System Stability Analysis Using Kalman Filter

Cited by 41 publications

References 6 publications

Estimation of Power System Inertia with the Integration of Converter-Interfaced Generation via MEMD during a Large Disturbance

Estimation of Power System Inertia with the Integration of Converter-Interfaced Generation via MEMD during a Large Disturbance

Learning a Generator Model from Terminal Bus Data

Synchronous machine parameters estimation using a new genetic-based algorithm

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