A Subspace Identification Technique for Real-Time Stability Assessment of Droop Based Microgrids

Hamalawy, Abdallah F. El; Ammar, Mohammed E.; Sindi, Hatem; Shaaban, Mostafa F.; Zeineldin, H. H.

doi:10.1109/tpwrs.2021.3126809

Cited by 4 publications

(3 citation statements)

References 41 publications

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“… SSI algorithm : The SSI algorithm is a technique for the identification of linear systems [47] and is frequently employed in multiple‐input multiple‐output systems, primarily due to its capacity to recognize the state‐space model of the system [1]. For the stochastic system shown in Equation (3), the Hankel matrix is first constructed for the measured time series data.…”

Section: Oscillation Modal Estimation Algorithmsmentioning

confidence: 99%

“…Large-scale integration of distributed energy sources (DERs) in the power system landscape increases the deployment of the power electronic converter, smart inverters and related loads [1]. According to the utility-scale solar report by the Lawrence Berkeley National Laboratory, the proportion of utility-scale PV in the United States would increase from 15 to 40 GW in 2020 and 2030, which is nearly three times the current solar capacity [2].…”

Section: Introductionmentioning

confidence: 99%

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Power system wideband oscillation estimation, localization, and mitigation

Qiu

et al. 2023

IET Generation Trans & Dist

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With the development of the energy structure of the power system, the characterization of the observed oscillation in the power system has steadily evolved. With the increasingly high penetration of distributed energy sources and power electronic devices, such as the photovoltaic, wind power and charging pile, the basic mechanism, frequency ranges and propagation of oscillation would become more dynamic and complex. Therefore, the traditional parameter estimation, as well as the suppression methods would also suffer from dynamic oscillations. To this end, this paper first investigates the oscillation propagation and typical events caused by the oscillations. Next, the oscillation model estimation method including the model-based and data-driven based methods is discussed. The benefits and difficulties of various parameter estimate techniques are highlighted. Under this context, the validity and limitations of the current power system stability are summarized. Furthermore, the different oscillation damping and mitigation measures are compared, and the major adaptability and limitations are listed for the specific application scenarios. Conclusions are drawn, and the wideband oscillation becomes the future research objective where the future work is presented at the end of the paper.

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Section: Oscillation Modal Estimation Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power system wideband oscillation estimation, localization, and mitigation

Qiu

et al. 2023

IET Generation Trans & Dist

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“…First, the local linear models are the most commonly used system identification method and develop a high-order linear system with input and output to approximate the system dynamics near an equilibrium point. From the perspective of linear system identification [5], existing methods include Prony analysis [6], [7] and state space methods, e.g., Minimal Realization algorithm [8], Eigenvalue Realization Algorithm (ERA) [9], Matrix Pencil method [10], Hankel Total Least Squares (HTLS) [11], subspace identification [12], Dynamic Mode Decomposition (DMD) [13], etc. Beyond the linear system perspective, there are nonparametric spectrum estimation such as Welch periodgram [14] and parametric methods, including Yule-Walker [15], Frequency Domain Decomposition [16], etc.…”

Section: Introductionmentioning

confidence: 99%

Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids

Jiang¹,

Li²,

Huang³

2022

Preprint

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Modularized Koopman Bilinear Form (M-KBF) is presented to model and predict the transient dynamics of microgrids in the presence of disturbances. As a scalable data-driven approach, M-KBF divides the identification and prediction of the high-dimensional nonlinear system into the individual study of subsystems; and thus, alleviating the difficulty of intensively handling high volume data and overcoming the curse of dimensionality. For each subsystem, Koopman bilinear form is applied to efficiently identify its model by developing eigenfunctions via the extended dynamic mode decomposition method with an eigenvalue-based order truncation. Extensive tests show that M-KBF can provide accurate transient dynamics prediction for the nonlinear microgrids and verify the plug-and-play modeling and prediction function, which offers a potent tool for identifying high-dimensional systems. The modularity feature of M-KBF enables the provision of fast and precise prediction for the microgrid operation and control, paving the way towards online applications.

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