Linking damping of electromechanical oscillations to system operating conditions using neural networks

Sulla, Francesco; Masback, Emil; Samuelsson, Olof

doi:10.1109/isgteurope.2014.7028818

Cited by 5 publications

(4 citation statements)

References 9 publications

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“… Machine learning algorithm : Some work explores the application of machine learning methods for oscillation monitoring under the scenario of increasingly complex power grids [48, 49, 61, 62]. Generally, these machine learning‐based algorithms are applied for the purpose of online monitoring [63].…”

Section: Oscillation Modal Estimation Algorithmsmentioning

confidence: 99%

“…Poor effect on non-smooth signals Machine learning algorithms NN [48]; ARMA [49]; DMD [50]; PCA-WT [51] Online monitoring; Strong scalability Poor interpretability oscillatory mode estimation, which is combined with the Prony algorithm [42,57]. [44] is an improved ESPRIT algorithm, a subspace-based signal analysis method.…”

Section: References Advantages Disadvantagementioning

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: References Advantages Disadvantagementioning

confidence: 99%

Power system wideband oscillation estimation, localization, and mitigation

Qiu

et al. 2023

IET Generation Trans & Dist

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“…Moreover, (Liu et al, 2018) adopts a bagging ensemble called Random Forests (RF) to detect instability in the Danish power system considering forecasting errors of RES generation. Similarly, (Sulla et al, 2014) trains a Neural Network to classify operating points into over or under damped based on a fixed damping ratio.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Damping of Electromechanical Oscillations by Using Grid-Connected Converter

et al. 2021

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The proliferation of grid-connected converter interfaced energy sources in Smart Grids, enhance sustainability and efficiency as well as minimizing power losses and costs. However, concerns arise regarding the stability and reliability of future smart grids due to this wide integration of power electronic devices, which are recognized to affect the dynamic response of the system, especially during disturbances. For instance, apart from the lower damping of existing electromechanical modes, new low-frequency oscillations begin to appear. Yet, the ability of grid-connected converters to provide grid support functionalities can alleviate the aforementioned challenges. Relevant studies show that these functionalities can be enhanced even further, if information regarding the oscillation characteristics are available. Traditional methods for extracting modal information are very well suited for monitoring purposes, however, they pose certain limitations when considered for control applications. Therefore, this paper proposes a multi-band intelligent power oscillation damper (MiPOD) that exploits 1) the inherent characteristics of grid-connected converters to damp multiple power oscillations and 2) the modeling capabilities of Artificial Intelligence (AI) for predicting the frequency of electromechanical oscillations in the system, as operating conditions change. Essentially, the MiPOD integrates the AI model in the control loop of the converter to attenuate multiple modes of oscillation. The proposed controller is validated for different disturbances and randomly generated operating points in the two area system. Specifically, in this case the AI model is a Random Forest ensemble regressor that is developed for tracking two electromechanical modes. As it is shown, the MiPOD can improve the overall performance of the system under various contingency scenarios with only 6% of the corresponding total nominal capacity of synchronous generators. In addition, the monitoring and damping abilities of the MiPOD are demonstrated for a vast range of operating points just by tuning two parameters; the predicted oscillation frequencies of the local and inter-area mode.

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“…Furthermore, prediction of damping state (i.e. well or poorly damped) is proposed by [19] using decision trees and by [20] using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Grid-Forming Power Converters Tuned Through Artificial Intelligence to Damp Subsynchronous Interactions in Electrical Grids

et al. 2020

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The integration of non-synchronous generation units and energy storage through power electronics is introducing new challenges in power system dynamics. Specifically, the rotor angle stability has been identified as one of the major obstacle with regards to power electronics dominated power systems. To date, conventional power system stabilizer (PSS) devices are used for damping electromechanical oscillations, which are only tuned sporadically leading to significant deterioration in performance against the ever-changing operating conditions. In this paper, an intelligent power oscillation damper (iPOD) is proposed for grid-forming converters to attenuate electromechanical inter-area power oscillation. In particular, the iPOD is applied to a synchronous power controller (SPC) based grid-forming power converter to increases gain of the active power control loop at the oscillatory frequency. Predictions regarding the mode frequency, corresponding to the current operating points, are given by an artificial intelligence ensemble model called Random Forests. The performance of the proposed controller is verified using the two area system considering symmetrical fault for random operating points. In addition, a comparison with PSS installed in each generator reveals the individual contribution with respect to the inter-area mode damping.

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Linking damping of electromechanical oscillations to system operating conditions using neural networks

Cited by 5 publications

References 9 publications

Power system wideband oscillation estimation, localization, and mitigation

Power system wideband oscillation estimation, localization, and mitigation

AI-Based Damping of Electromechanical Oscillations by Using Grid-Connected Converter

Grid-Forming Power Converters Tuned Through Artificial Intelligence to Damp Subsynchronous Interactions in Electrical Grids

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