Dynamic Parameter Estimation of Inter-area Oscillations in a Power System by a Combination of Kalman-Filtering and Wavelet Transformation Techniques

Maurer, Markus; Gutekunst, Florian; Scheffknecht, GÃ1⁄4nter

doi:10.3182/20140824-6-za-1003.02168

Cited by 2 publications

(2 citation statements)

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“…Because the EGFE process uses balanced data, the accuracy is suitable for performance evaluation. Accuracy is defined as shown in (22).…”

Section: ) Multi-class Confusion Matrixmentioning

confidence: 99%

“…In [21], the authors shared their experience with Nordic power systems using a KF technique for WAMS applications. Moreover, the authors of [22] introduced wavelet transformation with the random decrement (WTRD) technique [23], which can manage damping in the WT framework. These current adaptive monitoring techniques are well-defined time-varying analysis methods that can effectively characterize the oscillation features.…”

Section: Introductionmentioning

confidence: 99%

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Extended Geometric Feature Extraction Process for Detecting Multiple Frequency Oscillations in KEPCO System

et al. 2021

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In this study, a new algorithm dealing with time-varying modes for determining and tracing multiple frequency oscillations in power systems is proposed. Multiple modes or resonance of forced and natural oscillations can have severe effects on a power system. Therefore, it is crucial to recognize the dominant natural and forced oscillations because the oscillation mode possesses time-varying features that depend on the system operating conditions or changes in the parameters. The salient features of the proposed algorithm include the use of a time-series-based approach to recognize undesired modes (including multiple oscillations over a wide frequency range), tracing time-varying modes as the power system operating condition changes, and effectively determining and applying the oscillation features before implementing the corresponding control measures in the power system. In this study, multiple frequency oscillation scenarios for the test system and practical measurement data for a recent incident that occurred in Korea Electric Power Corporation (KEPCO) system are discussed. Therefore, the proposed algorithm can be practically applied in wide-area monitoring systems, not only for a single forced oscillation or local mode detection but also for system-wide inter-area mode recognition.

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“…Because the EGFE process uses balanced data, the accuracy is suitable for performance evaluation. Accuracy is defined as shown in (22).…”

Section: ) Multi-class Confusion Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended Geometric Feature Extraction Process for Detecting Multiple Frequency Oscillations in KEPCO System

et al. 2021

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Data-driven Analysis for Low Frequency Oscillation Identification in Smart Grid using FB-DMD and T-DMD Methods

Priyanga

Chandni

Mohan

et al. 2019

2019 9th International Conference on Advances in Computing and Communication (ICACC)

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Dynamic Parameter Estimation of Inter-area Oscillations in a Power System by a Combination of Kalman-Filtering and Wavelet Transformation Techniques

Cited by 2 publications

References 5 publications

Extended Geometric Feature Extraction Process for Detecting Multiple Frequency Oscillations in KEPCO System

Extended Geometric Feature Extraction Process for Detecting Multiple Frequency Oscillations in KEPCO System

Data-driven Analysis for Low Frequency Oscillation Identification in Smart Grid using FB-DMD and T-DMD Methods

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