Monitoring harmonic distortion in microgrids using dynamic mode decomposition

Saldana, A. E.; Barocio, E.; Messina, A.R.; Ramos, Juan; Segundo, J. P.; Tinajero, G. A.

doi:10.1109/pesgm.2017.8274696

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…DMD is one of the data-driven methods that has the potential to be used in the power system application due to its robustness and estimation speed. Specifically, DMD has been used for electromechanical oscillation monitoring [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] estimation of inertia [17] and frequency [18,19], harmonic distortion monitoring [20,21], fault detection [22], load forecasting [23][24][25] and data-driven control for enhancing rotor angle stability [26,27]. Regarding the oscillation monitoring using DMD, there are still some challenges related to the amount of input data that need to be addressed for achieving real-time oscillation monitoring.…”

Section: Motivationmentioning

confidence: 99%

An improved dynamic mode decomposition for real‐time electromechanical oscillation monitoring in power systems: The impact of ultra‐low frequency modes and its removal strategy

Fernández,

Iqbal,

Gusrialdi

2023

IET Generation Trans & Dist

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The growing complexity of power systems, driven by the increasing use of renewable energy sources, necessitates efficient real‐time monitoring of electromechanical oscillations, which is crucial for enhancing grid security. Dynamic Mode Decomposition (DMD) is a promising data‐driven method for addressing this challenge of monitoring the electromechanical oscillations, made possible by power system digitalization. However, DMD implementation faces unresolved issues, including the impact of ultra‐low‐frequency modes (ULFM) on accuracy due to trends caused by their excitation. While larger data windows can mitigate this, they slow down oscillation estimation. ULFM characteristics remain poorly studied. This study conducts a comprehensive ULFM analysis and proposes a solution by combining DMD with a high‐pass filter to counter ULFM's adverse effects on accuracy. This allows for a reduction in DMD's window size, significantly improving computational efficiency. Simulations on three test systems demonstrate that the enhanced DMD offers faster computation and superior accuracy compared to the traditional DMD method.

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Section: Motivationmentioning

confidence: 99%

An improved dynamic mode decomposition for real‐time electromechanical oscillation monitoring in power systems: The impact of ultra‐low frequency modes and its removal strategy

Fernández,

Iqbal,

Gusrialdi

2023

IET Generation Trans & Dist

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“…Recently, DMD has been explored for harmonics monitoring applications. In [21], harmonics monitoring in MGs is performed using DMD by utilizing its spatio-temporal decomposition ability. A novel methodology for harmonics identification using DMD is proposed in [1] using singular value thresholding.…”

Section: Dynamic Mode Decompositionmentioning

confidence: 99%

A data-driven technique for harmonics monitoring in emerging power grids using noise-aware dynamic mode decomposition

Mohan¹,

Soman²

2019

Meas. Sci. Technol.

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A future power grid should be more robust, efficient, renewable, stable, reconfigurable, resilient, and distributed with more advanced control, protection and security schemes. It will combine a myriad of technologies such as information, communication, and power system engineering with computational intelligence. Because of the high proliferation of renewable energy sources, distributed generation systems and non-linear loads, grids are affected by several distortions and issues related to quality, stability, and control. Harmonics monitoring is a primary task in power grids for their safe and stable operation. It is essential for the protection and control of microgrid systems. Detection of harmonics, inter-harmonics and sub-harmonics improves the quality supply of power and protects the consumer equipments from failures. This paper investigates the effectiveness of a noise-aware dynamic mode decomposition algorithm, namely total-dynamic mode decomposition (TDMD), for harmonics monitoring in power grids. The ability of the TDMD algorithm to extract the hidden dynamic characteristics of time-series data is exploited for harmonics identification and its analysis. In the proposed method, multiple time-shifted copies of measured power signals are appended to create the initial data matrices. A singular value decomposition-based hard-thresholding is performed to avoid the ambiguities in the measured signal. Further, the eigendecomposition is performed using the TDMD algorithm and the corresponding frequencies and amplitudes are estimated. The performance advantage of the proposed method is verified by conducting several experiments using simulated and field measurements. The satisfactory performance certifies the practical applications of the proposed method for harmonics monitoring in emerging power grids.

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“…That work has been mainly focused on the global dynamics of the electric grid. DMD has also been used for monitoring distortion of power system signals in microgrids [33].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Mode Decomposition Scheme to Analyze Power Quality Events

2021

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This paper presents a new method for detecting power quality disturbances, such as faults. The method is based on the dynamic mode decomposition (DMD) -a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The proposed method uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The paper shows how the proposed method can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed. In addition, a study on the effect that noise has on the proposed approach is presented.

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Monitoring harmonic distortion in microgrids using dynamic mode decomposition

Cited by 11 publications

References 17 publications

An improved dynamic mode decomposition for real‐time electromechanical oscillation monitoring in power systems: The impact of ultra‐low frequency modes and its removal strategy

An improved dynamic mode decomposition for real‐time electromechanical oscillation monitoring in power systems: The impact of ultra‐low frequency modes and its removal strategy

A data-driven technique for harmonics monitoring in emerging power grids using noise-aware dynamic mode decomposition

A Dynamic Mode Decomposition Scheme to Analyze Power Quality Events

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