A sparse representation-based algorithm for the voltage fluctuation detection of a power system

Sun, Dong; Gao, Qingwei; Zhu, Meiling

doi:10.1016/j.dsp.2015.09.022

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Optimization-based techniques are proposed for voltage fluctuations monitoring [21,22,23]. Hasawi et al [21] S-transform (ST) based techniques offer improved timefrequency visualization for voltage flicker monitoring [24,25].…”

Section: A Review Of Related Work and Motivationmentioning

confidence: 99%

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A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems

et al. 2023

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The voltage fluctuations are caused by variations in renewable energy source outputs, increased usage of nonlinear loads, high reactive power consumption of loads, etc. This results in damage to electric components and enormous economic losses. It is necessary to measure the parameters of voltage fluctuations and flicker levels to achieve a secure supply of power. However, higher-order harmonics and noises in voltage waveform make such assessment a burdensome task. The traditional techniques fail to provide exact monitoring due to the uncharacteristic variations in the input voltage signal. Nowadays, data-driven methods have become a prominent choice for non-stationary, nonlinear signal analysis as it efficiently tackles atypical changes by its capability to identify spatio-temporal information from given data. The present paper proposes a data-driven hybrid methodology for monitoring voltage fluctuations using the low-rank approximation of dynamic mode decomposition (DMD) and flat-top finite impulse response (FIR) filter. The low-rank approximation of DMD enables to identify the spatio-temporal coherent structures from the data by mapping the data into a lower-dimensional space. Adaptive flat-top FIR filter tracks the fundamental dynamic phasor by identifying the instantaneous frequency, magnitude and phase variations. The performance of the proposed hybrid methodology is assessed using different test cases, and is compared with various existing approaches. The promising results suggest that the proposed methodology can be used for accurate parameter identification and monitoring voltage fluctuations in smart grid scenarios. It can also be used as an accurate tool in distribution grids to detect higher-order harmonics. INDEX TERMSDynamic mode decomposition, Flat-top FIR filter, Power quality, Hankelization, Smart grid, Voltage fluctuations I. INTRODUCTION Voltage fluctuations are defined as random or systematic changes in the voltage envelope. According to the Information, Education & Communication (IEC) standards the voltage fluctuations are 10% of the rated voltage [1]. The major sources of voltage fluctuations are (1) the presence of nonlinear loads such as electric arc furnaces (EAF), arc welders, and large motors, (2) variations in renewable energy resources such as photovoltaic and wind power generator output, (3) increased reactive power consumption of loads, (4) switching of power factor capacitors, etc. The voltage fluctuations can result in undesired illumination effects

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Section: A Review Of Related Work and Motivationmentioning

confidence: 99%

“…-Convergence to local optimum -Computational complexity Sparse [22] ℓ 0 -norm based sparse representation is used for fluctuation detection. ℓ 2 -norm is used to extract components.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems

et al. 2023

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Analytic Mode Decomposition and Windowed Three-Point Interpolated Chirp-Z Transform for Voltage Flicker Components Detection

Gao

et al. 2020

Electric Power Systems Research

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A Novel AI-driven Hybrid Method for Flicker Estimation in Power Systems

Enayati,

Asef,

Yousefi

et al. 2024

2024 International Conference on Smart Energy Systems and Technologies (SEST)

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This paper introduces a novel hybrid method using a combination of an H-infinity filter and artificial neural network (ANN) to estimate flicker components within power distribution system voltages. The H-infinity filter first extracts the estimated envelope of the applied voltage waveforms, incorporating a new voltage fluctuation model that realistically accounts for both harmonic and flicker components. Furthermore, an ADALINE (adaptive linear neuron) extracts the specific flicker components within the envelope. The hybrid process decouples prediction states, enhancing convergence behavior. Additionally, it showcases robust flicker component tracking even in the presence of power harmonics and noise, offering advantages over traditional signal processing methods. The algorithm's performance in flicker estimation is validated through statistical analysis using Monte Carlo (MC) simulations and real world data.

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A sparse representation-based algorithm for the voltage fluctuation detection of a power system

Cited by 4 publications

References 27 publications

A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems

A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems

Analytic Mode Decomposition and Windowed Three-Point Interpolated Chirp-Z Transform for Voltage Flicker Components Detection

A Novel AI-driven Hybrid Method for Flicker Estimation in Power Systems

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