Characterization of electrical sags and swells using higher-order statistical estimators

Agüera-Pérez, Agustín; Palomares-Salas, José Carlos; Rosa, Juan José González de la; Sierra-Fernández, José María; Ayora-Sedeño, Daniel; Moreno-Muñoz, Antonio

doi:10.1016/j.measurement.2011.05.014

Cited by 46 publications

(39 citation statements)

References 11 publications

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“…This length is a complete number of cycles of the base sinusoid of the power signal. Statistical distribution of signal points will be the same, and consequently, all moments will be the same [16] . So a window, with a length exactly a complete number of cycles of the base frequency of the power signal, in this work one cycle has been selected, is taken and swept along the signal.…”

Section: Hos and Pq Indexmentioning

confidence: 99%

Evaluation of a new Power Quality index, based in Higher Order Statistics

Sierra-Fernández¹,

Rosa²,

Agüera-Pérez³

et al. 2024

RE&PQJ

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Abstract.The present works deals with the presentation and test of a novel Power Quality index, based in Higher-Order cumulants. Synthetics are used for test different start point, amplitude and length for the most common Power Grid disturbances (DIP, Oscillatory Transient, Harmonic Temporal Distortion and Impulsive Transient), obtaining a high accuracy (over 99% in some disturbances). Then real signals are used for confirm synthetics results. Finally, the Power Quality index presented, is confirmed with real signals as a good tool for detect imperfections in the power waveform.

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Section: Hos and Pq Indexmentioning

confidence: 99%

Evaluation of a new Power Quality index, based in Higher Order Statistics

Sierra-Fernández¹,

Rosa²,

Agüera-Pérez³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…Cumulants, defined in the Equation 6, are estimated by using the well-known Leonov-Shiryaev formula, which expresses the compact relationship among the cumulants of stochastic signals and their moments [9]. In this sense, the expressions for the second-, third-, and fourth-order cumulants for a real, random, and zero-mean (central cumulants) time series x(t) can be estimated via:…”

Section: Appendixmentioning

confidence: 99%

“…The same authors performed the classification of single and multiple disturbances using HOS in the time domain and Bayes' theory-based techniques [8]. HOS techniques and estimators have also been implemented to specifically detect sags and swells [9].…”

Section: Introductionmentioning

confidence: 99%

HOS network-based classification of power quality events via regression algorithms

Palomares-Salas

Rosa

Fernández

et al. 2015

EURASIP J. Adv. Signal Process.

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This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 power-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility between all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. These results verify that the proposed technique is capable of offering interesting results for power quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data.

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“…It is worthy to mention, at this juncture, that in AC network source voltages are relatively pure sinusoidal waveforms and that it is mostly at the transmission and distribution end that harmonic and other forms of distortions are introduced. The use of statistical estimators to detect distortions like sags and swells using synthetics analysis that allowed the identification of the variance, skewness, and kurtosis associated with normal sinusoidal waveforms was proposed in [18]. The increase use of nonlinear loads like computers, fluorescent lamps, adjustable speed drive motors, arc furnaces, arc welding machines, electronic control and power converters among others cause harmonics.…”

Section: Walsh Function Algorithmmentioning

confidence: 99%

An Improved Walsh Function Algorithm for Use in Sinusoidal and Nonsinusoidal Power Components Measurement

et al. 2013

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This paper presents an improved Walsh function IWF algorithms as an alternative approach for active and reactive powers measurement in linear and nonlinear, balanced and unbalanced sinusoidal three-phase load system. It takes advantage of Walsh function unified approach, simple algorithm and its intrinsic high level of accuracy as a result of coefficient characteristics and energy behaviour representation. The developed algorithm was modeled on the Matlab Simulink software; different types of load, linear and nonlinear, were also modeled based on practical voltage and current waveforms and tested with the proposed improved Walsh algorithms. The IEEE standard 1459-2000 which is based on fast Fourier transform FFT approach was used as benchmark for the linear load system. The data obtained from laboratory experiment to determine power components in harmonic load systems using Fluke 435 power quality analyzer PQA which complies with IEC/EN61010-1-2001 standard was modeled and used to validate the improved algorithm for nonlinear load measurement. The results showed that the algorithm has the potential to effectively measure three-phase power components under different load conditions.

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Characterization of electrical sags and swells using higher-order statistical estimators

Cited by 46 publications

References 11 publications

Evaluation of a new Power Quality index, based in Higher Order Statistics

Evaluation of a new Power Quality index, based in Higher Order Statistics

HOS network-based classification of power quality events via regression algorithms

An Improved Walsh Function Algorithm for Use in Sinusoidal and Nonsinusoidal Power Components Measurement

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