An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram

This paper presents the experimental set-up of identification of harmonic source location in the power distribution network using time-frequency analysis, known as S-transform (ST) at the point of common coupling (PCC). S-transform offers high frequency resolution in analyzing the low frequency component and able to represent signal parameters in time-frequency representation (TFR) such as TFR impedance (ZTFR). The proposed method is based on IEEE Std. 1459-2010, ST, and the significant relationship of spectral impedances components (ZS) that been extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). This experiment was conducted out on an IEEE 4-bus test feeder with a harmonic producing load in numerous different scenarios. The experimental was tested and verified for three consecutive months. The findings of this study reveal that the proposed method provides 100 percent correct identification of harmonic source location.

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“…− Instantaneous root-mean square fundamental voltage The instantaneous RMS fundamental voltage (V1rms(t)) can be computed as [79],…”

Section: − Instantaneous Root-mean Square Voltagementioning

confidence: 99%

Identification of harmonic source location in power distribution network

Jopri

Skamyin

Manap

et al. 2022

IJPEDS

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“…In a number of papers, it is proposed to determine the parameters of inverters and rectifiers based on various methods, such as time-frequency analysis technique [33,34], periodogram technique [35,36], and machine learning algorithm [37,38]. Basically, such an analysis is carried out for the classification and localization of nonlinear loads [39,40].…”

Section: Introductionmentioning

confidence: 99%

Computation of Nonlinear Load Harmonic Currents in the Presence of External Distortions

et al. 2022

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This paper deals with the issues of computation of the nonlinear load harmonic currents in the presence of external distortions based on the real measurements with help of passive harmonic. Such values are necessary when modeling an equivalent nonlinear load as current sources in the presence of external distortions. The passive filter allows to separate external and internal distortions, which is necessary when determining the harmonic currents of a single consumer. The influence of various parameters on the computation of harmonic currents of a nonlinear load, such as the parameters of a passive harmonic filter, the impedance of the power supply transformer, the load parameters of the consumer, taking into account the harmonic generation from the grid side, and from the consumer side, is considered. It is shown that an external source of distortion has practically no effect on the error in estimating the harmonic current magnitudes of a nonlinear load. The obtained simulation results were confirmed experimentally on a laboratory test bench. Recommendations for the selection of passive harmonic filter parameters have been developed to minimize the error in determining the harmonic current magnitudes of a nonlinear load.

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“…As a result, the harmonic distortion has increased losses and reduced the life expectancy of the equipments [3][4][5]. Therefore, it is important to define suitable methodologies to attribute the responsibilities for the deterioration of the power quality [6,7]. There are numerous methods have been suggested by researchers to identify the location of harmonic sources based on different theoretical principles, features, benefits and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

Jopri¹,

Abdullah²,

Too³

et al. 2020

IJEECS

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<span>A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.</span>

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An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram

Cited by 5 publications

References 14 publications

Identification of harmonic source location in power distribution network

Identification of harmonic source location in power distribution network

Computation of Nonlinear Load Harmonic Currents in the Presence of External Distortions

Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

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