A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

Jopri, Mohd Hatta; Abdullah, Abdul Rahim; Sutikno, Tole; Manap, Mustafa; Ghani, Mohd Ruddin Ab; Hussin, A.S.

doi:10.11591/ijece.v8i6.pp5399-5408

Cited by 2 publications

(2 citation statements)

References 16 publications

(17 reference statements)

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“…Furthermore, a diagnosis study using a short-time fourier transform (STFT) and S-transform based on a rules-based classifier is introduced in [22][23][24]. However, high technical knowledge is essential due to  ISSN: 2302-9285 distinguish the rules-based classifier threshold parameter values [25,26]. Power quality analysis is a tricky task as the presence of complex-linear, as well as nonlinear patterns in the power quality event and old statistical method of auto-regressive integrated moving average (ARIMA), is introduced [27,28].…”

Section: Introductionmentioning

confidence: 99%

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

et al. 2021

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The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, each ML algorithm is executed 10 times due to prevent any overfitting result and the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F measure are calculated.

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Section: Introductionmentioning

confidence: 99%

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

et al. 2021

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“…The impedance of the power supply system can help in determining the harmonic source, i.e. settle disputes between the supplier of electrical energy and the consumer [24][25][26].…”

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confidence: 99%

Determination of the grid impedance in power consumption modes with harmonics

Skamyin

Dobush

Jopri

2023

PMI

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The paper investigates the harmonic impedance determination of the power supply system of a mining enterprise. This parameter is important when calculating modes with voltage distortions, since the determined parameters of harmonic currents and voltages significantly depend on its value, which allow the most accurate modeling of processes in the presence of distortions in voltage and current. The power supply system of subsurface mining is considered, which is characterized by a significant branching of the electrical network and the presence of powerful nonlinear loads leading to a decrease in the power quality at a production site. The modernization of the mining process, the integration of automated electrical drive systems, renewable energy sources, energy-saving technologies lead to an increase in the energy efficiency of production, but also to a decrease in the power quality, in particular, to an increase in the level of voltage harmonics. The problem of determining the grid harmonic impedance is solved in order to improve the quality of design and operation of power supply systems for mining enterprises, taking into account the peculiarities of their workload in the extraction of solid minerals by underground method. The paper considers the possibility of determining the grid impedance based on the measurement of non-characteristic harmonics generated by a special nonlinear load. A thyristor power controller based on phase regulation of the output voltage is considered as such a load. Simulation computer modeling and experimental studies on a laboratory test bench are used to confirm the proposed method. The recommendations for selecting load parameters and measuring device connection nodes have been developed.

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A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

Cited by 2 publications

References 16 publications

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

Determination of the grid impedance in power consumption modes with harmonics

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