Harmonic source identification with optimal placement of PMUs

Dixit, Anupam; Kaur, Manpreet

doi:10.1109/icpeices.2016.7853434

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…In addition, the measurement signals are analyzed utilizing S-transform technique [48], and two types of harmonic sources are considered in this research comprise of harmonic current source (H C ) and harmonic voltage source (H V ) type-load [49]. Four specific cases are considered, which are [50], [51]: − Case 1: no harmonic source in the power system (N-N), − Case 2: harmonic source located at the downstream (N-H) of the PCC, − Case 3: harmonic source located at the downstream and upstream of the PCC (H-H), − Case 4: harmonic source located at the upstream of the PCC (H-N). The main goal of this research is to identify and diagnose type of harmonic sources in the power system.…”

Section: Methodsmentioning

confidence: 99%

K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

et al. 2021

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This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant 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, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency 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 prevent any overfitting result.

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Section: Methodsmentioning

confidence: 99%

K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

et al. 2021

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“…The increase in non-linear loads in the industrial, commercial and domestic power system has changed the utilities power quality conditions [1][2][3]. With the fast development in power electronics, the load behavior changes from linear to non-linear and these loads contribute harmonic pollution in electrical power system [4][5][6].…”

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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“…Also, a Pareto multi-objective optimisation is carried out considering uncertainties. An effective method for identification of the harmonic sources location and its injection levels is presented in [17] using the OPMUP. A technique is proposed in [18] based on graph theory for OPMUP in the power system to analyse system observability.…”

Section: Introductionmentioning

confidence: 99%

Optimal PMU placement for power system observability considering network expansion and N − 1 contingencies

Asgari

Firouzjah

2018

IET Generation, Transmission & Distribution

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Harmonic source identification with optimal placement of PMUs

Cited by 5 publications

References 15 publications

K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

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

Optimal PMU placement for power system observability considering network expansion and N − 1 contingencies

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