K-means clustering and correlation coefficient based methods for detection of flicker sources in non-radial power system

Dejamkhooy, Abdolmajid; Dastfan, Ali; Ahmadifard, Alireza

doi:10.3103/s1068371214040142

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…In [276], [277], [278] and [279], the K-means clustering analysis algorithm is applied to categorize and detect voltage sag, utilizing the historical data gathered from a largescale grid. Likewise, [280] used this clustering algorithm to identify and locate the flicker sources in a non-radial power system. Power quality disturbances (PQDs) are determined and clustered using the k-means method [21], [281] with SVD [282].…”

Section: Power Qualitymentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: Power Qualitymentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…The K-nearest-neighbour method is also used to detect power quality disturbances [30]. In addition, the application of the K-means clustering method to determine the source of Flicker [31], and the voltage sag pattern recognition [32] can be mentioned.…”

Section: Step 1: Pre-processing Of Measured Datamentioning

confidence: 99%

“…The K-means clustering method is one of the best and most widely used data classification methods in power quality topics [21,31,32]. In the proposed algorithm, the K-means clustering method is used to categorise changes in measurement data (harmonic current magnitude of each load) into three categories, including low, moderate, and high.…”

Section: Step 1: Pre-processing Of Measured Datamentioning

confidence: 99%

Harmonic indices for continuous evaluation of consumer's harmonic contribution

Tadayon

Hooshmand

Kiyoumarsi

et al. 2020

IET Generation, Transmission & Distribution

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“…In reference [18], the value of correlation coefficient is used to reflect the similarity of two variables. In this paper, the correlation coefficient of the voltage curves will be calculated.…”

Section: B Similarity Calculation Of Voltage Curves 1) Correlation Cmentioning

confidence: 99%

A Dimension Reduction Method Used in Detecting Errors of Distribution Transformer Connectivity

Tang¹,

Shen²,

Zhou³

et al. 2020

IEEE Access

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Many power utilities may have problems with the quality of data in records about which feeder a distribution transformer is connected to. This affects the operation and maintenance of smart grid infrastructure, outage management, line loss management, and workforce safety. The traditional manual way of verifying and updating the distribution transformer connectivity (DTC) is time-consuming and laborintensive. Researchers have proposed the method which makes use of secondary side single-phase voltage of distribution transformers to verify DTC. However, when the windings of distribution transformers are yyn0 connected, the performance of the single-phase voltage method (SPVM) is unsatisfactory due to the unbalanced three-phase voltages. This paper proposed a dimension reduction method (DRM) that can convert the unbalanced three-phase voltage to a balanced voltage. The data which include 29 days' secondary side voltages of 3866 distribution transformers have been collected. The performances of the DRM and the SPVM have been compared. Results show that the DRM always has higher accuracy compared with SPVM. The influences of the maximum voltage difference and the reference distribution transformer have also been discussed. The DRM's results are more stable compared with SPVM.

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K-means clustering and correlation coefficient based methods for detection of flicker sources in non-radial power system

Cited by 7 publications

References 22 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Harmonic indices for continuous evaluation of consumer's harmonic contribution

A Dimension Reduction Method Used in Detecting Errors of Distribution Transformer Connectivity

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