Anomaly Detection in Smart Grid with Help of an Improved OPTICS Using Coefficient of Variation

Fathnia, Farid; Javidi, Mohammad Hossein

doi:10.1109/icee.2018.8472534

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It could potentially identify high-consumption neighborhoods or commercial districts versus low-consumption areas, aiding in demand forecasting and grid management [372], [373], [374]. It can also be helpful in identifying clusters of power grid failures or anomalies [375] that might indicate issues requiring maintenance or infrastructure upgrades.…”

Section: ) Opticsmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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.

show abstract