Estimation Method of Line Loss Rate in Low Voltage Area Based on Mean Shift Clustering and BP Neural Network

Tan, Huachun; Li, Yuan; Yu, Liang; Liu, Jing; Ni, Lei; Diao, Xinping

doi:10.1088/1742-6596/1754/1/012225

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…These candidates are then shifted towards regions of the highest density, identified using a kernel density estimate. In power system applications, Mean Shift could be beneficial for detecting areas of high energy consumption or demand hotspots [321], [322], [323], providing valuable insights for power distribution and load management strategies [324], [325], [326].…”

Section: ) Mean-shift Clusteringmentioning

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: ) Mean-shift Clusteringmentioning

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

show abstract

“…BP neural network is an inverse propagation feed forward neural network, which consists of three layers of data structure: input layer, hidden layer and output layer [11][12]. As mentioned above, the input layer input data consists of three parts: H value distribution vector, minimum rectangular aspect ratio and microwave ranging results.…”

Section: Feature Recognitionmentioning

confidence: 99%