Application of machine learning methods in fault detection and classification of power transmission lines: a survey
The rising development of power systems and smart grids calls for advanced fault diagnosis
techniques to prevent undesired interruptions and expenses. One of the most important part …
techniques to prevent undesired interruptions and expenses. One of the most important part …
K-means and alternative clustering methods in modern power systems
As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …
and electric vehicles, the complexity of managing these systems increases. With the …
[PDF][PDF] Hybrid of K-Means and partitioning around medoids for predicting COVID-19 cases: Iraq case study
ABSTRACT COVID-19 was discovered near the end of 2019 in Wuhan, China. In a short
period, the virus had spread throughout the entire world. One of the primary concerns of …
period, the virus had spread throughout the entire world. One of the primary concerns of …
Fault detection on power transmission line based on wavelet transform and scalogram image analysis
Given the massive increase in demand for electrical energy, particularly owing to global
climate change and population expansion, as well as the development of complicated …
climate change and population expansion, as well as the development of complicated …
Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms
The reliable operation of power transmission networks depends on the timely detection and
localization of faults. Fault classification and localization in electricity transmission networks …
localization of faults. Fault classification and localization in electricity transmission networks …
On the use of machine learning for damage assessment in composite structures: a review
RF Ribeiro Junior, GF Gomes - Applied Composite Materials, 2024 - Springer
Composite materials are those formed by combining two or more different materials to take
advantage of the best characteristics of each one. However, due to this heterogeneity …
advantage of the best characteristics of each one. However, due to this heterogeneity …
An optimal allocation method for power distribution network partitions based on improved spectral clustering algorithm
L Pan, Z Han, Z Shanshan, W Feng - Engineering Applications of Artificial …, 2023 - Elsevier
Distribution network nodes are numerous and monitoring devices are widely distributed. All
monitoring data are uploaded to the cloud master for centralized processing may cause …
monitoring data are uploaded to the cloud master for centralized processing may cause …
Balanced Fair K-Means Clustering
R Pan, C Zhong, J Qian - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Fairness in clustering has recently received significant attention. The goal of fair clustering is
to ensure that a clustering algorithm mitigates or even eliminates bias in the original dataset …
to ensure that a clustering algorithm mitigates or even eliminates bias in the original dataset …
Fault Detection in Distribution Network with the Cauchy-M Estimate—RVFLN Method
C Haydaroğlu, B Gümüş - Energies, 2022 - mdpi.com
Fault detection is an important issue in today's distribution networks, the structure of which is
becoming more complex. In this article, a data-based Cauchy distribution weighting M …
becoming more complex. In this article, a data-based Cauchy distribution weighting M …
Parameter-free surrounding neighborhood based regression methods
T İnkaya - Expert Systems with Applications, 2022 - Elsevier
In machine learning, nearest neighbor (NN) regression is one of the most prominent
methods for numeric prediction. It estimates the output variable of a new data point by …
methods for numeric prediction. It estimates the output variable of a new data point by …