Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

Roy, Sukanta; Tufail, Shahid; Tariq, Mohd; Sarwat, Arif

doi:10.1109/tr.2024.3359540

Cited by 1 publication

(1 citation statement)

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“…A decision tree stands out as a supervised machine learning technique specifically designed to tackle both classification and regression problems through a systematic process of data division based on distinct parameters [20]. The leaves of the tree represent the decision outcomes, whereas the nodes facilitate the segmentation of the data.…”

Section: Decision Treementioning

confidence: 99%

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging

Behnamfar,

Stevenson,

Tariq

et al. 2024

Sensors

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Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.

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Section: Decision Treementioning

confidence: 99%