An Intelligent Fault Diagnosis Scheme For PV Array Using Machine Learning Techniques

Solankee, Laxman; Rai, Avinash; Kirar, Mukesh Kumar

doi:10.1109/icepes52894.2021.9699839

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Next, a decision tree is constructed using characteristics extracted from the current signal and features generated from the sequence components to identify and categorise faults. Both Decision Trees are extensively evaluated using 3860 data points [3].…”

Section: Literatue Reviewmentioning

confidence: 99%

Machine Learning Based Efficient Protection Scheme for AC Microgrid

Dixit¹,

Kumar²

2022

IRJEAS

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Micro grids have become popular as a way to reduce carbon emissions and use nonrenewable energy sources to produce power. Microgrids allow users to generate and regulate energy as needed, reducing their reliance on the utility grid. They may also sell excess electricity to the grid and make money. Due to its simple design, fast installation, and easy maintenance, photovoltaic systems are a vital microgrid resource. Microgrids threaten the reliability and optimum functioning of major power grids. It's crucial to discover defects early and fix them before catastrophic system breakdown. This research proposes a unique method based on Discrete wavelet transform and ensemble of Decision tree classifier for detecting and classifying microgrid faults. Once the particular fault type is recognised and categorised, a suitable protective strategy may be used to address it early, enhancing the system's overall safety.

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Section: Literatue Reviewmentioning

confidence: 99%