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A neuro-fuzzy approach for the detection of partial discharge | IEEE Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This article describes a neuro-fuzzy approach for the detection of partial discharge, which uses fuzzy logic and neural networks in conjunction with the wavelet transform to identify parameters in the PD pulse form.
2. The pulse form parameters taken into account for pulse type identification purposes include rise time, amplitude, width, decay time, and apparent charge connected with the discharge pulse.
3. Wavelet transformation is used to extract information related to aging characteristics of dielectric surfaces exposed to partial discharges.
Article analysis:
The article is written by experts in the field and published in a reputable journal (IEEE Xplore). The authors provide evidence for their claims by citing relevant literature and providing examples from experiments conducted on dielectric surfaces exposed to partial discharges. The article is well-structured and provides clear explanations of the concepts discussed.
However, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to detecting partial discharges other than their proposed neuro-fuzzy approach. Additionally, they do not discuss any potential risks associated with using this approach or any possible limitations that may arise from its implementation. Furthermore, they do not present both sides of the argument equally; instead they focus solely on promoting their own approach without considering other perspectives or solutions.
In conclusion, while this article is generally reliable and trustworthy due to its expert authorship and publication in a reputable journal, it does contain some potential biases that should be noted when evaluating its content.