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Yijun Chen

Researcher at Kunming University of Science and Technology

Publications -  12
Citations -  291

Yijun Chen is an academic researcher from Kunming University of Science and Technology. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 5, co-authored 9 publications receiving 47 citations.

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Photovoltaic cell parameter estimation based on improved equilibrium optimizer algorithm

TL;DR: The proposed improved equilibrium optimizer can obtain a highly competitive performance compared with other state-of-the-state algorithms, which can efficiently improve both optimization precision and reliability for estimating photovoltaic cell parameters.
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Optimal sizing and placement of energy storage system in power grids: A state-of-the-art one-stop handbook

TL;DR: A critical one-stop handbook related to one hundred and four methods in six categories covering all kinds of networks and tailored applications for ESS selection, evaluation criteria, modelling and solution methods is provided.
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Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms

TL;DR: Simulation results verified that BRNN based MhAs (BRNN-MhAs) can effectively extract the parameters of PEMFC with higher accuracy, faster speed, and enhanced stability.
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Recent advances and summarization of fault diagnosis techniques for proton exchange membrane fuel cell systems: A critical overview

TL;DR: An in-depth and comprehensive overview of various typical faults and state-of-the-art diagnosis methods of PEMFC systems is carried out to offer insightful guidance to prompt related researchers/engineers to broaden the width of their researches.
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Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

TL;DR: Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification and simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification.