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Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference

Published in Energy, 2023

This paper uses a modified Bayesian Neural Network for the probalistic forecasting of bidding behaviors of generation companies and analyse the uncertainty within. It also provide a sensitivity analysis of the bidding behavior influencing factors.

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Interpretable Detection and Localization of False Data Injection Attacks Based on Causal Learning

Published in 2023 IEEE Power & Energy Society General Meeting (PESGM), 2023

This paper propose a causal learning structure to quantify the casuality between measurements and locate the measurements under cyber attacks based on the causality anomalies.

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Training A Dynamic Neural Network to Detect False Data Injection Attacks Under Multiple Unforeseen Operating Conditions

Published in IEEE Transactions on Smart Grid, 2024

This paper is aimed at addressing the concept drift issue in power system measurements data while detecting FDIAs. It proposes an online self-adptive mechanism to accmodate the traditional attack detection model to unforeseen system operating points.

Recommended citation: Dongping Hu, Shengyang Wu, Jingyu Wang and Dongyuan Shi, "Training a Dynamic Neural Network to Detect False Data Injection Attacks Under Multiple Unforeseen Operating Conditions," IEEE Transactions on Smart Grid, vol. 15, no. 3, pp. 3248-3261.
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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