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Shengyuan Liu
Researcher at Zhejiang University
Publications - 76
Citations - 1031
Shengyuan Liu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Electric power system & Computer science. The author has an hindex of 10, co-authored 56 publications receiving 324 citations. Previous affiliations of Shengyuan Liu include University of Tennessee.
Papers
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Journal ArticleDOI
Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort
Muhammad Waseem,Muhammad Waseem,Zhenzhi Lin,Zhenzhi Lin,Shengyuan Liu,Intisar Ali Sajjad,Tarique Aziz +6 more
TL;DR: Simulation results indicate that the proposed GWCSO approach is robust, computationally efficient, and outperforms conventional ones in terms of electricity cost, peak to average ratio, and it also demonstrate that there is a trade-off between users’ comfort considering appliances waiting time and electricity cost.
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Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor
TL;DR: A novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.
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Model-Free Data Authentication for Cyber Security in Power Systems
TL;DR: A measurement data source authentication (MDSA) algorithm based on feature extraction techniques including ensemble empirical mode decomposition (EEMD) and fast Fourier transform (FFT) and machine learning for real-time measurement data classification is proposed.
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Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources
Shengyuan Liu,Zhenzhi Lin,Yuxuan Zhao,Yilu Liu,Yi Ding,Bo Zhang,Li Yang,Qin Wang,Samantha Emma White +8 more
TL;DR: A novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES.
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Practical Method for Mitigating Three-Phase Unbalance Based on Data-Driven User Phase Identification
Shengyuan Liu,Renyun Jin,Haifeng Qiu,Xueyuan Cui,Zhenzhi Lin,Lian Zikuan,Lin Zhi'an,Fushuan Wen,Yi Ding,Qin Wang,Li Yang +10 more
TL;DR: An integrated method for solving related issues including user phase identification based on spectral clustering and three-phase unbalance mitigation is presented, and a Mixed Integer Linear Programming (MILP) model is formulated.