Výsledky vyhledávání - "Wang, Yishen"

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Evaluating Load Models and Their Impacts on Power Transfer Limits

Autor: Wang, Xinan, Wang, Yishen, Shi, Di, Wang, Jianhui, Wang, Siqi, Diao, Ruisheng, Wang, Zhiwei

Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic c

Externí odkaz: http://arxiv.org/abs/2008.03336

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Global Sensitivity Analysis in Load Modeling via Low-rank Tensor

Autor: Lin, You, Wang, Yishen, Wang, Jianhui, Wang, Siqi, Shi, Di

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this paper, a tensor-based method is proposed for identifying composite loa

Externí odkaz: http://arxiv.org/abs/2001.02771

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Akademický článek

State-of-the-art review on the Ultrasonic Vibration Assisted Laser Cladding (UVALC)

Autor: Mohsan, Aziz Ul Hassan, Zhang, Mina, Wang, Dafeng, Zhao, Su, Wang, Yishen, Chen, Changyong, Zhang, Jiahao

Publikováno v: In Journal of Manufacturing Processes 1 December 2023 107:422-446

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Akademický článek

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Akademický článek

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Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach

Autor: Wang, Xinan, Wang, Yishen, Shi, Di, Wang, Jianhui, Wang, Zhiwei

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load

Externí odkaz: http://arxiv.org/abs/1911.04894

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Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

Autor: Peng, Yayu, Wang, Yishen, Lu, Xiao, Li, Haifeng, Shi, Di, Wang, Zhiwei, Li, Jie

Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, low-aggregati

Externí odkaz: http://arxiv.org/abs/1903.10679

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Probabilistic Load Forecasting via Point Forecast Feature Integration

Autor: Chang, Qicheng, Wang, Yishen, Lu, Xiao, Shi, Di, Li, Haifeng, Duan, Jiajun, Wang, Zhiwei

Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improv

Externí odkaz: http://arxiv.org/abs/1903.10684

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