no code implementations • 8 Nov 2021 • Ying Zhang, Yanbo Chen, Jianhui Wang, Yue Meng, Tianqiao Zhao
Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models.
no code implementations • 25 Feb 2021 • Ying Zhang, Meng Yue, Jianhui Wang
Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in practical power systems under contingencies.
no code implementations • 7 Aug 2020 • Xinan Wang, Yishen Wang, Di Shi, Jianhui Wang, Siqi Wang, Ruisheng Diao, Zhiwei Wang
Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
no code implementations • 8 Nov 2019 • Xinan Wang, Yishen Wang, Di Shi, Jianhui Wang, Zhiwei Wang
However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters.
no code implementations • 17 Jan 2019 • Mahdi Khodayar, Jianhui Wang, Zhaoyu Wang
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns.
no code implementations • 10 Sep 2018 • Mahdi Khodayar, Jianhui Wang, Zhaoyu Wang
The electricity signal of each device is then modeled by a linear combination of such patterns with sparse coefficients that determine the contribution of each device in the total electricity.
no code implementations • 10 Sep 2018 • Mahdi Khodayar, Saeed Mohammadi, Mohammad Khodayar, Jianhui Wang, Guangyi Liu
In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph.
no code implementations • 13 Feb 2017 • You Lin, Ming Yang, Can Wan, Jianhui Wang, Yonghua Song
Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models.