Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Vu Thanh Long"'
This paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk
Externí odkaz:
http://arxiv.org/abs/2312.10635
This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked micr
Externí odkaz:
http://arxiv.org/abs/2306.14018
Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to distributed paradigm, LFC needs to consider the peer-to-peer (P2P) based scheme that con
Externí odkaz:
http://arxiv.org/abs/2209.12361
Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked microgrids are vuln
Externí odkaz:
http://arxiv.org/abs/2209.07385
High penetration of wind power with conventional grid following controls for inverter-based wind turbine generators (WTGs) weakens the power grid, challenging the power system stability. Grid-forming (GFM) controls are emerging technologies that can
Externí odkaz:
http://arxiv.org/abs/2203.02105
Publikováno v:
Non-archival document for workshop discussion, 2021
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently. Reinforcement learnin
Externí odkaz:
http://arxiv.org/abs/2112.01484
Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which c
Externí odkaz:
http://arxiv.org/abs/2103.14186
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive selection of control actions such that the voltage recovery criterion can be
Externí odkaz:
http://arxiv.org/abs/2102.00077
In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities. Consequently, the
Externí odkaz:
http://arxiv.org/abs/2012.06685
The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems hav
Externí odkaz:
http://arxiv.org/abs/2011.09664