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• W3109926422 abstract "In this study, on the basis of a new agent-based situational awareness platform, the amount of severity in closeness to voltage instability occurrence is determined. In the proposed platform (with the aid of the agents of the disturbance detection unit that are responsible for perception of environmental events and understanding the current situation and the agents of the early enough instability prediction unit that predicts instability occurrence), the novelty is in creating the agents of clustering units that are responsible for prediction of the future status, i.e. severity in closeness to voltage instability occurrence through determining the severity class. This clustering unit maps the reactive power-based features obtained from unconstrained power flow analyses, to the instability time; without any need of post-disturbance data, i.e. the method belongs to just-after-disturbance algorithms. Then, it estimates the severity of the closeness to instability for any test scenario according to its membership degree related to the pre-defined severity clusters. The simulation results show acceptable overall accuracy (up to 99%) in performing early warning task for operators of power systems." @default.
• W3109926422 created "2020-12-07" @default.
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• W3109926422 date "2020-10-14" @default.
• W3109926422 modified "2023-09-27" @default.
• W3109926422 title "Agent‐based situational awareness system for severity in closeness of voltage instability occurrence" @default.
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• W3109926422 doi "https://doi.org/10.1049/iet-gtd.2020.0407" @default.
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