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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. [ABSTRACT FROM AUTHOR]

In this study, a simple, fast, and adaptive network reduction procedure is presented for long‐term voltage stability analyses, to speed up the simulation time. The proposed procedure consists of two parts including disturbance detection based on data from synchronised measurement devices; and the rest, up to the final estimated values, based on simulations and calculations. In turn, simulations and calculations result in an adaptive dynamic‐oriented determination of external sub‐network (according to the disturbance occurred) and substituting an appropriate equivalent for that subsystem. The external subsystem is determined without any need to measure post‐disturbance data, while the detection criteria are calculated by simulations at 0.8 s after disturbance occurrence. The results have shown considerable reduction in the simulation time while providing acceptable accuracy in delivering the time of voltage instability occurrences, especially when applying the proposed error compensation procedure. [ABSTRACT FROM AUTHOR]

In this study, the proposed algorithm is a help for utilities to find out when voltage instability will happen, as soon as they realise that a disturbance may/will result in voltage instability. In this regard, the data gathered, on‐line, by a wide area monitoring system (WAMS) is supposed to be processed in an energy management system, where, finally, voltage instability inception time will be determined. The proposed algorithm employs k‐nearest neighbour algorithm as an instance‐based learning algorithm. This estimation uses only pre‐disturbance information and exact disturbance data, so it is fast enough for real‐time applications. A comprehensive investigation of the input features has resulted in introducing the most appropriate feature set and the most effective search procedure in estimating the instability time. The simulation results show an acceptable accuracy in estimation of voltage instability time. [ABSTRACT FROM AUTHOR]