PMU Measurement-Based Prediction of Sensitive Nodes Using ANN
Keywords:
Artificial Neural Network (ANN), Binary Integer Linear Programming (BILP), Phasor measurement unit, Sensitive node, State estimationAbstract
To successfully manage critical circumstances, complex power systems require improved observability and visualisation capabilities. By using synchronised phasor measurements, power system dynamics can be quickly controlled with intra-second accuracy. To measure the angle and magnitude of voltage phasors at a bus, a phasor measuring unit (PMU) is used. In this study, a BILP is employed to model the system using topological observation theory. This reduces the number of PMUs needed to monitor the entire system. It was discovered that the system's unknown phasors and the phasors where PMUs are placed are related. The outcomes can serve as the state estimator's initial evaluation. Using PMU data collected under varying load conditions, an ANN is employed to predict the critical nodes in the IEEE 14-Bus system. The outcome of this work is helpful for the operator to take corrective measures to bring bus voltages within the operating limits to ensure the system's reliability and security.
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