Dynamic stability recognition of power system using generalized regression neural networks
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ngocau@hcmute.edu.vnKeywords:
Dynamic stability recognition, Feature Selection, Power System, monitoring, Generalized Regression Neural NetworksAbstract
A modern power system has faced in stress condition and its operating point is close to its stability limit, while the power system is subjected to variety large oscillation. These lead to blackout problem, so the unstable state of power system must be early detected in order to activate an emergency control system. This may be save the power system from falling into blackout. The paper presented a process to build dynamic stability recognition model of power system (DSRMPS) using Generalized Regression Neural Networks (GRNN). After trained GRNN, in operation phase, output of GRNN function yields a value that is a stable index. It can be visually expressed on a screen. The paper designed an interactive visualization tool that enables the user to monitor the stable status of power system. The study is tested on IEEE 39-bus power system network.
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