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The ability to rapidly acquire synchronized phasor measurements from around the system opens up new possibilities for power system protection and control. In this paper we develop a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements to provide fast transient stability swings prediction for use with high-speed control. Primary features of the method include constructing a fuzzy neural network for all fault locations, using a short window of realistic-precision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.

The ability to rapidly acquire synchronized phasor measurements from around the system open up new possibilities for power system operation and control. A novel neuro-fuzzy network, Fuzzy Hyperrectangular Composite Neural Network, is proposed for voltage security monitoring (VSM) using synchronized phasor measurements as input patterns. This paper demonstrates how neuro-fuzzy networks can be constructed off-line and then utilized on-line for monitoring voltage security. The neuro-fuzzy network is tested on 3000 simulated datas from randomly generated operating conditions on the IEEE 30-bus system to indicate its high classification rate for voltage security monitoring.