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A novel transient stability assessment (TSA) approach using random vector functional link (RVFL) network optimized by Jaya algorithm, called Jaya-RVFL, is proposed for power systems in this paper. First, by extracting system-level features from phasor measurement unit (PMU) measurements as predictors, an RVFL-based TSA model is proposed. In order to improve the performance of RVFL classifiers, a quantile scaling approach is utilized to optimize the randomization range of input weights via the Jaya algorithm. The simulation results on IEEE 39-bus system and a real-world power system show that the presented method outperforms other popular methods comprising multilayer perception, probabilistic neural network, and support vector machine.

Random Vector Functional Link Network Optimized by Jaya Algorithm for Transient Stability Assessment of Power Systems