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

Jianhong Pan,Aidi Dong,Yang Li,Jiashu Fan,Ying-Yi Hong

doi:10.1155/2020/8895022

Abstract

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.

Highlights

Transient stability assessment (TSA) has long been considered to be of paramount importance for ensuring safe operation of power systems [1]
The successful application of time-stamped phasor measurement units- (PMUs-) based wide-area measurement system (WAMS) makes synchronized measurements available for use, which opens up new opportunities for developing a modern wide-area protection and control (WAPaC) system [17,18,19]
Two conclusions can be drawn: (1) the Jaya algorithm is effective to strengthen the classification ability of the presented model, and the Jaya algorithm is obviously superior to other alternatives such as genetic algorithm (GA) and practice swarm optimization (PSO)

Summary

Introduction

Transient stability assessment (TSA) has long been considered to be of paramount importance for ensuring safe operation of power systems [1]. Recent research demonstrates that pattern recognition techniques, such as decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), are promising for assessing transient stability status of power systems, called pattern recognition-based TSA (PRTSA) [27]. (1) DT-based TSA: In [28], a dynamic security assessment approach is proposed by using PMUs and DT; and an adaptive ensemble DT is developed for doing so in [29]; in [30], a generic DT-based probabilistic framework is put forward for predicting transient stability of power systems via PMU data.

Construction of the Original Feature Set

Principle of the Proposed Approach

Jaya-RVFL-Based TSA

Case Study

Case 1

Conclusions