Performance Evaluation of Voltage Stability Indices for Dynamic Voltage Collapse Prediction

2006 ◽  
Vol 6 (5) ◽  
pp. 1104-1113 ◽  
Author(s):  
Muhammad Nizam ◽  
Azah Mohamed . ◽  
Aini Hussain .
Keyword(s):  
Performance Evaluation ◽  
Voltage Stability ◽  
Voltage Collapse ◽  
Collapse Prediction ◽  
Dynamic Voltage ◽  
Stability Indices

scholarly journals POWER SYSTEM CONTINGENCY RANKING BASED ON SHORT-TERM VOLTAGE STABILITY INDICES

2019 ◽  
Vol 49 (4) ◽  
pp. 225-232
Author(s):  
Jaime Dwaigth Pinzon Casallas ◽  
D. G. Colomé
Keyword(s):  
Dynamic Response ◽  
Power System ◽  
New England ◽  
Voltage Stability ◽  
Short Term ◽  
Voltage Instability ◽  
Contingency Ranking ◽  
Dynamic Voltage ◽  
Stability Indices

This paper presents a novel methodology to identify critical contingencies that produce short-term voltage stability problems (STVS). The proposed methodology classifies the state of the pow-er system for each contingency, assessing the voltage stability of the post-contingency dynamic response from the calculation of the maximal Lyapunov expo-nent (MLE) and dynamic voltage indices at each bus and the whole system. In order to determine the crit-ical contingencies, the values of the indices and the results of the classification of the post-contingency state are statistically analysed. The methodology is tested in the New England 39-bus system, obtaining satisfactory results in relation to the identification not only of the most critical contingencies but also of vulnerable buses to voltage instability. New contri-butions of this work are the contingency classifica-tion methodology, the algorithm for calculating dy-namic indices and the method of classification of the operating state as a function of the STVS problem magnitude.

scholarly journals Voltage collapse prediction using artificial neural network

2021 ◽  
Vol 11 (1) ◽  
pp. 124
Author(s):  
Samuel Isaac ◽  
Soyemi Adebola ◽  
Awelewa Ayokunle ◽  
Katende James ◽  
Awosope Claudius
Keyword(s):  
Neural Network ◽  
Voltage Stability ◽  
Stability Index ◽  
Voltage Collapse ◽  
Feed Forward Neural Network ◽  
Voltage Stability Index ◽  
Voltage Instability ◽  
Collapse Prediction ◽  
R Value ◽  
Bus System

Unalleviated voltage instability frequently results in voltage collapse; which is a cause of concern in power system networks across the globe but particularly in developing countries. This study proposed an online voltage collapse prediction model through the application of a machine learning technique and a voltage stability index called the new line stability index (NLSI_1). The approach proposed is based on a multilayer feed-forward neural network whose inputs are the variables of the NLSI_1. The efficacy of the method was validated using the testing on the IEEE 14-bus system and the Nigeria 330-kV, 28-bus National Grid (NNG). The results of the simulations indicate that the proposed approach accurately predicted the voltage stability index with an R-value of 0.9975 with a mean square error (MSE) of 2.182415x10<sup>−5</sup> for the IEEE 14-bus system and an R-value of 0.9989 with an MSE of 1.2527x10<sup>−7</sup> for the NNG 28 bus system. The results presented in this paper agree with those found in the literature.

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