Voltage collapse prediction using artificial neural network

Abstract

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.

Voltage Collapse Prediction Using Artificial Neural Network

“…NLSI_1 is a voltage stability indicator that falls within the range of zero to one. If the value is close to zero then the bus will be considered stable; if the value is close to one, then the bus will be considered as a critical bus upon which the voltage can collapse [5]. We calculated the stability index for the base case when the system was running under normal operating conditions as well as for the contingency case, where the system was applied to a reactive power loading, which meant that we changed the reactive power of the buses to check whether the voltage collapse had occurred on the system or not.…”

Voltage instability curtailment and quality improvement with phasor measurement unit in power system

“…Voltage breakdown occurs when a power system's voltage is unstable. As a result, Adebayo and Sun (2020) predict a voltage failure bus using a method known as the critical bus voltage stability index, which is dependent on the voltage variation of the system's load bus (Isaac et al, 2021). The Isaac et al (2021) uses a machine learning methodology and a voltage stability index called the new line stability index to create an online voltage collapse prediction model.…”

“…As a result, Adebayo and Sun (2020) predict a voltage failure bus using a method known as the critical bus voltage stability index, which is dependent on the voltage variation of the system’s load bus (Isaac et al , 2021). The Isaac et al (2021) uses a machine learning methodology and a voltage stability index called the new line stability index to create an online voltage collapse prediction model. García Sanchez et al (2020) examine the effect of load dependency on voltage on the phenomena of voltage stability, especially the characteristics of the breakdown point or instability point (Wu et al , 2020).…”

Power system contingency classification using machine learning technique

“…For static security assessment of power systems, decision tree, random forest, and ensemble classification approaches were reported [13]- [15]. For contingency analysis, a fuzzy logic technique and artificial neural networks are used [16]- [21].…”