Artificial Neural Network Based Approach for Voltage Stability Analysis of for Sustained Operation of Power System

Authors

  • Yuba Raj Adhikari Department of Electrical Engineering, Pulchowk Campus, IOETU, Nepal
  • Rishi Kumar Barnawal Nepal Electricity Authority

DOI:

https://doi.org/10.3126/jacem.v6i0.38364

Keywords:

Power system stability, Voltage stability index, Artificial Neural Network, Error-back propagation, weak bus

Abstract

For reliable and secure power system, the stability analysis is recognized as an important problem. Voltage stability is the capacity of a power system to maintain steady acceptable voltages at all buses in the system. Voltage stability index (VSI) evaluation for a situation of power system can act as an accurate and fast indicator of the proximity of the system to voltage instability. Recently there has been considerable interest in intelligent methods based on artificial neural network (ANN), fuzzy logic and genetic algorithm to voltage stability assessment problem. ANN, with the ability to provide non-linear input/output mapping, parallel processing, learning and generalization have the potential to make them ideally suited for estimating VSI’s of a power system without solving the governing power system equations. This paper is to purpose an alternative method using ANN for finding the closeness of system operating point to voltage collapse that would be claimed to have better computational speed, accuracy, efficiency and reliability. Voltage Stability Analysis (VSA) using VSI is performed for different alternate loading strategies of power network building ANN models for every different scenario. The outcome found working satisfactorily in analyzing voltage stability problem, basically in ranking the network buses according vulnerability order.

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Published

2021-07-10

How to Cite

Adhikari, Y. R., & Barnawal, R. K. (2021). Artificial Neural Network Based Approach for Voltage Stability Analysis of for Sustained Operation of Power System. Journal of Advanced College of Engineering and Management, 6, 259–267. https://doi.org/10.3126/jacem.v6i0.38364

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Section

Articles