Voltage stability prediction by using Artificial Immune Least Square Support Vector Machines (AILSVM)

• Main Authors: Abaziz N.F., Rahman T.K.A., Zakaria Z.
• Other Authors: 57221906825
• Format: Conference Paper
• Published: IEEE Computer Society 2023

This paper presents a new hybrid optimisation technique for voltage stability prediction called Artificial Immune Least Square Support Vector Machine (AILSVM). In this paper, a newly developed index named as Voltage Stability Condition Indicator (VSCI) was used to assess the stability condition of load buses in a system. VSCI was derived from a current equation in a complex form of a general 2-bus system. Support Vector Machine (SVM) has been proven to be a powerful tool for solving numerous problems in many fields. However, in order to obtain its best performance, a right combination of SVM parameters is needed. Therefore, Artificial Immune System (AIS) was used as the evolutionary search technique to optimise the value of SVM parameters. The simulations were carried out in a steady state analysis and the data generated were trained and tested under various types of loading conditions either due to an increase in active and/or reactive power. The obtained results show that the proposed methods can successfully give a very good prediction with the predicted values very close to the actual value. All simulations were tested on IEEE 30 bus Reliability Test Systems (RTS). © 2014 IEEE.