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Received: 11 February 2019 Accepted: 3 June 2019 A novel Extreme learning Machine (ELM) algorithm based tuning of the parameters of the SVC FACTS controller was implemented in order to control voltage at various buses over a wide range. The ELM algorithm is a non-iterative method which forecasts the parameters of SVC FACTS controller quickly and effectively while Back Propagation Neural Network (BPNN) algorithm is an iterative method which takes a long time for training as well prediction of parameters. The load perturbation is one of the nonlinear disturbances which is considered to investigate the operational capability of the control methodology. Standard IEEE 5 and 30 bus systems are considered as test systems and operation of two models of SVC observed with the BPNN and ELM controllers. The weakest bus is identified using LIndex method which is the optimal location of the SVC FACTS. Results show that the novel ELM method expeditiously and efficiently tunes the parameters of the SVC FACTS controller online such that the voltage regulated to desired value when there is a perturbation in load.

Machine Learning Algorithm Based Static VAR Compensator to Enhance Voltage Stability of Multi-machine Power System