Legendre wavelet embedded NeuroFuzzy algorithms for multiple FACTS

• Online adaptive Legendre wavelet based NeuroFuzzy control has been proposed for FACTS.
• The proposed control is fully adaptive multivariable MIMO control.
• The parameters are updated using gradient descent based on adaptive learning rates.
• The performance of conventional and proposed control strategies is compared.
• Comparison is based on nonlinear time and frequency simulations and performance indices.

Since their inception, damping of Low Frequency Oscillations (LFOs) has been a critical issue in electric power systems. Voltage Source Converter (VSC) based Flexible AC Transmission Systems (FACTS) have a renowned capability for rapid regulation of various network quantities and combat LFOs when equipped with efficient supplementary damping control. This work is a design paradigm shift from conventional Takagi-Sugeno Kang (TSK) based control to advanced control based on Legendre wavelet neural networks. The parameters of the proposed control are updated using adaptive learning rates based on Lyapunov stability criteria. The contributions of this framework are the damping performance improvement with fast convergence. The proposed control scheme has been tested for different contingencies and various operating conditions. The nonlinear time domain simulations and different performance evaluation techniques show that the proposed hybrid control paradigm gives better performance in transient and steady-state regions.