Adaptive recurrent NeuroFuzzy control for power system stability in smart cities

Abstract

Abstract A smart city is a dynamic and sustainable urban system that provides a great quality of service to its residents by optimally managing its resources. In smart cities, low-frequency oscillations are a serious concern to the power system as they adversely affect system’s stability. Moreover, power system stabilizers are inefficient due to their fixed-parameter architecture. Flexible ac transmission system controller performs effective damping of low-frequency oscillations when provided with suitable supplementary damping control like a Static synchronous series compensator. In this context, the paper proposes an adaptive neuro-fuzzy recurrent wavelet control for smart cities that uses the recurrent Gaussian membership function and recurrent wavelet neural network in antecedent and consequent parts respectively. The paper applies the gradient descent optimization with a back-propagation algorithm to update the parameters of suggested adaptive neuro-fuzzy recurrent wavelet control. Simulations are performed on two test systems, both showing the effectiveness of the recommended control scheme as compared to traditional lead-lag control and artificial neuro-fuzzy Takagi Sugeno Kang control. Calculations of performance index for various fault scenarios lead to the conclusion that the control scheme can damp oscillations effectively and enhances the power system’s transient stability.