English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Motivated by the synergistic integration of soft computing paradigms this paper introduces a fully adaptive multiple-input-multiple-output NeuroFuzzy control for multi-type Flexible AC Transmission Systems (FACTS) to damp low frequency oscillations. The novel control strategy integrates the complementary features of locally controllable fuzzy Bspline membership functions and robust wavelet neural networks in NeuroFuzzy structure. The gradient decent based back-propagation mechanism used for parameters update has been optimized using online Adaptive Learning Rates (ALRs). The stability of the proposed algorithm has been ensured by deriving an upper bound on ALRs using Lyapunov stability criteria. The application of this controller to provide damping signals to various FACTS controllers like Static Synchronous Series Compensator (SSSC) and Static Synchronous Compensator (STATCOM) can effectively enhance the dynamic stability of the system. A benchmark multi-machine power system has been used for performance validation of the controller by applying various faults under different loading scenarios. Conventional Lead-Lag and NeuroFuzzy controls have been considered for comparative evaluation using nonlinear time and frequency domain techniques to reveal that the proposed control performs better in different operating regions. Furthermore, the graphical results obtained from time and frequency domain simulations have been quantified numerically using different performance indices and Energy Spectral Density (ESD), respectively. The temporal, spectral and numerical analysis confirms the superior performance of the proposed control scheme.

MIMO Adaptive Bspline-Based Wavelet NeuroFuzzy Control for Multi-Type FACTS