Hybrid adaptive neuro-fuzzy B-spline--based SSSC damping control paradigm using online system identification

B-spline membership functions have produced promising results in the field of signal processing and control due to their local control property. This work explores the potential of B-spline--based adaptive neuro-fuzzy wavelet control to damp low frequency power system oscillations using a static synchronous series compensator (SSSC). A comparison of direct and indirect adaptive control based on hybrid adaptive B-spline wavelet control (ABSWC) is presented by introducing the online identification block. ABSWC with identification (ABSWCI) provides the sensitivity information of the plant needed to control the system. The parameters of the control and identification block are updated online using a gradient descent-based backpropagation algorithm. The stability and convergence of the proposed control system is discussed based on Lyapunov stability criteria. The robustness of the proposed control algorithm has been evaluated for local and interarea modes of oscillations using different faults. The nonlinear time-domain simulations have been analyzed on the basis of different performance indices and time-frequency representation, showing that ABSWC effectively damps low-frequency oscillations and incorporation of online identification optimizes the control system performance in terms of control effort, which reduces the switching losses of the converter.

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