This research work proposes a multi-input multi-output (MIMO) online adaptive feedback linearization NeuroFuzzy control (AFLNFC) scheme to improve the damping of low frequency oscillations (LFOs) in an AC/DC power system. Optimized NeuroFuzzy identification architecture online captures the oscillatory dynamics of the power system through wide area measurement system (WAMS)-based measured speed signals of machines. Based on the identified power system model, the appropriate control law is derived through feedback linearization control with a self-tuned coefficient vector. The generated control signal modulates the real power flow through a high voltage direct current (HVDC) link during perturbed operating conditions and enhances system stability. The effectiveness of the proposed control strategy is demonstrated through different contingency conditions of a multi-machine test power system with multiple HVDC links. The results validate the significance of the proposed control strategy to improve the capability of HVDC links to damp inter-area modes of LFOs. The proposed MIMO AFLNFC performance is bench-marked against conventional PID based supplementary control.
Inter-area oscillation, high voltage direct transmission, adaptive NeuroFuzzy system, feedback linearization, nonlinear control
AHMAD, SAGHIR and KHAN, LAIQ
"A self-tuning NeuroFuzzy feedback linearization-based damping control strategy for multiple HVDC links,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 25:
2, Article 21.
Available at: https://journals.tubitak.gov.tr/elektrik/vol25/iss2/21