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Adaptive NeuroFuzzy Legendre based damping control paradigm for SSSC

Authors: Rabiah Badar; Laiq Khan;

Adaptive NeuroFuzzy Legendre based damping control paradigm for SSSC

Abstract

The controllable series injected voltage can be used to damp low frequency power and rotor angle oscillations. Conventional linear and NeuroFuzzy control schemes perform well only for a specific operating condition, or in the vicinity of the tuned operating point of highly nonlinear power system, due to their fixed parameters architecture. To improve the performance of the damping control, nonlinear behavior of power system must be incorporated via some nonlinear control scheme. This work presents an online adaptive nonlinear control paradigm by incorporating Legendre polynomial NNs in the consequent part of the conventional TSK structure. The proposed control scheme is successfully applied to damp local and inter-area modes of oscillations for different contingencies and operating conditions. The robustness of the proposed control scheme is validated using comparative analysis based on nonlinear time domain simulations and different performance indices.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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