A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique
Author(s) -
Godpromesse Kenné,
René Fochie Kuate,
Andrew Muluh Fombu,
Jean de Dieu Nguimfack–Ndongmo,
Hilaire Bertrand Fotsin
Publication year - 2017
Publication title -
advances in electrical engineering
Language(s) - English
Resource type - Journals
eISSN - 2356-6655
pISSN - 2314-7636
DOI - 10.1155/2017/7873491
Subject(s) - unified power flow controller , control theory (sociology) , robustness (evolution) , electric power system , flexible ac transmission system , computer science , artificial neural network , nonlinear system , control engineering , power flow , engineering , power (physics) , control (management) , artificial intelligence , physics , biochemistry , chemistry , quantum mechanics , gene
This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller
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