Intelligent dual input power system stabilizer

Avdhesh Kr. Sharma

doi:10.1016/S0378-7796(02)00198-0

2003-03-01

PRE

Avdhesh Kr. Sharma

Kothari, ML

DOI：10.1016/S0378-7796(02)00198-0

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This paper presents a new approach for real-time tuning of a dual input power system stabilizer using neuro-fuzzy system (NFS). Intelligent dual input power system stabilizer (IDIPSS) comprises of NFS and conventional dual input power system stabilizer. The NFS is a fuzzy inference system implemented in the framework of multi-layered feed forward adaptive network. NFS network is trained using hybrid training algorithm for real-time tuning of the dual input power system stabilizer. The generator real power (P-e), reactive power (Q(e)), and terminal voltage (V-t)characterizing the operating condition are input signals to the network while optimum DIPSS parameters K-S1 and T-1 are the outputs. Investigations have been carried out considering three, five and seven membership functions (MFs) of Triangular, Trapezoidal, and Gaussian shapes. Studies reveal that for real-time tuning of the dual input PSS, the NFS network with three MFs of any shape is adequate. The proposed IDIPSS exhibits quite a robust performance to wide variations in loading condition, system parameters and large perturbations. (C) 2002 Elsevier Science B.V. All rights reserved.

Keyword:

dual input power system stabilizer

neuro-fuzzy controllers

adaptive network fuzzy inference system

intelligent controllers

adaptive power system stabilizer