In this paper, a cuckoo search (CS) algorithm-based neuro-fuzzy controller (NFC) is developed to improve the performance of unified power quality conditioner (UPQC). The CS algorithm is used for optimising the output of neural network (NN) so that the classification output of the NN
is enhanced. The inputs of the networks are error and change of error voltage of the PQ issue signal of nonlinear load which are calculated by comparison with the reference signal. Next, the output of network, i.e. regulated (compensated) voltage, is optimised by the CS algorithm. From the
output of CS, an optimum rule-based fuzzy interference system is developed and the PQ problem is compensated. The CS-NFC-based UPQC is implemented in MATLAB/Simulink and the PQ issue clearing performance is analysed. The PQ issue clearing performance of proposed UPQC is compared with traditional
UPQC, NFC-UPQC, GA-NFC-UPQC and adaptive GA-NFC-UPQC. The CS-NFC-based UPQC controller has lesser error deviation of 2.8% with traditional UPQC, 2.12% with NFC, 1.7% with GA-NFC and 0.6% with adaptive GA-NFC.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Department of Electrical and Electronics Engineering, Noorul Islam University, Nagercoil, India
Department of Electrical Engineering, Noorul Islam University, Nagercoil, India
Publication date: November 2, 2015
More about this publication?