Voltage dip mitigation in wind farms by UPQC based on Cuckoo Search Neuro Fuzzy Controller

This paper presents, Cuckoo Search Algorithm (CSA) based Neuro Fuzzy Controller (NFC) to improve the performance of Unified Power Quality Conditioner (UPQC) for voltage sag mitigation in wind farms. CSA is used for optimizing the output of neural network so the classification output of the neural network is enhanced. CSA is an optimization algorithm which inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds. The inputs of the networks are error and change of error voltage signals of wind farm which calculated by compare with the reference signal. Next, the output of network i.e. compensated voltage is optimized by CSA. From the output of CSA, an optimum rule base fuzzy inference system is developed voltage dip mitigation in wind farm based Squirrel Cage Induction Generator (SCIG). The proposed CSA-NFC based UPQC is implemented in MATLAB. performance is analyzed. The performance of proposed UPQC is compared with traditional UPQC, NFC-UPQC, GA-NFC-UPQC, and adaptive GA-NFC-UPQC.