Abstract
The real problems in diminution of power quality (PQ) occur due to the rapid growth of nonlinear load are leading to a sudden decrease of source voltage for a few seconds. All these problems can be compensated by unified power quality controller (UPQC). The proposed research is based on designing a wind energy conversion system (WECS) fed to the dc-link capacitor of UPQC so as to maintain proper voltage across it and operate the UPQC for PQ improvement. The proposed research utilizes two techniques for enhancing the performance of UPQC known as integrated ant lion optimizer (IALO)-adaptive neuro fuzzy inference system (ANFIS), called IALO-ANFIS. Here, induction motor is considered as non-linear load. ALO searching behavior is enhanced by crossover and mutation. Initially, the objective function parameters are defined, that is, voltage, real, grid parameters, load parameters, real and reactive power and current. Based on these parameters, the control pulse is produced for series and shunt active power filter (APF). IALO is used to identify the optimal solutions and creates the training dataset. In light of the accomplished dataset, ANFIS predicts the best control signals of UPQC. During load variation conditions, the proposed strategy minimized the power loss and voltage instability issue individually. Subsequently, the power quality of the system is enhanced. In order to evaluate the effectiveness of the proposed method, three different cases are considered. The performance of the proposed technique is validated through MATLAB/Simulink and compared with existing techniques such as genetic algorithm and ALO.
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