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Performance Investigation of Unified Power Quality Conditioner Using Artificial Intelligent Controller

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A neural network based synchronous reference frame theory (SRF) control strategy is proposed in this paper to compensate power quality issues in three phase three wire (3P3W) distribution system through Unified Power Quality conditioner (UPQC) for various balanced/Unbalanced load and balanced/unbalanced/distorted source conditions. The ANN controller is designed to replace the Low pass filters and Proportional-integral controllers of the conventional control strategy for UPQC. Separate ANN controller is implemented to maintain voltage across the capacitor under all varying conditions of source and load. The Proposed control strategy mitigates harmonic/reactive currents, ensures balanced and sinusoidal source current from the supply mains that are nearly in phase with the supply voltage in spite of unbalance in the three phase load currents and source voltage conditions, in 3P3W distribution systems. The performance of the proposed ANN controller for UPQC is validated and investigated through simulations using MATLAB software. The simulation results prove the efficacy of the proposed neural network based control strategy under varying source and load conditions.
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Neural Network; UPQC; Neutral Current Mitigation; Total Harmonic Distortion (THD); Linear and Non-Linear Loads; Unbalanced and/or Distorted Source

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