Novel LMS-Neural Network based DSTATCOM for Improving Power Quality

Abstract

The Distribution Static Compensator (DSTATCOM) consistency in a 3-phase linear & non-linear load fed by a 3-phase supply is investigated in this paper using a Novel LMS-based neural network control approach. The DSTATCOM is generally implemented in distribution networks to minimize power quality problems. The Novel LMS-based neural networks are preferable for reducing computation complexity. Power Quality(PQ) issues arise when non-linear load connections are made. The load current will produce harmonics, and the voltage at the point of common coupling (PCC) will also be distorted. The controlling mechanism mitigates these PQ issues. The switching pulses for DSTATCOM's voltage source converter will be generated by using a Novel LMS-based control technique to generate reference source current. For generating reference current, the control algorithm has several steps. Estimation of weighting values, voltage control around the DC capacitor, and generation of reference current are the stages involved. The source current is compared to the reference current, and these values are then used to generate pulses using a hysteresis current controller. The main objective of this paper is to preserve the THD value of source current in accordance with IEEE-519 requirements for harmonic limit.