Power Estimation in Three-Phase PWM Rectifiers Using ANN
Main Article Content
Abstract
At the industrial consumer level, an unbalanced threephase supply is very common, which creates control issues with PWM rectifiers. Direct power control (DPC) has been a proven controller in the field of rectifier control. The DPC requires the estimation of active and reactive as these quantities are used as the control variable in the loop. For an unbalanced grid, the
power estimation becomes complex with the extended pq theory. This article presents a novel approach to estimating the instantaneous power using an artificial neural network (ANN), streamlining the complex control structures involving back-to-back connected secondorder generalized integrator (SOGI) blocks. The intricate interconnection of multiple SOGI blocks poses challenges in terms of computational complexity. The proposed method leverages ANN to replace the SOGI blocks, which makes the system simpler and more efficient. Rigorous
simulation using MATLAB Simulink and comparative studies reveal that with ANN, the performance of the based system can be replicated, and a reduced computational burden can be achieved, leading to improved realtime response. Real-time simulation in RT-LAB validates the proposed ANN-based method for PWM rectifiers’ power estimation. Integrating ANN models ensures accurate emulation of complex control structures, affirming the approach’s efficacy for industrial deployment.
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