Design of Parallel Hybrid Active Power Filter with Adaptive DC-Link Voltage Control Based on Artificial Neural Network
In this study, an Artificial Neural Network (ANN)-based adaptive DC-link voltage (Vdc) controller is developed for a Parallel Hybrid Active Power Filter (PHAPF). The proposed controller aims to simultaneously determine the DC-link reference voltage (Vdc_ref) and the PI controller gains (Ki, Kp) as a function of the operating conditions. Training data for the ANN are obtained from simulations performed in the MATLAB/Simulink environment. Simulations performed using this training set show that adapting the DC-link reference voltage reduces total harmonic distortion (THD) compared to a PHAPF with a fixed Vdc_ref and reduces the DC-link voltage at low-power loads, which has the potential to lower switching losses, while adaptive PI gains improve transient behavior after large load changes. Therefore, the two adaptive quantities affect complementary aspects of performance. The trained ANN model is coded in the C programming language and implemented on a microcontroller-based control card. A 5 kVA PHAPF system is designed and fabricated for experimental verification. Experimental results demonstrate that the proposed ANN-based adaptive DC-link voltage control algorithm achieves lower total harmonic distortion (THD) than PHAPFs employing a constant Vdc_ref. In addition, reducing the DC-link voltage under low-power operating conditions has the potential to decrease voltage stress across the power switches and reduce switching losses. Furthermore, the proposed ANN-based adaptive DC-link voltage control algorithm exhibits better harmonic suppression performance despite the processing load and filtering delays.