Three novel machine learning-based adaptive controllers for a photovoltaic shunt active power filter performance enhancement

This study develops three new machine learning-based algorithms using the SVR prediction approach. The overall objective is to enhance the performance of the PV shunt active power filter (PV-SAPF) in order to successfully fulfil its multi-functionality in terms of PV power generation along with power quality upgrades. The first technique, named "SVR-INC MPPT", incorporates an ML-based duty cycle prediction function, which is intended to speed up the MPP finding process. The algorithm then switches to the INC approach, guaranteeing an accurate steady-state response. The second and third techniques are the ML-based Adaline for DC voltage regulation, and the ML-based Adaline SRF strategy for reference currents generation. The adopted approaches are based on the prediction of two weights performing the actions of the proportional and integral of DC voltage controller, and nine weights for the fundamental currant extraction. The advantage lies in overcoming the Adaline-based algorithm's limitations, requiring an on-line large number of iterations. Three simulation scenarios along with six controllers based on classical and intelligent techniques, are adopted to prove the effectiveness of the proposed ML-controllers. The results display (i) an accurate and immediate ML-based harmonics identification (minimizing the extraction error by up to 99% compared to the Adaline approach). (ii) a response time reduction range from 65% to 100%, of the ML-based DC voltage output controller compared to the PI-based one. (iii) a THD range from 2.41% to 4.45%. (iv) 20ms and 0% of PV power response time and overshoot, respectively (v) DC voltage overshoot range from 0.04% to 1.1%. Consequently, these three ML-based controllers offer the best options for a powerful PV-SAPF system in terms of performance tracking and harmonic attenuation. Moreover, the obtained metrics comply with IEEE-519 standard.