Machine Learning-Enabled Active Filters for Power Quality Improvement in Grid-Connected Systems
This paper introduces a shunt active power filter (SAPF) with machine learning that is used to provide power quality (PQ) improvement to a PV-Battery hybrid inverter in a grid-connected system. A predictive compensation strategy based on a long short-term memory (LSTM) is used to produce adaptive reference current to reduce harmonic distortion and supply reactive power. The proposed approach takes advantage of time dependence of disturbance profiles, unlike traditional fixed-gain PI-based controllers typical of active filters, which provide proactive compensation to nonstationary conditions generated by renewable variability and nonlinear loads. An in-depth MATLAB/Simulink analysis integrating the hybrid patterns of irradiance with intermittent dips at cloud fronts was used in testing the PQ performance. The suggested controller resulted in a decrease of the current total harmonic distortion (THD) to 28.6% (without compensation) and 7.8% (PI-controlled SAPF), and an increase in power factor to unity. The momentary analyses indicated lower overshoot and quicker convergence in PV and load disturbances. Computational profiling showed an average inference latency of 38 μs, meeting real-time control requirements on embedded ARM-dsp designs. These findings suggest that predictive control with machine learning support can be used to supplement the traditional SAPF capabilities and improve auxiliary PQ services in renewable-based distribution networks.