An optimal design of ANNC-PID technique for renewable/ hydrogen energy powered seven-level shunt active power filter Using enhanced lyrebird algorithm

Distribution systems have been significantly impacted in the present scenario by incorporating solar and wind power electronic devices. This work optimally designs the solar PV system (SPVS), battery storage (BS), and fuel cell (FC) sourced seven-level cascade H-bridge converter for shunt active power filter (7L-SHAPF) by selecting the filter resistance and inductance parameters in addition to inductance of boost and buck boost converters of SPVS, BS, and FC by enhanced lyrebird optimization (LOA) called as (ELOA). The multilevel cascaded topology provides superior performances in power quality enhancement. The hybrid control ELOA trained ANNC-PID controller is developed by optimal selection of weights and bias values of artificial neural network controller (ANNC) by reducing mean square error (MSE) to obtain gain values of PID controller. The primary goal is to efficiently handle the current waveforms distortions with DC link voltage balancing, and to reduce the source current total harmonic distortion (THD) of the system connected to the grid that integrates SPVS, FC along with BESS which is denoted as (7L-SHAPF-SFCB). The effectiveness of the developed method is compared with genetic algorithm and artificial bee colony algorithm. The proposed hybrid optimally designed method reduces the THD to 3.53%, 2.44%, 4.15%, and 3.79% which are much lower than those of compared techniques and existing methods available in literature.