Improvement of voltage stability for grid connected solar photovoltaic systems using static synchronous compensator with recurrent neural network

Purpose. This article proposes a new control strategy for static synchronous compensator in utility grid system. The proposed photovoltaic fed is utilized along with recurrent neural network based reference voltage generation presented system network. novelty of the work consists presenting Landsman converter enhanced algorithm, to generate and maintain voltage-gain ratio. Methods. algorithm which provides sophisticated cost-effective solution utilization adaptive neuro-fuzzy inference as maximum power point tracking assures controlled output supports extraction complete from panel. Grid interconnected solar power, phase angle mismatch, harmonic instability may occur distribution grid. technique validated using MATLAB/Simulink software hardware model analysis working performances. Results. results obtained show that quality issue, overcome through elimination harmonics, current necessary, accomplished by By network, signal generated more accurately accordingly pulses are controlling inverter. Originality. Compensation issues, stability reduction controller. Practical value. concerns comparative study application controller achieve good performance presents between conventional compensator, implementation different load. on use continuous network-linear well non-linear loads efficient manner. simulation model.