Spotted Hyena-Bat Optimized Extreme Learning Machine for Solar Power Extraction

Artificial intelligence, machine learning and deep algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global (GMPP) under incomplete concealing conditions stay overwhelming assignment tracks different nearby greatest power focuses halfway conditions. The advent artificial intelligence has guaranteed accurate GMPP while expanding significant performance efficiency Partial Shading Conditions (PSC). Still selection an efficient based is complex because each model its advantages drawbacks. Recently, Meta-heuristic algorithm Learning techniques provided better tracking but still exhibit dull performances PSC. This work represents excellent optimization on Spotted Hyena Enabled Reliable BAT (SHERB) models, SHERB-MPPT integrated with powerful extreme machines to identify fast convergence, low steady-state oscillations, good efficiency. Extensive testing using MATLAB-SIMULINK, 50000 data combinations gathered partial shade normal settings. As a result simulations, proposed approach offers 99.7% slower convergence speed. To demonstrate predominance system, we compared system other hybrid models. Results proved that cross breed had beaten recognizing viably fractional