IoT enabled microgrid framework using a novel dispersal diffusion artificial neural network controller for PV systems and wind energy to minimize electrical faults

A system based on a PV-Wind will ensure better efficiency and flexibility using lower energy production. Today, plenty of work is being focussed on Doubly Fed Induction Generators (DFIG) utilized in wind energy systems. DFIG is found to be the best option in the Wind Energy Conversion Systems (WECS) to mitigate the issues caused by power converters. In this work, a new Artificial Neural Network (ANN) is proposed with the Diffusion and Dispersal strategy that works on Maximum Power Point Tracking (MPPT) along with Wind Energy Conversion System (WECS)to minimize electrical faults. The controller focus was not just to increase performance but also to reduce damage owing to any phase to phase fault or Phase to phase to ground fault. To ensure optimal MPPT for the proposed WECS, ANN achieves the optimal PI controller parameters for the indirect control of active and reactive power of DFIG. The optimal allocation and size of the DGs within the distributed system and for MPPT control are obtained using a population of agents. The generated solutions are evaluated and on being successful, the agents test their hypothesis again to create a positive feedback mechanism. Simulations are carried out, and the proposed IoT framework efficiency indicates performance improvement and faster recovery against faults by 9 percent for phase to ground fault and by 7.35 percent for phase to phase fault.