Battery management in IoT hybrid grid system using deep learning algorithms based on crowd sensing and micro climatic data.

Hybrid Grid System (HGS) installation in small and large residential area has major challenges due to domestic loads. Domestic loads are in different duty cycle such as (i) continuous duty i.e., vehicle charging, (ii) short time duty, (iii) periodic duty and (iv) intermittent duty. In this paper, proposed HGS comprises of Internet of Thing (IOT), Photovoltaic (PV) system and wind system (PWS) with Lithium-Phosphate battery paralleled with Super-capacitor, Deep learning controller with PWS is termed as IOT enabled PWS (IPWS). IPWS has zero export converters, reduces electricity demand on grid. Zero-export inverter avoids excess energy to grid and excess energy stored in super-capacitor. IPWS has crowd sensing for microclimatic conditions data acquisition system. Microclimatic Data is used for tuning zero export converters and Battery Management System (BMS) through IPWS. IPWS controller perform with different hybrid Deep learning algorithm such as (i) SCO-LSTM controller and JO-LSTM based BMS (ii) JO-LSTM controller and HBO-LSTM based BMS (iii) HBO-LSTM controller and SCO-LSTM based BMS. IPWS reduces time and space complexity in controller. Among the proposed methods, IPWS with JO-LSTM/ HBO-LSTM based BMS eliminates output power fluctuations and increases transient stability (TS) and damping ratio (DR). Comparative analysis for DC-link and super-capacitor in IPWS is presented. IPWS with JO-LSTM controller, super-capacitor suits for residence loads and provides 29% improved power factor, reduces harmonics 14%, DR of 6%, and low TS.