Smart IOT based Short Term Forecasting of Power Generation Systems and Quality Improvement Using Resilient Back Propagation Neural Network

Abstract

In modern times, the instant improvement of changeable energy generations especially as of wind and solar energy resources in the power grid led with these generations developing a significant resource of ambiguity with behavior of load however living being the major variable source. It is crucial for the economic scheduling in the generation and load balance of the generating units and in trades of electricity market. The forecasting of energy managed to alleviate several challenges to encounter which rises the resource uncertainty. Solar and wind power prediction is observing a intensifying interest from the community research and numerous investigations working on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. Therefore, this work presents a Resilient Back Propagation Neural Network (RBPN) model to produce solar and wind power Short Term Forecasting (STF) and monitoring using Internet of Things (IoT).However, STF is very complex to handle due to the random and nonlinear characteristics of solar irradiance and wind speed under changeable weather conditions. But the proposed Resilient Back Propagation Neural Network (RBPN) is suitable for STF modeling and also the proposed forecasting system is directly connected to IEEE-9 bus to reduce Total Harmonics Distortion (THD) and also reduce the power quality issues in various conditions, such as voltage unbalance control, active and reactive power control. The performance of proposed forecasting system is validated through both hardware and simulation, the simulation is developed by using Matlab Simulink software. In the proposed system, the sensitivity analysis for varied variables and the model comparison with the aspect of proper selection incorporating the persistence and multiple linear regression models. Therefore, execution of the Internet of Things (IoT) in the supervising of solar PV and wind forecasting system was recommended and its implementation was analyzed. Suggested system comprised of data acquisition, data gateway, and smartphone application demonstration.