FPGA based substantial power evolution controlling strategy for solar and wind forecasting grid connected system

The solar and wind are both the most promising renewable and clean energy sources, the solar stable energy progress and environmental protection have been increasingly noticeable. In this regard, an accurate solar and wind energy prediction is extremely important to avoid large voltage changes to th...

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Bibliographic Details
Published inMicroprocessors and microsystems Vol. 74; p. 103001
Main Authors Anand, P., Mohana Sundaram, K.
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier B.V 01.04.2020
Elsevier BV
Subjects
Alternative energy sources
Clean energy
Commercialization
Electric power grids
Environmental protection
Evolution
Forecasting
Geographical locations
Grid forecasting
Hybrid systems
Mathematical models
Photovoltaic power
Prediction models
Research projects
Solar energy
Strategy
Substantial Power Evolution Strategy (SPES)
Wind power
Windmills
Substantial Power Evolution Strategy (SPES)
Grid forecasting
Wind power
Photovoltaic power
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Summary:The solar and wind are both the most promising renewable and clean energy sources, the solar stable energy progress and environmental protection have been increasingly noticeable. In this regard, an accurate solar and wind energy prediction is extremely important to avoid large voltage changes to the power grid and to provide a mechanism for the system to optimally manage the generated energy. Wind energy forecasting is widely practiced among modest power systems for high levels of windmills. This paper aims to develop a new hybrid system for wind and solar energy prediction. The proposed hybrid (wind & solar) energy prediction model is based on a Substantial Power Evolution Strategy (SPES) dedicated to short-term forecasting. The proposed forecasting system SPES is implemented using MATLAB. This paper implements the short-term and hybrid power forecasting using Substantial Power Evolution Strategy based on Prediction Intervals (PIs). This feature is one of the major innovations in the proposed hybrid renewable energy forecasting system. The accuracy of the proposed system will be revealed by comparing the results of the corresponding values of the independent forecasting models called persistence models. The designed device presents a real-time application of predicting daily total solar and wind power using any geographic location and environmental conditions using FPGA. Finally, fully developed system packages can be commercialized and/or utilized for further research projects, and researchers can analyze, validate and visualize their models for related fields.
ISSN:0141-9331
1872-9436
DOI:10.1016/j.micpro.2020.103001