Improving Constant Power Supply in Renewable Energy Integration and Optimization Using ANN Based SVC

The consistent power failure in the country today that has crippled business activities that solely depend on power for their daily activities are caused by intermittency of renewable energy sources, inadequate energy storage systems, grid instability and voltage fluctuations, transmission and distribution losses, lack of grid flexibility and modernization, reactive power imbalance, inadequate backup power systems, weather-related factors, harmonic distortions and power quality issues and regulatory and policy barriers. To outwit this there was an introduction of improving constant power supply in renewable energy integration and optimization using ANN based SVC. To vehemently achieved this, it was done in this manner, characterizing and establishing the causes of power failure in improving constant power supply in renewable energy integration and optimization, training ANN in the established causes of power failure for effective minimization in improving constant power supply in renewable energy integration and optimization, designing a SIMULINK model for SVC, developing an algorithm that will implement the process, designing a SIMULINK model for improving constant power supply in renewable energy integration and optimization using ANN based SVC and validating and justifying percentage improvement in the reduction of establish causes of power failure in improving constant power supply in renewable energy integration and optimization with and without ANN based SVC. The results obtained were the conventional intermittency of renewable energy cause of power failure in improving constant power supply in renewable energy integration and optimization was30%. Meanwhile, when ANN based SVC was integrated in the system, it automatically reduced the core cause of power failure from 30% to 25.84% thereby improving constant power supply, the conventional Grid Instability and Voltage Fluctuations cause of power failure in improving constant power supply in renewable energy integration and optimization was 15%. Meanwhile, when an ANN based SVC was inculcated into the system, it drastically reduced to12.92% thereby enhancing consistent power supply and the conventional transmission and distribution losses cause of power failure in improving constant power supply in renewable energy integration and optimization was10%. However, when an ANN based SVC was imbibed into the system, it decisively reduced to 8.6%. Finally, with these results obtained, it definitely shown that the percentage improvement of constant power supply in renewable energy integration and optimization when an ANN based SVC was imbibed in the system was 1.4%improved in terms of consistent power supply in renewable energy integration to the grid.

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Publication Details

Journal
American Journal of Multidisciplinary Research and Innovation
Published
2025-05-29
DOI
https://doi.org/10.54536/ajmri.v4i3.4821
Primary Topic
Energy Load and Power Forecasting
Type
article
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article

Improving Constant Power Supply in Renewable Energy Integration and Optimization Using ANN Based SVC

Otto J. Gregory, Chukwuagu Monday Ifeanyi, Chukwu Linus
American Journal of Multidisciplinary Research and Innovation
Energy Load and Power Forecasting
article

Improving Constant Power Supply in Renewable Energy Integration and Optimization Using ANN Based SVC

Otto J. Gregory, Chukwuagu Monday Ifeanyi, Chukwu Linus
article en

Abstract

The consistent power failure in the country today that has crippled business activities that solely depend on power for their daily activities are caused by intermittency of renewable energy sources, inadequate energy storage systems, grid instability and voltage fluctuations, transmission and distribution losses, lack of grid flexibility and modernization, reactive power imbalance, inadequate backup power systems, weather-related factors, harmonic distortions and power quality issues and regulatory and policy barriers. To outwit this there was an introduction of improving constant power supply in renewable energy integration and optimization using ANN based SVC. To vehemently achieved this, it was done in this manner, characterizing and establishing the causes of power failure in improving constant power supply in renewable energy integration and optimization, training ANN in the established causes of power failure for effective minimization in improving constant power supply in renewable energy integration and optimization, designing a SIMULINK model for SVC, developing an algorithm that will implement the process, designing a SIMULINK model for improving constant power supply in renewable energy integration and optimization using ANN based SVC and validating and justifying percentage improvement in the reduction of establish causes of power failure in improving constant power supply in renewable energy integration and optimization with and without ANN based SVC. The results obtained were the conventional intermittency of renewable energy cause of power failure in improving constant power supply in renewable energy integration and optimization was30%. Meanwhile, when ANN based SVC was integrated in the system, it automatically reduced the core cause of power failure from 30% to 25.84% thereby improving constant power supply, the conventional Grid Instability and Voltage Fluctuations cause of power failure in improving constant power supply in renewable energy integration and optimization was 15%. Meanwhile, when an ANN based SVC was inculcated into the system, it drastically reduced to12.92% thereby enhancing consistent power supply and the conventional transmission and distribution losses cause of power failure in improving constant power supply in renewable energy integration and optimization was10%. However, when an ANN based SVC was imbibed into the system, it decisively reduced to 8.6%. Finally, with these results obtained, it definitely shown that the percentage improvement of constant power supply in renewable energy integration and optimization when an ANN based SVC was imbibed in the system was 1.4%improved in terms of consistent power supply in renewable energy integration to the grid.

American Journal of Multidisciplinary Research and InnovationVol. 4(3)
Enugu State University of Science and Technology (NG), Caritas University (NG)
Openalex Percentile: Top 90%
Energy Load and Power Forecasting
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