Research Article
ENHANCING THE PERFORMANCE OF WEARABLE TECHNOLOGY IN AN INDUSTRY USING ARTIFICIAL NEURAL NETWORK (ANN) BASED SOLID STATE VAR COMPENSATOR (SSVC)
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 187 - Issue 23 |
| Published: July 2025 |
| Authors: Chukwuagu Monday Ifeanyi, Chukwu Linus, Onyegbadue Ikenna Augustine |
10.5120/ijca2025925292
|
Chukwuagu Monday Ifeanyi, Chukwu Linus, Onyegbadue Ikenna Augustine . ENHANCING THE PERFORMANCE OF WEARABLE TECHNOLOGY IN AN INDUSTRY USING ARTIFICIAL NEURAL NETWORK (ANN) BASED SOLID STATE VAR COMPENSATOR (SSVC). International Journal of Computer Applications. 187, 23 (July 2025), 52-60. DOI=10.5120/ijca2025925292
@article{ 10.5120/ijca2025925292,
author = { Chukwuagu Monday Ifeanyi,Chukwu Linus,Onyegbadue Ikenna Augustine },
title = { ENHANCING THE PERFORMANCE OF WEARABLE TECHNOLOGY IN AN INDUSTRY USING ARTIFICIAL NEURAL NETWORK (ANN) BASED SOLID STATE VAR COMPENSATOR (SSVC) },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 23 },
pages = { 52-60 },
doi = { 10.5120/ijca2025925292 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Chukwuagu Monday Ifeanyi
%A Chukwu Linus
%A Onyegbadue Ikenna Augustine
%T ENHANCING THE PERFORMANCE OF WEARABLE TECHNOLOGY IN AN INDUSTRY USING ARTIFICIAL NEURAL NETWORK (ANN) BASED SOLID STATE VAR COMPENSATOR (SSVC)%T
%J International Journal of Computer Applications
%V 187
%N 23
%P 52-60
%R 10.5120/ijca2025925292
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The integration of wearable technology in industrial environments has revolutionized workforce monitoring, safety, and operational efficiency. However, the performance and reliability of these devices are often compromised by inconsistent power quality and electromagnetic interference within industrial settings. This study proposes the enhancement of wearable technology performance through the implementation of an Artificial Neural Network (ANN)-based Solid State Var Compensator (SSVC). The ANN-based SSVC dynamically analyzes and compensates for voltage fluctuations and reactive power imbalances, thereby stabilizing the power supply to wearable devices. By intelligently adapting to real-time power conditions, the system ensures uninterrupted functionality, minimizes signal distortions, and extends device lifespan. Simulation results demonstrate significant improvements in voltage regulation, power factor correction, and overall system stability. The proposed approach offers a scalable and intelligent power management solution for robust wearable technology operation in complex industrial environments, promoting higher productivity, worker safety, and technological resilience. The conventional Power Quality Issues that cause poor performance of wearable technology in an industry was 25%. However, when an ANN based solid state VAR compensator (SSVC) was incorporated in the system, it drastically reduced it to22.5% and the conventional Inadequate Battery Life that cause poor performance of wearable technology in an industry was 10%. Meanwhile, when an ANN based solid state VAR compensator (SSVC was imbibed into it, it automatically reduced it to 9%. Finally, with these results obtained the percentage enhancement in the performance of wearable technology in an industry when an ANN based solid state VAR compensator (SSVC was integrated into it became 1%.
References
- Kumar, R., & Singh, S. (2021). Artificial neural network-based reactive power compensation for industrial power systems. International Journal of Electrical Power & Energy Systems, 130, 106933. https://doi.org/10.1016/j.ijepes.2021.106933
- Singh, A., Verma, Y., & Sharma, R. (2022). Real-time power quality enhancement using ANN-based SVC in smart industrial networks. Journal of Industrial Electronics and Applications, 9(2), 75–84.
- Zhou, J., Wang, H., & Chen, Y. (2020). Wearable technology in industry: Current status and future trends. IEEE Access, 8, 137904–137920. https://doi.org/10.1109/ACCESS.2020.3011901
Index Terms
Keywords