Artificial intelligence for power quality optimization in renewable energy circulation relations

Hossain, Md Shahdat, Chadni, Farabi Hasan, Rahman, Md Atiqur

Innovative Multidisciplinary Journal of Applied Technology · 2023-yil

Annotatsiya

Background: The rising adoption of renewable power systems throughout present-day distribution systems has produced various obstacles which affect the delivery of stable power with high quality. The irregular operation of solar and wind power systems creates multiple problems which include voltage changes and harmonic interference and unstable power grid frequency. Methods: The study used a quantitative cross-sectional survey framework to assess AI systems optimize power quality for renewable energy networks which serve American power distribution systems. A total of 185 respondents, including engineers, utility operators, and energy researchers, participated in a structured questionnaire. Pearson correlation along with descriptive statistics based on percentage analysis to study the connection between AI adoption rates and power system performance indicators. Results: AI technology produces a 26% rise in fault detection precision and creates a 20% improvement in load distribution performance and voltage network stability. Machine Learning stands as the most popular AI method which receives 28% of usage while Neural Networks follow with 22% usage. AI adoption creates strong positive connections with power quality and system reliability and fault detection efficiency according to correlation analysis results r = 0.78, 0.74, and 0.81 respectively. The deployment of this technology faces three main obstacles which include expensive installation costs that affect 25% of users and 22% of users struggle to find qualified personnel and 15% of users need to handle security threats. Conclusion: Artificial Intelligence plays a crucial role in enhancing power quality and reliability in renewable energy distribution networks.

Maqola ma’lumotlari
MualliflarHossain, Md Shahdat, Chadni, Farabi Hasan, Rahman, Md Atiqur
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2023-04-01
Jild1
Son1
Betlar129-138
TilIngliz
Ilmiy sohaTexnika
DOI10.51699/y2xfck12
Indeksga qo‘shilgan2026-08-26

DOI: 10.51699/y2xfck12 · Maqolaning asl sahifasi

Kalit so‘zlar

Distribution Network, Power Quality, Renewable Energy, Artificial Intelligence

Ilmiy soha

Texnika

Iqtibos keltirish

APAHossain, Md Shahdat, Chadni, Farabi Hasan, & Rahman, Md Atiqur (2023). ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS. Innovative Multidisciplinary Journal of Applied Technology, 1(1), 129-138. https://doi.org/10.51699/y2xfck12
GOST (OAK)Hossain, Md Shahdat, Chadni, Farabi Hasan, Rahman, Md Atiqur ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS // Innovative Multidisciplinary Journal of Applied Technology. – 2023. – T. 1, № 1. – P. 129-138. – DOI: 10.51699/y2xfck12.
MLAHossain, Md Shahdat, Chadni, Farabi Hasan, and Rahman, Md Atiqur. “ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS.” Innovative Multidisciplinary Journal of Applied Technology, 1(1), 129-138, 2023. https://doi.org/10.51699/y2xfck12
ChicagoHossain, Md Shahdat, Chadni, Farabi Hasan, and Rahman, Md Atiqur. “ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS.” Innovative Multidisciplinary Journal of Applied Technology, 1(1), 129-138 (2023). https://doi.org/10.51699/y2xfck12
HarvardHossain, Md Shahdat, Chadni, Farabi Hasan, and Rahman, Md Atiqur (2023) ‘ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS’, Innovative Multidisciplinary Journal of Applied Technology, 1(1), 129-138. https://doi.org/10.51699/y2xfck12
BibTeX
@article{hossainmdshahdat2023,
  title = {ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS},
  author = {Hossain, Md Shahdat and Chadni, Farabi Hasan and Rahman, Md Atiqur},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2023},
  volume = {1},
  number = {1},
  pages = {129-138},
  doi = {10.51699/y2xfck12},
  url = {https://doi.org/10.51699/y2xfck12}
}

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