Hossain, Md Shahdat and patel, Uravang and Rahman, Md Atiqur (2024) Optimization of Series and Shunt Capacitor Compensation for Power Factor Improvement in Electrical Power Systems. American Journal of Engineering , Mechanics and Architecture, 2 (3). pp. 220-228. ISSN 2993-2637

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Abstract

Background: Power factor improvement stands as a vital requirement because low power factor generates multiple challenges which include rising power loss during transmission and unstable voltage levels and decreased system operational capacity. The solution to these problems arrives through capacitor compensation which uses shunt capacitors as the main solution for actual operational systems. Methods: This study conducted a quantitative data gathering first-hand information from 185 electrical experts completed a standardized survey. The study selected participants demonstrated specific expertise through its purposive sampling technique. Descriptive statistics together with Pearson correlation analysis to study power factor improvement relates to shunt capacitors and series capacitors and load levels and harmonic distortion and voltage levels and power loss reduction. Results: Shunt capacitors serve as the primary solution which users select at 68.1% frequency while hybrid systems follow at 57.8% and users select series capacitors at 51.9%. The primary advantage exists in power factor enhancement which accounts for 40.0% of the benefits followed by efficiency improvement at 28.1%. The correlation analysis demonstrates that shunt capacitor usage produces the most substantial positive correlation with power factor improvement at r = 0.72 and series capacitors create a substantial effect at r = 0.65. Power loss reduction (r =0.60) and voltage level (r =0.54) demonstrate moderate positive relationships. The system performance shows negative effects from harmonic distortion because of the correlation coefficient which reached -0.51. Conclusion: The study establishes that shunt capacitor compensation serves as the main power factor enhancement method which delivers the most effective results.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	28 Jun 2026 23:23
Last Modified:	28 Jun 2026 23:23
URI:	http://eprints.umsida.ac.id/id/eprint/16658

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