CFP last date
22 September 2025
Call for Paper
October Edition
IJCA solicits high quality original research papers for the upcoming October edition of the journal. The last date of research paper submission is 22 September 2025

Submit your paper
Know more
Random Articles
Reseach Article

Improving Project Management Software using Fuzzy based Static Var Compensator (SVC)

by Chukwuagu Monday Ifeanyi, Chukwu Linus, Onyegbadue Ikenna Augustine
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 34
Year of Publication: 2025
Authors: Chukwuagu Monday Ifeanyi, Chukwu Linus, Onyegbadue Ikenna Augustine
10.5120/ijca2025925289

Chukwuagu Monday Ifeanyi, Chukwu Linus, Onyegbadue Ikenna Augustine . Improving Project Management Software using Fuzzy based Static Var Compensator (SVC). International Journal of Computer Applications. 187, 34 ( Aug 2025), 6-20. DOI=10.5120/ijca2025925289

@article{ 10.5120/ijca2025925289,
author = { Chukwuagu Monday Ifeanyi, Chukwu Linus, Onyegbadue Ikenna Augustine },
title = { Improving Project Management Software using Fuzzy based Static Var Compensator (SVC) },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2025 },
volume = { 187 },
number = { 34 },
month = { Aug },
year = { 2025 },
issn = { 0975-8887 },
pages = { 6-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number34/improving-project-management-software-using-fuzzy-based-static-var-compensator-svc/ },
doi = { 10.5120/ijca2025925289 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-22T14:52:00.257192+05:30
%A Chukwuagu Monday Ifeanyi
%A Chukwu Linus
%A Onyegbadue Ikenna Augustine
%T Improving Project Management Software using Fuzzy based Static Var Compensator (SVC)
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 34
%P 6-20
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today’s dynamic and complex project environments, efficient project management software plays a critical role in ensuring successful execution and delivery. However, challenges such as computational overload, system instability, real-time data inconsistency, and limited adaptability to fluctuating workloads often hinder performance. This research introduces a novel approach to enhancing the performance and reliability of project management software by integrating a Fuzzy Logic-Based Static VAR Compensator (SVC). The proposed system applies fuzzy inference to dynamically stabilize the software’s computational environment, optimize processing voltage conditions, and reduce system lag during high-demand tasks. Through simulation and analysis, the fuzzy-based SVC demonstrated significant improvements in execution speed, task scheduling accuracy, system responsiveness, and resource utilization. The study provides a new interdisciplinary framework that bridges power systems engineering with software performance optimization, offering an intelligent, adaptive solution for managing uncertainty and complexity in project management platforms. This advancement paves the way for the development of more robust and scalable project management tools aligned with the demands of Industry 4.0. The conventional inadequate user interface that causes poor project management software was20%. On the other hand, when Fuzzy based static VAR compensator (SVC) was incorporated into the system, it decisively reduced it to17.3% and the conventional inadequate risk management that caused poor project management software was 8%. On the other hand, when Fuzzy based static VAR compensator (SVC) was integrated into the system, it automatically reduced it to 6.9%. Finally, with these results obtained, it showed that percentage improvement of project management software when Fuzzy based static VAR compensator (SVC) was input in the system was 1.1%.

References
  1. Bou Nassif, A., Azzeh, M., Idri, A., & Abran, A. (2019). Software development effort estimation using regression fuzzy models. arXiv preprint arXiv:1902.03608. https://arxiv.org/abs/1902.03608
  2. Ogorodova, A., Shamoi, P., & Karatayev, A. (2024). Fuzzy intelligent system for student software project evaluation. arXiv preprint arXiv:2405.00453. https://arxiv.org/abs/2405.00453
  3. Sharma, A., & Ranjan, R. (2019). Software effort estimation using neuro fuzzy inference system: Past and present. arXiv preprint arXiv:1912.11855. https://arxiv.org/abs/1912.11855
  4. Ogata, K. (2010). Modern control engineering (5th ed.). Prentice Hall.
  5. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
  6. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  7. Hingorani, N. G., & Gyugyi, L. (2000). Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. IEEE Press. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  8. Hingorani, N. G., & Gyugyi, L. (2000). Understanding FACTS: Concepts and technology of flexible AC transmission systems. IEEE Press.
  9. Kerzner, H. (2022). Project management: A systems approach to planning, scheduling, and controlling (13th ed.). Wiley.
  10. Zadeh, L. A. (1996). Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 4(2), 103–111. https://doi.org/10.1109/91.493904
Index Terms

Computer Science
Information Sciences

Keywords

Project Management Software Fuzzy logic Static Var Compensator