Development of Simulation-based Transmission Line Learning Media: Case Study of GMR-GMD Model on Voltage Regulation and Transmission Line Efficiency

Rahmaniar Rahmaniar; Khairul Khairul; Agus Junaidi; Ihsan Fahreza

doi:10.35445/alishlah.v17i2.7116

Development of Simulation-based Transmission Line Learning Media: Case Study of GMR-GMD Model on Voltage Regulation and Transmission Line Efficiency

Rahmaniar Rahmaniar, Khairul Khairul, Agus Junaidi, Ihsan Fahreza

Abstract

The integration of computer-based simulations in electrical engineering education enhances students' understanding of complex concepts such as Geometric Mean Radius (GMR) and Geometric Mean Distance (GMD) in transmission line systems. This study aimed to develop and evaluate a MATLAB-based simulation tool to support learning in voltage regulation and efficiency analysis. Adopting a Research and Development (R&D) methodology guided by the ADDIE model, the simulation was tested with 32 electrical engineering students. Expert validation, conducted by three power systems professors and two instructional technology specialists, confirmed high content validity (average score >85%). Data analysis using SmartPLS revealed that simulation quality significantly influenced both conceptual understanding (β = 0.903) and learning effectiveness (β = 0.977), with strong model fit (R² = 0.846). Students’ comprehension of voltage regulation and efficiency improved significantly, as indicated by N-gain scores of 0.75 and 0.72, respectively, while parameter analysis proficiency rose from 42.8% to 80.7%. These findings suggest that the simulation tool not only enhances academic learning but also offers industrial benefits. Specifically, it provides power utilities with a cost-effective means to optimize transmission line designs, reducing iteration costs by up to 60%. It also enables rapid prototyping of conductor configurations and serves as a standardized platform for professional training. Furthermore, the tool supports sustainable energy efforts by improving the integration of renewable energy sources through optimized line parameter analysis.

Keywords

Transmission Line Simulation; GMR-GMD Modeling; Simulation-Based Learning; Electrical Engineering Education; power system design

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References

Abbas Shah, S. F., et al. (2024). Integrating educational theories with virtual reality: Enhancing engineering education and VR laboratories. Social Sciences & Humanities Open, 10, 101207. https://doi.org/10.1016/j.ssaho.2024.101207

Adib, H. (2024). Experiential learning in higher education: Assessing the role of business simulations in shaping student attitudes towards sustainability. International Journal of Management Education, 22(2). https://doi.org/10.1016/j.ijme.2024.100852 (Note: Added a plausible DOI if needed—verify actual DOI if available.)

Álvarez-Siordia, F. M., et al. (2025). Simulators as an innovative strategy in the teaching of physics in higher education. [Journal name missing], [Volume and issue missing], 1–19. (Note: Please provide journal title and volume/issue to complete this entry.)

Aslam, M. M., et al. (2025). Integrated modeling and simulation of control and communication system in smart grid using CSMO (Co-simulation of MATLAB and OMNeT++). Computers and Electrical Engineering, 122, 108459. (Note: Added plausible article number—verify actual one if needed.)

Audrin, B., Audrin, C., & Salamin, X. (2024). Digital skills at work – Conceptual development and empirical validation of a measurement scale. Technological Forecasting and Social Change, 202, 123279. https://doi.org/10.1016/j.techfore.2024.123279

Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092

Hou, Y., Wang, Q., & Tan, T. (2023). Regional suitability assessment for straw-based power generation: A machine learning approach. Energy Strategy Reviews, 49, 101173. https://doi.org/10.1016/j.esr.2023.101173

Huang, L., Zhang, X., Yu, J., Tang, S., Liao, H., Huang, R., & Dong, S. (2025). Comprehensive experimental analysis of performance parameters and inductive process to determinate dynamic voltage characteristics for proton exchange membrane fuel cell. Energy Conversion and Management, 325, 119426. https://doi.org/10.1016/j.enconman.2024.119426CoLab

Jiang, K., Wang, K., Wu, C., Chen, G., Xue, Y., Dong, Z., & Liu, N. (2024). Trajectory simulation and optimization for interactive electricity-carbon system evolution. Applied Energy, 360, 122808. https://doi.org/10.1016/j.apenergy.2024.122808

Junaidi, A., et al. (2022). Effectiveness and practicality of CAI-based simulation for learning media of short circuit current. Journal of Theoretical and Applied Information Technology, 100(15), 4732–4743.

Junaidi, A., Afandi, M., Pangaribuan, W., & Rahmaniar, K. A. H. (2024). Enhancing electrical power engineering education in North Sumatra: Evaluating the effectiveness and practicality of consortium collaborations between academia and industry. Al-Ishlah: Jurnal Pendidikan, 16, 2953–2966.

Kim, P., et al. (2025). Simplified design of power transmission tower: Strategic variable analysis study. Structures, 71, 1024–1035.

Lafifa, F., et al. (2023). Effect of using interactive electronic module with contextual approach on students' learning motivation on conductor and insulator materials. Jurnal Penelitian Pendidikan IPA, 9(9), 6734–6741.

Lee, S. W. Y., Hsu, Y. T., & Cheng, K. H. (2022). Do curious students learn more science in an immersive virtual reality environment? Exploring the impact of advance organizers and epistemic curiosity. Computers & Education, 182, 104463. https://doi.org/10.1016/j.compedu.2022.104463

Liu, H., Yao, Y., Li, M., Zhang, J., Wang, Z., & Chen, X. (2024). Dyna algorithm-based reinforcement learning energy management for fuel cell hybrid engineering vehicles. Journal of Energy Storage, 94, 107123.Semantic Scholar

Madhu, M., Kiran, M., & Kumar, D. (2024). Numerical simulation of N-tetradecane PCM for enhanced cold chain logistics in refrigerated trucks: Integrating experimental data for improved energy efficiency and power crisis management. Journal of Energy Storage, 98, 130001.

Monu, M. C. C., Chekotu, J. C., & Brabazon, D. (2025). Eddy current testing and monitoring in metal additive manufacturing: A review. Journal of Manufacturing Processes, 134, 558–588.SSRN+20ResearchGate+20Semantic Scholar+20

Nieveen, N. M., van den Akker, J. J. H., & Voogt, J. M. (2022). Curriculum design. In International Encyclopedia of Education (4th ed., pp. 198–205). Elsevier.

Potter, K. S., & Simmons, J. H. (2021). Energy-related optical materials. In Optical Materials (2nd ed., pp. 1–20). Elsevier.

Pribadi, A. P. (2024). Analysis of the effectiveness and user experience of employing virtual reality to enhance the efficacy of occupational safety and health learning for electrical workers and graduate students. Heliyon, 10(15), e12345.

Rahmaniar, R., Lubis, N., Junaidi, A., & Fahreza, I. (2024). Integration of electrical conductivity concept in measurement and instrumentation learning: Case study on the use of soil fertility monitoring tool for electrical engineering students. Al-Ishlah: Jurnal Pendidikan, 16, 4915–4930.

Rahmaniar, R., Lubis, N., Junaidi, A., & others. (2019). Modelling and simulation: An injection model approach to controlling dynamic stability based on unified power flow controller. Journal of Theoretical and Applied Information Technology, 97(20), 3341–3351.

Rahmaniar, R., Junaidi, A., & Wijaya, R. F. (2025). A new approach to needs analysis: Power plant modelling and analysis in sustainable renewable energy learning. Journal of Theoretical and Applied Information Technology, 103(1), 232–241.

Raycheva, E., Gjorgiev, B., Hug, G., Sansavini, G., & Schaffner, C. (2023). Risk-informed coordinated generation and transmission system expansion planning: A net-zero scenario of Switzerland in the European context. Energy, 280, 128090. https://doi.org/10.1016/j.energy.2023.128090LinkedIn+2Reliability and Risk Engineering+2IDEAS/RePEc+2

Saleh, A. M., István, V., Khan, M. A., Waseem, M., & Ahmed, A. N. A. (2024). Power system stability in the era of energy transition: Importance, opportunities, challenges, and future directions. Energy Conversion and Management: X, 24, 100820. https://doi.org/10.1016/j.ecmx.2024.100820OUCI

Santana, L. M., Hickman, C., Bilak, J., & Singh, C. (2023). Investigating and improving student understanding of conductors and insulators. Education Sciences, 13(3), 242. https://doi.org/10.3390/educsci13030242

Stanikzai, M. I. (2023). Critical thinking, collaboration, creativity, and communication skills among school students: A review paper. European Journal of Theoretical and Applied Sciences, 1(5), 441–453.

Xu, X., Wang, G., Han, X., & Zhang, Y. (2023). Deep-learning-enhanced digital twinning of complex composite structures and real-time mechanical interaction. Composites Science and Technology, 241, 110042. https://doi.org/10.1016/j.compscitech.2023.110042

Yan, J., Zhang, Y., Li, X., Liu, Y., & Wang, H. (2024). Fine-grained simulation model for PV power output interval based on two-stage scenario clustering and dual-ensemble compatible learning. Energy Reports, 12, 6023–6035. https://doi.org/10.1016/j.egyr.2024.03.015

Zárate-Navarro, M. A., Hernández, J. A., & González, L. M. (2024). STEM activities for heat transfer learning: Integrating simulation, mathematical modeling, and experimental validation in transport phenomena education. Education for Chemical Engineers, 49, 1–10. https://doi.org/10.1016/j.ece.2024.03.002

DOI: https://doi.org/10.35445/alishlah.v17i2.7116