Efficient Strategy for Distribution Transformer Replacement: A Study on Replacement Methods in Power Systems
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Nur Asmi Rahmawati

Efficient Strategy for Distribution Transformer Replacement: A Study on Replacement Methods in Power Systems

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Introduction

Efficient strategy for distribution transformer replacement: a study on replacement methods in power systems. Optimize power system efficiency & reduce operating costs with an efficient strategy for distribution transformer replacement. This study uses economic analysis to identify optimal replacement times.

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Abstract

A dependable and efficient electric power distribution system is increasingly required due to growing industrial and population demand. Many distribution transformers currently in use are outdated, which increases operating expenses and reduces efficiency. This study examines the issue of determining the most cost-effective time to replace aging distribution transformers to optimize operational expenses and enhance service performance. The purpose of this study is to calculate the annual operating costs of dis-tribution transformers at PT PLN (Persero) Makassar Branch and to identify the optimal replacement time to support more effective decision-making. Using an economic feasibility analysis based on the annuity method and linear regression, this study compares the annual operating costs of old transformers with those of new ones. The findings indicate that the annual cost of old transformers is higher than that of new ones, suggesting that replacing old transformers is more economical. The results show that operating expenses can be reduced and distribution efficiency improved through a systematic transformer replace-ment approach. Based on economic engineering analysis, this study provides a practical and relevant model for transformer replacement decision-making that can assist asset management and investment planning in the power industry.


Review

The study, "Efficient Strategy for Distribution Transformer Replacement: A Study on Replacement Methods in Power Systems," addresses a critical and highly relevant challenge faced by power utilities globally: the management of aging distribution transformers. With increasing industrial and population demands necessitating a dependable power system, the issue of outdated assets leading to elevated operating expenses and reduced efficiency is paramount. This research provides a timely and practical investigation into determining the most cost-effective moment for transformer replacement, aiming to optimize operational costs and enhance service performance. Its focus on PT PLN (Persero) Makassar Branch grounds the study in a real-world operational context, making its findings directly applicable to utility asset management. The methodology employed in this study centers on an economic feasibility analysis, leveraging the annuity method and linear regression to compare the annual operating costs of existing older transformers against those of newer models. This approach is well-suited for quantifying the economic benefits of replacement. The findings strongly indicate that the annual costs associated with maintaining outdated transformers are significantly higher than those for new units, thereby presenting a clear economic justification for replacement. The study effectively demonstrates that a systematic transformer replacement program can lead to tangible reductions in operating expenses and marked improvements in distribution efficiency. Crucially, it contributes a practical and relevant model, derived from economic engineering principles, designed to aid decision-making in asset management and investment planning within the power industry. While the study offers a valuable framework for economic decision-making in transformer replacement, its primary focus on cost optimization and application to a specific utility branch suggests avenues for further research. Future work could explore the integration of non-economic factors such as reliability metrics, environmental impact, and outage risks into the replacement model to provide a more holistic decision support system. Additionally, validating this model across diverse utility landscapes and regulatory environments would enhance its generalizability and broader applicability. Nevertheless, the research provides a solid foundation for proactive asset management, offering a clear, data-driven argument for strategic investment in power infrastructure upgrades. This work is highly commendable for its practical relevance and potential to guide utilities toward more efficient and sustainable operational practices.


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