Transformer Remnant Life Estimation and Asset Management model based on Insulation Stress Assessment

Mharakurwa, Edwell. T.; Nyakoe, George N.; Akumu, A. O.

doi:10.1109/eic43217.2019.9046526

Cited by 5 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data analysts and computational experts frequently use the term "artifact" to refer to various resources needed for model construction. Due to the experimental nature of ML [28], which necessitates preserving artifacts for later use, these artifacts qualify as assets in this context. Compared to the engineering of systems using ML, traditional software engineering frequently has fewer asset categories because it focuses exclusively on source code assets.…”

Section: • Grid Enlargement • Am • System Operation and Maintenancementioning

confidence: 99%

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

RAJORA,

Sanz-Bobi,

Tjernberg

et al. 2024

Preprint

View full text Add to dashboard Cite

Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.

show abstract

Section: • Grid Enlargement • Am • System Operation and Maintenancementioning

confidence: 99%

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

RAJORA,

Sanz-Bobi,

Tjernberg

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…e model in [12] was based on parameters that can potentially be measured online or on-site which facilitates a better and timely maintenance action based on the model output. A comprehensive multi-attribute residual life estimation model based on fuzzy logic was developed in [10,13]. However, some factors such as failure history, maintenance data, and loading regimes have been ignored when determining the remaining life of a power transformer.…”

Section: Overview Of Transformer Remnant Life Estimation Approachesmentioning

confidence: 99%

“…Among other ancillary equipment, power transformers' core, bushings, tap changers, and windings apparently have longer life as their aging is extremely slow. erefore, the performance and life issues of an oil-impregnated power transformer depend on the status of paper insulation system that has a limited life cycle [5][6][7][8][9][10][11]. In addition, paper is the most critical element of the transformer insulation system since its dielectric and mechanical properties cannot be recovered once compromised [11].…”

Section: Introductionmentioning

confidence: 99%

“…ey further articulated that if electrical and thermal insulation withstanding thresholds are exceeded, fault stresses occur which are noticeable through partial discharge, arcing, and localized overheating. Currently, in many utility organizations, transformer incipient faults are routinely diagnosed by dissolved gas analysis (DGA) approaches [5][6][7][8][9][10][16][17][18][19][20][21][22]. On the other hand, insulation degradation leads to aging stresses that also reduce transformer lifespan.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In-Service Power Transformer Life Time Prospects: Review and Prospects

Mharakurwa

2022

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Power transformers are essential assets in power system networks that must be meticulously monitored throughout their operational life cycle. Given that a substantial percentage of in-service power transformers around the world have reached the end of their projected technical lifespan, utilities are adopting various transformer condition-based maintenance strategies to minimize potentially devastating equipment failure. Thus, further usage of aging fleet of power system assets can be improved by gaining a better understanding of life threatening factors. In-service power transformers and their auxiliary components are susceptible to a variety of operational risks, which can compromise their performance and efficiency, thus resulting in devastating failures, financial losses, and power outages. Hence, it is necessary to investigate transformer life time issues so as to fully grasp the operational and performance status in order to reduce failures and operating costs. This paper presents a unified framework of attributes pertaining to life time issues for transformer operation evaluation together with a summary of recent findings in order to explore the current status and progress of this rapidly progressing field. The failure statistics analysis is presented first, followed by an examination of the transformer’s insulation degradation and aging mechanism. Furthermore, emphasis was on detailing the commonly adopted models and strategies in transformer condition evaluation, fault diagnostics, and remnant life estimation. Despite significant advancements in ascertaining transformer health diagnosis and prognosis technologies, including test accuracy, quick, precise fault localization, and fault type classification, there are still several shortcomings that require additional research, and thus future research perspectives are also discussed.

show abstract

“…An online algorithm to calculate and forecast transformer rating to assess the overload capability during short and long times was undertaken in (Alvarez et al, 2019). The possibility of developing a remaining life estimation and asset management decision model using diagnostics of transformer insulation characteristics was proposed in (Mharakurwa et al, 2019). The study adopted an integration of the fuzzy logic diagnostic tool and the fuzzy logic remnant life mapping model.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Enoch

George

Aning

2020

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

Distribution Transformer is a crucial element in deciding the power flow in large power systems. Their better performance implies high power system efficiency and enhanced power transfer capability. However, various Distribution Transformer failures in the recent past lead to power supply disturbance and have acquired much attention from the electrical intellectuals. It is of considerable significance to accurately get the running state of distribution transformers and timely detect the existence of potential transformer faults. This project work presents a predictive model to predict the potential of a distribution transformer failing before its expected years in service. Using Random Forest machine learning techniques, we examine transformer data from August 2010 to June 2019. Our experimental results reveal that a total of 90 distribution transformers were damaged within nine years. Thus, average the company losses ten (10) transformer in a year, which amount to the US $92300-95770 per year. Also, most of the places that recorded rate of distribution transformer damage were a location that had mini and major factories around. Thus, the Sunyani Municipality recorded the highest transformer damage (12), representing 13%, followed by Mim (10). Again, lighting strike was the significant causes of transformer damage. Thus twenty-one (21) out of the ninety (90) damage transformers was caused by a lightning strike. The results further show that 33.33% of the damage transformers were with 24.75-36.75% of their life expectancy. As low as 3.33% of the damage transformers have been in service for 73% of the life expectancy. From the study results, it can be concluded that a high percentage (68.9%) of the damage transformers in the Bono, Bono East and Ahafo regions of Ghana have been in service less the half of its expected years of service. Rate-offaulty-occurrence, Type-of-faults-sustained and Tap-changer-type are the most significant factors that determine the number of years left for a distribution transformer to fail. We observed that the make of a transformer was of less importance in predicting the years left for a transformer to fail. Finally, the RMSE of 0.001639 and MAPE error of 0.001321 achieved by the proposed model shows that the proposed model fits very well to the dataset.

show abstract

Transformer Remnant Life Estimation and Asset Management model based on Insulation Stress Assessment

Cited by 5 publications

References 14 publications

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

A Review of Asset Management using Artificial Intelligence based Machine Learning models with applications for the Electric Power and Energy System

In-Service Power Transformer Life Time Prospects: Review and Prospects

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Contact Info

Product

Resources

About