A Weighting Factor-Fuzzy logic based Transformer Residual Life Estimation Model

Mharakurwa, Edwell. T.; Juma, Shaibu. A.

doi:10.1109/powerafrica52236.2021.9543460

Cited by 2 publications

(2 citation statements)

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“…As a result, the fundamental factors used in life estimation were taken on an individual basis, which could potentially jeopardize the outcome because the parameters are closely interrelated. A weighting factor approach cascaded with a fuzzy inference system to determine the residual life of a power transformer was discussed in [14]. Rather than individual attributes, the model makes use of the grouping factor of technical life threatening agencies.…”

Section: Overview Of Transformer Remnant Life Estimation Approachesmentioning

confidence: 99%

“…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]. e inevitable irreversible chemical degradation of paper can be further escalated in the presence of accelerating agents such as excessive temperatures, moisture, and oxygen (that influence the rate of reaction), which are detrimental in life expectancy of the transformer [12][13][14][15]. Generally, transformers are designed to be online in the grid for more than 40 years [6].…”

Section: Introductionmentioning

confidence: 99%

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

Mharakurwa

2022

Journal of Electrical and Computer Engineering

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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.

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Section: Overview Of Transformer Remnant Life Estimation Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mharakurwa

2022

Journal of Electrical and Computer Engineering

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A review of control strategies for optimized microgrid operations

Juma,

Ayeng'o,

Kimambo

2024

IET Renewable Power Gen

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Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

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A Weighting Factor-Fuzzy logic based Transformer Residual Life Estimation Model

Cited by 2 publications

References 12 publications

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

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

A review of control strategies for optimized microgrid operations

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