Multiparameter-Based Fuzzy Logic Health Index Assessment for Oil-Immersed Power Transformers

Abstract

e health index scheme can be the most fundamental tool that unifies all transformer condition status information into a singular outcome, thereby enhancing the power transformer asset management and life longevity strategies. is study aims at establishing a multiple parameter-dependent transformer health index estimation model cascaded with a fuzzy logic inference system. is strategy is centered on the effect of dynamic loading regime, varying hotspot temperatures and multiple attesting results of the insulation system. Furthermore, a nonintrusive degree of polymerization (DP) model based on furans and carbon oxide ratios as DP pointers is also factored in developing the health index model. e general outcome of the health index depends on entirely considered elements, but not on any isolated attribute. Data obtained from in-service transformers were used to validate the proposed model. e outcome of the model mirrors the practical condition of the evaluated transformers. erefore, the proposed health index model can be a vital tool to asset managers and power utilities.

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

“…ereby, internal faults are typically revealed by the decomposition of oil-paper, which produces seven primary key gases. Carbon monoxide (CO) and carbon dioxide (CO 2 ) reveal paper degradation related faults; ethylene (C 2 H 4 ) and ethane (C 2 H 6 ) are significant in indicating increase of oil temperature; partial discharge being low level energy yields hydrogen (H 2 ) and methane (CH 4 ); arcing can be recognized by noting the progression of acetylene (C 2 H 2 ) and hydrogen (H 2 ) [17][18][19][20]40].…”

“…e conventional approaches rely on IEC and IEEE standards, which divide the status rank into "normal," "caution," "abnormal," and "severe" categories. Additionally, the outcomes of the transformer condition evaluation are typically presented as a condition or health ranking [40,130,131].…”

Health Index for Power Transformer Condition Assessment: A Comparison of Three Different Techniques.

“…Even though HI tends to use almost the same input framework, employed algorithms take different approaches to process input data. For instance, main algorithms include systems based on Weighted Sum Models (WSM) (Jahromi et al, 2009;Jian et al, 2020;Naderian et al, 2008;Tamma et al, 2020), systems based on the Markov chain model (Yahaya et al, 2017), fuzzy logic systems (Cerón et al, 2015;Mharakurwa & Goboza, 2019;Rosero-Z et al, 2018), and systems based on artificial neural networks (Islam et al, 2017).…”

“…Despite the several and different techniques to compute a HI for PT, a simple question can arise for an asset manager in charge of a PT fleet: which one is the adequate tool to compute HI. Having in mind the above question, and after an in-depth review of the HI techniques listed in the previous paragraph, three of them were selected to be compared in this article, i.e., (Cerón et al, 2015;Li & Song, 2014;Mharakurwa & Goboza, 2019). The selection of the HI techniques was supported by the fact that these use clearly defined quantities, measures, and test regularly done by PT owners during routine inspections.…”

“…Although (Li & Song, 2014) and (Cerón et al, 2015) use almost the same input parameters, (Li & Song, 2014) makes use of WSM to process data, while (Cerón et al, 2015) employs fuzzy logic. On the other hand, (Mharakurwa & Goboza, 2019) presents some differences in the set of input parameters regarding the other two techniques, and combines the weighting technique and the fuzzy logic methodology to reach the final HI score.…”