Abstract:As a critical indicator for assessing the survivability and condition of transformers in a fleet, the transformer health index has attracted attention from both asset owners and international organizations like CIGRE and IEEE DEIS/PES. To provide a systematic and comprehensive review for further study or to guide transformer asset management, this paper summarizes the state-of-the-art of the transformer health index, from the early proposed weighted-score-sum approaches to the more recently proposed artificial… Show more
“…The data gathered from the transformer’s historical events, conducted tests, as well as visual inspection is utilized to forecast imminent failures and EOL of each transformer, and according to these factors, a decision is made to prioritize transformers for repair, maintenance, or replacement if a transformer has reached its EOL 69 . Figure 3 demonstrates the primary descriptive framework of the approach utilized, which relies on HI calculations 70 , 71 . Figure 3 Predicting imminent failures and EOL based on HI and historical data.…”
Section: Methods Used To Investigate Transformers In-servicementioning
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.
“…The data gathered from the transformer’s historical events, conducted tests, as well as visual inspection is utilized to forecast imminent failures and EOL of each transformer, and according to these factors, a decision is made to prioritize transformers for repair, maintenance, or replacement if a transformer has reached its EOL 69 . Figure 3 demonstrates the primary descriptive framework of the approach utilized, which relies on HI calculations 70 , 71 . Figure 3 Predicting imminent failures and EOL based on HI and historical data.…”
Section: Methods Used To Investigate Transformers In-servicementioning
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.
“…The relationships used to determine the total transformer health index HI T and RFL are as follows [3,20,25]:…”
Section: Determination Of the Health Index And Service Life Of The Tr...mentioning
confidence: 99%
“…Several studies have shown that the transformer HI is a factor that uses information from operational studies, field inspections and laboratory tests to provide a reliable decision for the user [22][23][24][25][26].…”
Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.
“…Indeks kesehatan trafo yang diusulkan akan memberikan penilaian kondisi kesehatan trafo yang akan berguna untuk pemeliharaan, memastikan kinerja trafo yang optimal, peningkatan efisiensi dan juga peningkatan waktu hidup yang diharapkan. Berikutnya [4] menyatakan indeks kesehatan transformator dinyatakan dalam bentuk probabilitas, yang dapat dengan mudah memberikan pemahaman kepada staf tentang keadaan transformator dalam penggunaan praktis. Sebelum menghitung indeks kesehatan, perlu untuk mendapatkan statistik untuk lokasi kesalahan transformator dan faktor-faktor yang mempengaruhi lokasi ini sesuai dengan faktor-faktor seperti penggunaan transformator dan penyebab kesalahan dan untuk membuat diagram asiklik terarah.…”
Transformator merupakan salah satu peralatan yang sangat penting dalam sistem tenaga listrik. Fungsi utama transformator adalah sebagai penyalur daya atau tegangan, serta mengubah level tegangan dari satu level tegangan ke level tegangan yang lain. Transformator yang dilakukan penelitian merupakan transformator daya BAT GT 2.1 PLTGU Priok yang digunakan untuk step up tegangan dari keluaran generator menuju jaringan transmisi. Penelitian kondisi kesehatan dan predikasi umur transformator penting untuk dilakukan supaya diketahui bagaimana kondisi dan tindakan yang diperlukan pada transformator. Salah satu metode yang digunakan untuk analisa kondisi transformator adalah metode health index. Metode health index sendiri menggunakan 4 parameter pengujian transformator seperti parameter Dissolved Gas Analysis (DGA), kualitas minyak, furan dan kondisi tap changer. Dari hasil perhitungan total health index transformator BAT GT 2.1 PLTGU Priok nilai yang didapatkan adalah 82%. Berdasarkan nilai health index tersebut maka transformator BAT GT 2.1 PLTGU Priok berada dalam kondisi baik dan normal serta diperkirakan mampu beroperasi lebih dari 10 tahun.
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