21 research outputs found
An Approach of The Analytic Hierarchy Process to Acquire The Weighting Factor of High Voltage Circuit Breaker Health Index
No full textMachine learning based multi-method interpretation to enhance dissolved gas analysis for power transformer fault diagnosis
No full textAccurate interpretation of dissolved gas analysis (DGA) measurements for power transformers is essential to ensure overall power system reliability. Various DGA interpretation techniques have been proposed in the literature, including the Doernenburg Ratio Method (DRM), Roger Ratio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duval Pentagon Method (DPM). While these techniques are well documented and widely used by industry, they may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used gases fall outside the specified limits. Incorrect interpretation of DGA measurements can lead to mismanagement and may lead to catastrophic consequences for operating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles to integrate existing interpretation methods into one comprehensive technique. The robustness of the proposed method is assessed using DGA data collected from several transformers under various health conditions. Results indicate that the proposed multi-method, based on the scoring index and random forest; offers greater accuracy and consistency than individual conventional interpretation methods alone. Furthermore, the multi-method based on random forest demonstrated higher accuracy than employing the scoring index only
Identifikasi gangguan dan analisis gas terlarut untuk transformator daya
No full textBuku ini menyajikan pembahasan terkait sistem tenaga listrik, dengan fokus khusus pada transformator daya—mulai dari prinsip kerja, sistem isolasi, hingga analisis gas terlarut untuk identifikasi gangguan. Melalui pembahasan yang terstruktur dalam sebelas bab, pembaca akan dibawa memahami secara mendalam pentingnya transformator dalam sistem tenaga listrik, cara kerjanya, hingga strategi terkini dalam mendeteksi dan menganalisis potensi ganggua. Supaya lebih paham baca terlebih dahulu daftar isi Buku Teknik Elektro terbaik ini
A Multiple Expert Consensus Model for Transformer Assessment Index Weighting Factor Determination
No full textMengujikan Internet Addiction Test (IAT) ke Responden Indonesia
No full textInternet telah menjadi bagian tak terpisahkan dari kehidupan Manusia. Sejumlah penelitian mengemukakan konsep kecanduan internet sebagai disorder. Tujuan dari penulisan artikel ini adalah untuk mengujikan IAT menggunakan Bahasa Indonesia dengan responden 514 orang kemudian melakukan pembahasan tentang perdebatan adiksi internet berdasarkan sisi filsafat ilmu pengetahuan. Hasil uji reabilitas yag dilakukan menghasilkan reabilitas yang baik, yaitu Cronbach’s Alpha 0.895 dan hasil uji validitas dari 20 pertanyaan hanya pada pertanyaan 7 yang memiliki koefisien korelasi yang lebih rendah dari 0.4 sehingga jika dilihat dari sisi filsafat ilmu pengetahuan, dengan melakukan survey dan perhitungan statistika, IAT membuktikan bahwa IAT adalah logis,terbukti secara empirik maka termasuk dalam kategori sains bukan pseudosains
Study on Correlation Among Oil Dielectric Characteristics, Dissolved Gases, and Operating Life of 150 kV Power Transformer
No full textAssessment of High Voltage Power Transformer Aging Condition Based on Health Index Value Considering Its Apparent and Actual Age
No full textImproving Transformer Health Index Prediction Performance Using Machine Learning Algorithms with a Synthetic Minority Oversampling Technique
No full textMachine learning (ML) has emerged as a powerful tool in transformer condition assessment, enabling more accurate diagnostics by leveraging historical test data. However, imbalanced datasets, often characterized by limited samples in poor transformer conditions, pose significant challenges to model performance. This study investigates the application of oversampling techniques to enhance ML model accuracy in predicting the Health Index of transformers. A dataset comprising 3850 transformer tests collected from utilities across Indonesia was used. Key parameters, including oil quality, dissolved gas analysis, and paper condition factors, were employed as inputs for ML modeling. To address the class imbalance, various oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, SMOTE-Tomek, and SMOTE-ENN, were implemented and compared. This study explores the impact of these techniques on model performance, focusing on classification accuracy, precision, recall, and F1-score. The results reveal that all SMOTE-based methods improved model performance, with SMOTE-ENN yielding the best outcomes. It significantly reduced classification errors, particularly for minority classes, ensuring better predictive reliability. These findings underscore the importance of advanced oversampling techniques in improving transformer diagnostics. By effectively addressing the challenges posed by imbalanced datasets, this research provides a robust framework for applying ML in transformer condition monitoring and other domains with similar data constraints
Dissolved Gas Analysis of Generator Step Up Transformer in Grati Power Plant Using Random Forest Based Method
No full textTransformers are one of the important electrical equipment in the power system. To prevent some electrical contact on the component in transformers, an insulator or dielectric material is needed likely insulating oil. DGA test is important for diagnosis and deciding the maintenance of transformers. Duval Triangle and Duval Pentagon methods are DGA identification methods with the highest of accuracy compared to other methods. The data used in this article is from the DGA measurement test of transformers GT 3.1 Steam and Gas Power Plant Grati. The DGA data was analyzed by Random Forest based-model of Duval Triangle and Pentagon method, in accordance to IEEE C57.104-2019 and IEC 60599-2015 guidelines. Random Forest based-model has the best performance in implemented Duval method than others. The result of DGA identification using Random Forest based-model showed PD and S for Duval Triangle, and S for Duval Pentagon and from the results of identification using the Duval Triangle and Pentagon it does not always show the same results on the same test sample, so it is necessary to identify the history of DGA testing to get accurate results. This article presents the use of the combined Duval Triangle and Pentagon for diagnosis transformers
Model-building of multiple binary logit using model averaging
No full textMany researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC
