Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

Prasojo, Rahman Azis; Putra, Muhammad Akmal A.; Ekojono, .; Apriyani, Meyti Eka; Rahmanto, Anugrah Nur; Ghoneim, Sherif S. M.; Mahmoud, Karar; Lehtonen, Matti; Darwish, Mohamed M. F.

doi:10.1016/j.epsr.2023.109361

Cited by 29 publications

(4 citation statements)

References 26 publications

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“…Random forest RF is an ensemble learning method employed for addressing regression and classification tasks that uses a combination of multiple decision trees and aggregates their prediction outcomes [35]. This technique uses the power of ensemble learning, which is a technique of combining multiple classifiers to solve complex problems more effectively [36]. The RF algorithm comprises numerous decision trees, forming a 'forest'.…”

Section: Support Vector Machinementioning

confidence: 99%

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Sharma,

Dhanka,

Kumar

et al. 2024

Eng. Res. Express

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Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model's performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.

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Section: Support Vector Machinementioning

confidence: 99%

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Sharma,

Dhanka,

Kumar

et al. 2024

Eng. Res. Express

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“…A multi information decision fusion method based on improved RF and evidence theory was proposed in [7], which considers the diagnostic distribution of all classification regression trees in RF and solves the conflict between multi information diagnostic results. In addition, Prasojo et al [8] put forward a RF fault diagnosis model with synthetic minority oversampling technique. Based on the complexity of fault signs, Wang et al [9] proposed a two-layer RF-based fault diagnosis method to isolate multiple faults in variable air volume terminals, which significantly improved the accuracy of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis framework based on heterogeneous ensemble learning for air conditioning chiller with unbalanced samples

Jia,

Yao,

Zhao

et al. 2024

Meas. Sci. Technol.

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The studies on air conditioning fault diagnosis based on big data have developed rapidly in recent years. However, the data imbalance phenomenon due to the small amount of fault data and the large amount of normal data shared in engineering will trigger problems such as poor model prediction and low accuracy. In order to solve the problem of imbalance fault diagnosis of air-conditioning chillers, this paper proposes an ensemble learning-based diagnostic model, which achieves diagnosis by combining multiple base models and through a majority voting method. The method uses four classification models, random forest model, decision tree model, k-nearest neighbor model, and isomorphic ensemble model, as base classifiers and synthesizes the four base classifiers into a heterogeneous ensemble algorithm through ensemble learning, and performs diagnostic detection of seven types of typical faults of chiller units using the majority voting method of integrated learning. The effectiveness of the proposed algorithm is verified on the RP-1043 dataset, and the experimental results show that the accuracy of the heterogeneous ensemble model can reach 96.87%, which is a significant improvement compared with the accuracy of the other four base classifier models (81.04% to 96.25%). Therefore, it can be considered that ensemble learning model is a promising way to solve the problem of unbalanced data fault diagnosis in engineering.

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“…These power transformers may be dry or oil-type transformers. In the oil-type power transformers, oil is used as an insulating and cooling medium [ 1 , 2 ]. The popular insulating oil used in oil-filled transformers is mineral oil (MO).…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment of Natural Ester–Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties

Karaman

Mansour

Lehtonen

et al. 2023

Sensors

Self Cite

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Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil.

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Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

Cited by 29 publications

References 26 publications

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

A fault diagnosis framework based on heterogeneous ensemble learning for air conditioning chiller with unbalanced samples

Condition Assessment of Natural Ester–Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties

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