Power Transformer Fault Diagnosis with Intrinsic Time-Scale Decomposition and XGBoost Classifier

Sami, Shoaib Meraj; Bhuiyan, Mohammed Imamul Hassan

doi:10.1007/978-981-16-6636-0_40

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Especially since XGBoost has demonstrated in several studies its performance in the field of medical diagnostics. (11,12,13)…”

Section: Resultsmentioning

confidence: 99%

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An artificial intelligence-based approach for an urgent detection of the pesticide responsible of intoxication

Ghanimi,

Fadoua,

Ghanimi

et al. 2023

Data and Metadata

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Acute poisoning by pesticides in Morocco is an important public health issue, because the use of pesticides has become both massive and anarchic. This is the cause of deaths whose incidence is unfortunately increasing. Unfortunately, these deaths are not always accidental. Pesticides are also used as a means of suicide; according to the WHO, these are means suicide chemicals most used in the world, since, out of the 800,000 suicides recorded per year, more than a third are caused by this type of product. Even more serious, these suicides are currently being observed among children and teenagers. Faced with this alarming figure, and in order to prevent deaths and improve emergency treatment of cases of pesticide poisoning, it becomes important to use the potential of artificial intelligence in the treatment of these admissions. Our approach is essentially based on machine learning algorithms, including decision support software capable of predicting, based on major clinical signs, the most likely pesticide responsible of the intoxication in the triage room. This, before moving on to the confirmation stage based on biological and toxicological investigations, which are often costly and time-consuming.

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“…Especially since XGBoost has demonstrated in several studies its performance in the field of medical diagnostics. (11,12,13)…”

Section: Resultsmentioning

confidence: 99%

“…This involves transforming the representation of the data in a larger space where they will be linearly separable. (9,10,11,12,13) To do this, we use the scalar product of our data with the space of larger dimensions. Nonlinear kernels make it possible to match this space of higher dimensions.…”

Section: Support Vector Machinementioning

confidence: 99%

An artificial intelligence-based approach for an urgent detection of the pesticide responsible of intoxication

Ghanimi,

Fadoua,

Ghanimi

et al. 2023

Data and Metadata

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“…The work in [11] proposed a deep-learning-based framework named SigdetNet, which takes the power spectrum as the network's input to localize the spectral locations of the signals. The research in [12] proposed an intrinsic time-scale decomposition (ITD)-based method for power transformer fault diagnosis based on dissolved gas analysis (DGA) parameters and used an XGBoost classifier to classify the optimal PRC feature set. The work in [13] proposed an artificial neural network (ANN) to establish the power transformer fault classification based on DGA.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

Mao

Wei

et al. 2022

Sensors

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The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers’ three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices’ fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy.

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Detection of Pesticide Responsible of Intoxication: An Artificial Intelligence Based Method

Ghanimi,

Ghanimi

et al. 2024

Lecture Notes in Networks and Systems

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Power Transformer Fault Diagnosis with Intrinsic Time-Scale Decomposition and XGBoost Classifier

Cited by 5 publications

References 18 publications

An artificial intelligence-based approach for an urgent detection of the pesticide responsible of intoxication

An artificial intelligence-based approach for an urgent detection of the pesticide responsible of intoxication

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

Detection of Pesticide Responsible of Intoxication: An Artificial Intelligence Based Method

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