A cognitive system for fault prognosis in power transformers

Sica, Fernando Cortez; Guimarães, Frederico Gadelha; Duarte, Ricardo de Oliveira; Reis, Amanda Maria Sena

doi:10.1016/j.epsr.2015.05.014

Cited by 39 publications

(21 citation statements)

References 44 publications

(40 reference statements)

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“…Evaluating firms' knowledge sharing behavior merely by human cognition seems irrational. Cognitive systems are a kind of intellectual systems which can make or support complex decision makings with the help of data analytic techniques like machine learning techniques (known as artificial cognitive systems) [32] to improve human cognition and decision-making effectiveness [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Toward a Knowledge Sharing-Aimed Virtual Enterprise

Hajiheydari

Talafidaryani

Khabiri

2019

Advances in Intelligent Systems and Computing

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Section: Literature Reviewmentioning

confidence: 99%

Toward a Knowledge Sharing-Aimed Virtual Enterprise

Hajiheydari

Talafidaryani

Khabiri

2019

Advances in Intelligent Systems and Computing

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“…We demonstrated the effectiveness and usefulness by conducting a simulation for transformer fault diagnostics. The experimental results show that the ability of global optimization of GRNN with CSA can greatly improve the accuracy and robustness of the model. It is also obvious that the RS can be used to establish appropriate rules for demission reduction of the original data and the CSA is useful for optimizing GRNN by the smoothing factor.…”

Section: Introductionmentioning

confidence: 99%

An optimized GRNN‐enabled approach for power transformer fault diagnosis

Yang

Xie

et al. 2019

IEEJ Transactions Elec Engng

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This article presents an innovative approach for fault diagnosis based on an optimized generalized regression neural network (GRNN) by integrating with dissolved gas analysis, cuckoo search algorithm (CSA), and rough set theory (RS). In the proposed method, the high dimensioned data will be simplified and reduced by RS to generate better features or attributes for the GRNN input. Meanwhile, to enhance the network performance, the smoothing factor of GRNN is optimized by CSA with Levy flight, which leads to a good global convergence. As a consequence, CSA can provide a good solution to effectively improve the fault diagnosis performance. To validate and demonstrate the proposed method, we applied it to a real‐world fault diagnosis application, power transformer fault diagnosis, by comparing the results with those of other methods. From the experimental results obtained from the evaluation, it is obvious that the proposed fault diagnosis method enabled with RS‐CSA‐GRNN can provide a useful solution for power transformer fault diagnosis because it outperformed other GRNN‐based methods that deployed different optimizing algorithms such as the particle swarm optimization and genetic algorithm. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…The worst faults may even lead to the collapse of the entire power system, greatly hindering the development of the whole national economy. Therefore, it is important to study fault diagnosis technology pertaining to power transformers [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

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A cognitive system for fault prognosis in power transformers

Cited by 39 publications

References 44 publications

Toward a Knowledge Sharing-Aimed Virtual Enterprise

Toward a Knowledge Sharing-Aimed Virtual Enterprise

An optimized GRNN‐enabled approach for power transformer fault diagnosis

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

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