Improving diagnostic performance of a power transformer using an adaptive over-sampling method for imbalanced data

Tra, Viet; Duong, Bach-Phi; Kim, Jong‐Myon

doi:10.1109/tdei.2019.008034

Cited by 59 publications

(28 citation statements)

References 20 publications

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“…Moreover, a separate imbalanced classification phase is embedded into the model. In doing so, different types of algorithms, i.e., multi-task learning [28], adaptive sampling [44], and synthetic oversampling method [46], were integrated and tested.…”

Section: Imbalanced Handlingmentioning

confidence: 99%

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Ghahramani¹,

Galle²,

Ratti³

et al. 2021

Preprint

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Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people's opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform's reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of-and justification for-future investments in urban green space.

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Section: Imbalanced Handlingmentioning

confidence: 99%

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Ghahramani¹,

Galle²,

Ratti³

et al. 2021

Preprint

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“…The same optimal solution was obtained by using the three optimization techniques with the same diagnostic accuracy. Equation (22) presents the obtained optimal limits of gas percentages and Table III…”

Section: Code Implementationmentioning

confidence: 99%

Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

2020

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The transformer fault diagnosis based on dissolved gas analysis is greatly affected by the uncertainties existing in measured data during oil sampling, handling and storage. This work aims to develop an efficient code matrix based on dissolved gas percentages for accurate fault diagnosis considering measurement uncertainties. Fuzzy system is utilized to produce the rules that map the limits of gas ratios for different fault types. Each gas percentage range is divided into three regions represented by three fuzzy memberships. The fuzzy system is then developed to relate the gas percentages to the fault type. The membership limits are then optimized by using heuristic algorithms in order to maximize the accuracy. Hybrid Grey Wolf Optimization (HGWO) algorithm is utilized to produce the diagnostic code matrix. The proposed code limits the impact of measurement uncertainties on the output fault diagnosis. Different levels of measurement uncertainties up to 20% are considered to validate the effectiveness of the new code in improving the diagnostic accuracy. The accuracies during the training and testing processes attained 97.45% and 95.45%, respectively, with the maximum uncertainty level of 20%. Moreover, randomly selected case studies representing various fault types are investigated using the proposed method. For each case study, various levels of uncertainties are imposed on the original data. The proposed method proved its easiness towards diagnosing transformer faults and robustness against measurement uncertainties.

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“…As one of the main pillars of the current economy, electric energy is gradually accelerating the pace of its intelligent construction, and the scale is also expanding. The oil-immersed transformer, as the key hub of a power system, undertakes the task of power transmission and transformation of the whole power grid, and its operation condition will directly affect the safety of the power network and users [1][2][3][4]. However, insulation faults like partial discharge and partial overheating inevitably exist during oil-immersed transformer long running process [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

The Adsorption of H2 and C2H2 on Ge-Doped and Cr-Doped Graphene Structures: A DFT Study

Liao

Peng

et al. 2021

Nanomaterials

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In order to find an excellent sensing material for dissolved gases in transformer oil, the adsorption structures of intrinsic graphene (IG), Ge-doped graphene (GeG), and Cr-doped graphene (CrG) to H2 and C2H2 gas molecules were built. It was found that the doping site right above C atom (T) was the most stable structure by studying three potential doping positions of the Ge and Cr atom on the graphene surface. Then, the structural parameters, density of states, and difference state density of these adsorption systems were calculated and analyzed based on the density functional calculations. The results show that the adsorption properties of GeG and CrG systems for H2 and C2H2 are obviously better than the IG system. Furthermore, by comparing the two doping systems, CrG system exhibits more outstanding adsorption performances to H2 and C2H2, especially for C2H2 gas. Finally, the highest adsorption energy (−1.436 eV) and the shortest adsorption distance (1.981 Å) indicate that Cr-doped graphene is promising in the field of C2H2 gas-sensing detection.

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Improving diagnostic performance of a power transformer using an adaptive over-sampling method for imbalanced data

Cited by 59 publications

References 20 publications

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements

The Adsorption of H2 and C2H2 on Ge-Doped and Cr-Doped Graphene Structures: A DFT Study

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