Soft Computing -- Applied to Transformer Incipient Fault Diagnosis

Ahuja, Hemant; Bhuvaneswari, G.; Balasubramanian, R.; Singh, Arika

doi:10.1109/cicn.2012.187

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Liquid-filled power transformers are subject to electrical, thermal, and chemical stresses, thus causing degradation and ageing of the insulation and gradually leading to catastrophic internal incipient faults [3]. A sudden failure of a large power transformer causes inevitable prolonged outages in power supply, thus resulting in high repair costs, revenue losses, and environmental damage [4,5]. To prevent a transformer failure, incipient fault should be detected in the early stage.…”

Section: Introductionmentioning

confidence: 99%

Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation

Senoussaoui

Brahami

Fofana³

2018

IET Generation, Transmission & Distribution

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Section: Introductionmentioning

confidence: 99%

Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation

Senoussaoui

Brahami

Fofana³

2018

IET Generation, Transmission & Distribution

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Improved Self-Organizing Map Clustering of Power Transformer Dissolved Gas Analysis Using Inputs Pre-Processing

2020

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Ability to organize data spatially while conserving the topological relation between data features makes the Self Organizing Map (SOM) a very useful tool for analysis and visualization of high dimensional data such as a power transformer's Dissolved Gas Analysis (DGA). Past SOM application required large historical data for its training and has limited fault detection sensitivity. In this paper, the effects of input features and data normalization are studied to enhance SOM's clustering. SOM is trained using DGA results extracted from actual faulted transformers. Combination of input features and data normalization methods are tested on SOM before the best SOM is identified. Validation is conducted using several datasets i.e. the IEC Technical Committee 10 database. Compared with past SOM applications, the proposed SOM required lesser training data, improved SOM's sensitivity in incipient fault detection and has good diagnosis accuracy. The proposed SOM is also compared with other AI-based DGA interpretation method i.e. Support Vector Machine (SVM) for benchmarking.INDEX TERMS Dissolved gas analysis, fault detection, power transformer, self-organizing map, data pre-processing.

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Performance of Load Frequency Control of Two-Area Power System by Using Proportional Integral Derivative Controller

Maurya

Khan

Ahuja

2023

2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)

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Soft Computing -- Applied to Transformer Incipient Fault Diagnosis

Cited by 3 publications

References 12 publications

Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation

Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation

Improved Self-Organizing Map Clustering of Power Transformer Dissolved Gas Analysis Using Inputs Pre-Processing

Performance of Load Frequency Control of Two-Area Power System by Using Proportional Integral Derivative Controller

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