Construction and Verification of Novel Insulation Defect Location System with High Space Resolution for Next Generation Power Module

Maki, Junya; Matsuzoe, Takakazu; Kozako, Masahiro; Hikita, Masayuki; Nakamura, Yoko; Taniguchi, Koki; Ikeda, Yoshinari; Okamoto, Kenji

doi:10.23919/icep.2019.8733578

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…This value of neurons is proceeding with U-Matrix analysis for performance verification. [11,9] 0.000 0.205 0 120 [12,10] 0.000 0.057 1 140 [13,11] As depicted in Figure 8, the mapping result exposed a bit of unsatisfactory mapping. Whereas, there are certain normal (label N7) situation data is drift away (green dotted line) to the PD activities region.…”

Section: 'Log' Normalization Resultsmentioning

confidence: 99%

Intelligent PD Classification via SOM with Optimized Correlation

2019

IJITEE

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The Malaysian power industry was a regulated, organized electricity supply wise inherited from British colonization and showed to be robust and reliable throughout the millennium. The electricity sector is always committed to ensuring that supply reliability is preserved on a continual basis without any major disturbance. The most prevalent downtime causes are closely linked to high voltage (HV) equipment malfunctions or defects, especially transformers, switchgears and cables. The consequences of these occurrences are catastrophic and lead to the loss of millions of dollars in terms of the refurbishment or replacement of HV infrastructure. Power utility company has mostly attempted to stop this event from happening through HV equipment monitoring to classify the irregularity before it is more severe. Partial discharge (PD) event is among the phenomena that is being measured and evaluated. PD is commonly found in solid, gaseous or fluid form correlated with void. In this research, an unsupervised approach to the neural network is recommended for PD classification. This journal introduces the PD classification use an analysis based on the combo of self-organizing maps (SOMs) and correlation analysis. This project established that PD data was successfully correlated and clustered using MATLAB software's SOM Toolbox and correlation tool to identify the type of PD in a HV apparatus.

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Section: 'Log' Normalization Resultsmentioning

confidence: 99%

Intelligent PD Classification via SOM with Optimized Correlation

2019

IJITEE

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“…The implementation of CBM facilitates engineers with a new descriptive way of understanding the need for different maintenance regimes according to the level of health and ageing of each HV equipment [6][7][8]. A PD survey is the most accurate way to evaluate on HV apparatus or cable over the live operation [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A smart partial discharge classification SOM with optimized statistical transformation feature

et al. 2021

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Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.

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Construction and Verification of Novel Insulation Defect Location System with High Space Resolution for Next Generation Power Module

Cited by 2 publications

References 2 publications

Intelligent PD Classification via SOM with Optimized Correlation

Intelligent PD Classification via SOM with Optimized Correlation

A smart partial discharge classification SOM with optimized statistical transformation feature

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