PD source recognition by Weibull processing of pulse height distributions

Contin, A.; Montanari, Gian Carlo; Ferraro, C

doi:10.1109/94.839341

Cited by 87 publications

(37 citation statements)

References 21 publications

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“…As observed in Fig. 5, the shape factor varies between 2.5 < ȕ < 3 at the preliminary stage, which is an indication of an internal discharge source [6], [7]. However, this value dropped during the last days before breakdown to 1.5 < ȕ < 2, possibly an indication of the existence of multiple discharge source.…”

Section: A Circuit Imentioning

confidence: 81%

Practical experiences with on-line PD monitoring and interpretation for MV cable systems

Gargari

Wouters

Wielen

et al. 2010

2010 10th IEEE International Conference on Solid Dielectrics

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Section: A Circuit Imentioning

confidence: 81%

Practical experiences with on-line PD monitoring and interpretation for MV cable systems

Gargari

Wouters

Wielen

et al. 2010

2010 10th IEEE International Conference on Solid Dielectrics

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“…Numerous experimental results demonstrate that the life of components, equipment, and systems that cause the global function to stop running owing to the failure or breakdown in certain parts obey the Weibull distribution [18]. Moreover, according to Reference [19], the life of liquid insulation obeys a Gumbel distribution, while the lifetime of solid insulation follows a two-parameter distribution or lognormal distribution. Therefore, this paper applies a two-parameter Weibull distribution to research the life distribution features of hot-spot absolute temperature insulation samples.…”

Section: Overloading Probability Measurementmentioning

confidence: 99%

Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers

Wang

et al. 2016

Energies

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Smart grids have been constructed so as to guarantee the security and stability of the power grid in recent years. Power transformers are a most vital component in the complicated smart grid network. Any transformer failure can cause damage of the whole power system, within which the failures caused by overloading cannot be ignored. This research gives a new insight into overload capability assessment of transformers. The hot-spot temperature of the winding is the most critical factor in measuring the overload capacity of power transformers. Thus, the hot-spot temperature is calculated to obtain the duration running time of the power transformers under overloading conditions. Then the overloading probability is fitted with the mature and widely accepted Weibull probability density function. To guarantee the accuracy of this fitting, a new objective function is proposed to obtain the desired parameters in the Weibull distributions. In addition, ten different mutation scenarios are adopted in the differential evolutionary algorithm to optimize the parameter in the Weibull distribution. The final comprehensive overload capability of the power transformer is assessed by the duration running time as well as the overloading probability. Compared with the previous studies that take no account of the overloading probability, the assessment results obtained in this research are much more reliable.

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“…Since the gray intensity images contain a large amount of PD information, the PD characteristics including statistics characteristic [2], fractal characteristic [3][4][5], wavelet characteristic [6,7], and moment characteristic [8] are usually extracted from two and three dimensional images Q-qn (discharge frequency phase Q, discharge amplitude q, and discharge numbers n). Because of the high pattern description and distinguishing ability and a few characteristics parameters, the fractal characteristic is widely used in the characteristics extraction of PD images and some achievements have been made.…”

Section: Introductionmentioning

confidence: 99%

PD pattern recognition based on multi-fractal dimension in GIS

Zhang

Yao

Tang

et al. 2008

Front. Mech. Eng. China

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This paper designs four types of gas insulated substation (GIS) defect models based on partial discharge (PD) characteristics and its defections. The GIS gray intensity images are constructed based on the mass specimens gathered by the ultra-high frequency and high-speed sampling systems. The multi-fractal dimension is founded on the box-counting dimension and multi-fractal theories. The GIS gray intensity images distillation methods, based on multi-fractal characteristics, is put forward. The box-counting dimension, multi-fractal dimension, and discharge centrobaric characteristics of the PD images are also extracted. The characteristic variables are then classified by the radial basis function (RBF) network. Identified results show that the methods can effectively elevate the discrimination of the four types of defects in GIS.

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PD source recognition by Weibull processing of pulse height distributions

Cited by 87 publications

References 21 publications

Practical experiences with on-line PD monitoring and interpretation for MV cable systems

Practical experiences with on-line PD monitoring and interpretation for MV cable systems

Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers

PD pattern recognition based on multi-fractal dimension in GIS

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