Gaussian Process Regression based Model for Prediction of Discharge Voltage of Air Gaps under Positive Polarity Lightning Impulse Voltages

Vidya,; Sunitha, K.; Kumar, Deepa S.; Mishra, Deepak; Ashok, S.

doi:10.1109/ccece47787.2020.9255791

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…In recent years, the data-driven models based on artificial intelligence algorithms provide a new possibility to realize dielectric strength prediction, which is an alternative way except for the physical models and empirical formulas. Some artificial intelligence or machine learning algorithms, such as artificial neural networks (ANN) [17][18][19][20], support vector machine (SVM) [21][22][23][24][25], fuzzy logic [26,27], Gaussian process regression (GPR) [28], least squares regression (LSR) [29], and extremely randomized trees [30], etc., have been applied for breakdown voltage prediction of air gaps, transformer oils, and solid dielectrics like insulation paper and nanocomposites. These data-driven models mainly focus on the mathematical correlations between the dielectric strength and various influencing factors, and do not directly consider the discharge evolution process full of randomness and uncertainty, which is not restricted by the complex and unclear physical process.…”

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confidence: 99%

A machine learning‐based approach for dielectric strength prediction of long air gaps with engineering configurations

Qiu

Zhang

et al. 2022

IET Generation Trans & Dist

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It is a long‐term goal in external insulation studies to determine the discharge voltages of complicated engineering gaps by simulation methods. Based on the one‐to‐one correspondence between air gap structure and the static electric field (EF) distribution, this paper characterizes the transmission tower gap configuration by spatial EF features, which were used for machine learning to achieve switching impulse discharge voltage prediction. An interelectrode path and a conical zone between the energized sub‐conductor and the crossarm or the tower window were considered as EF regions strongly associated with gap breakdown, where 73 parameters were extracted to construct a feature set. Taking 15 extra‐high voltage (EHV) transmission tower gaps as training samples, with different gap distances and tower configurations, their EF features were input to a support vector machine (SVM) for model training to establish the relationships with discharge voltages. The trained SVM model was used to predict the impulse discharge voltages of 20 EHV and ultra‐high voltage (UHV) transmission tower gaps. The prediction results with different feature dimensions and various sizes of conical zones were compared to the experimental values, which demonstrate similar variation trends and acceptable errors. This study contributes to realize insulation strength calculation of engineering gaps.

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