Hybrid prediction of the power frequency breakdown voltage of short air gaps based on orthogonal design and support vector machine

Qiu, Zhibin; Ruan, Jiangjun; Huang, Daochun; Wei, Mengting; Tang, Liezheng; Huang, Congpeng; Xu, Wilsun; Shu, Shi

doi:10.1109/tdei.2015.005398

Cited by 21 publications

(15 citation statements)

References 28 publications

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“…Through the introduction of ϵ ‐insensitive loss function , SVM is generalized to solve the regression problems, and it is divided into SVC and SVR. In this paper, SVR is used to establish the discharge voltage prediction model, which is different from the previous studies . The principle of SVR is briefly introduced as follows.…”

Section: Fundamental Of the Svr Prediction Modelmentioning

confidence: 99%

“…Based on cross validation , the parameter set ( C , γ , ϵ ) is optimized using a GA in this paper. GA is a heuristic algorithm based on evolution theory and genetics, and the fundamental is random information exchange and survival of the fittest.…”

Section: Fundamental Of the Svr Prediction Modelmentioning

confidence: 99%

“…In recent years, a prediction model developed by the authors based on electric field features and a support vector machine (SVM), a machine‐learning algorithm, provides a new idea to solve this problem . This method regards the discharge process as a gray box, and the SVM was used to establish the relationships between the gap structure, characterized by the static electric field distribution and the breakdown voltage, without direct consideration of the random discharge process.…”

Section: Introductionmentioning

confidence: 99%

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Application of a SVR model to predict lightning impulse flashover voltages of parallel gaps for insulator strings

Qiu

Wang

Ruan

2019

IEEJ Transactions Elec Engng

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Discharge voltage prediction of engineering gap configurations is a great challenge for external insulation design in high-voltage engineering. This paper presents an air gap discharge voltage prediction model based on the support vector regression (SVR). A series of features are defined on the shortest interelectrode path of an air gap, and they are extracted from the electric field calculation results using the finite element method. These electric field features are considered as the input parameters of the SVR-based model, and the output is the air gap discharge voltage. The genetic algorithm is used for parameter optimization of the SVR model. Trained by a few experimental data of the Institute of Electrical and Electronics Engineers standard rod-rod gaps, this model is applied to predict the standard lightning impulse flashover voltages of parallel gaps for insulator strings under different gap configurations. The prediction results coincide well with the experimental data collected from previous publications. The mean absolute percentage error of the eight test samples is within 4.07%, which is acceptable for engineering applications.The SVR-based model may be helpful to guide the structure design and insulation coordination of parallel gaps and insulator strings, thus reducing the required full-scale tests.

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Section: Fundamental Of the Svr Prediction Modelmentioning

confidence: 99%

Section: Fundamental Of the Svr Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of a SVR model to predict lightning impulse flashover voltages of parallel gaps for insulator strings

Qiu

Wang

Ruan

2019

IEEJ Transactions Elec Engng

Self Cite

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“…The artificial neural network (ANN) [32,33], fuzzy logic system [34,35], and support vector machine (SVM) [36,37] have been applied to predict the discharge voltages of air insulation gaps. In [36,37], a method based on electric field features and SVM was proposed for discharge voltage prediction of air gaps, and it has been successfully applied to predict the breakdown voltages of air gaps with typical and atypical electrodes [38][39][40] and the corona onset voltages of rod-plane gaps, conductors, and valve hall fittings [41,42]. Some features were extracted from the calculation results of the electric field distribution of an air gap to characterize its spatial structure, and the SVM was applied to establish the multidimensional nonlinear relationships between these features and the air gap discharge voltage.…”

Section: Introductionmentioning

confidence: 99%

“…The penalty factor C and the kernel parameter γ determine the classification performance of SVM. They can be optimized by grid search (GS) method or genetic algorithm (GA) based on K-fold cross validation or leave-one-out (LOO) cross validation, so as to obtain the optimal predictive model [36][37][38][39].…”

Section: Brief Introduction Of Svmmentioning

confidence: 99%

Electric Field Features and Its Application for Air Gap Breakdown Voltage Prediction

Qiu¹,

Ruan²

2018

Electric Field

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Air gap breakdown voltage prediction by mathematical calculations instead of experiments has been a long sought goal in the area of high-voltage engineering. In this chapter, a prediction method is proposed based on the electric field features and support vector machine (SVM). Two sets of electric field features are defined on the shortest interelectrode path of sphere-sphere and rod (sphere)-plane gap to characterize their spatial structures, which can be extracted from the electric field calculation results by finite element method (FEM). A breakdown voltage prediction model is established by SVM, while the input parameters are the electric field features, and the output parameters are À1 and 1, respectively, characterizing withstanding and breakdown of an air gap under the applied voltage. The proposed method is used to predict the power frequency breakdown voltages of IEC standard sphere-sphere air gaps and the switching impulse discharge voltages of large sphere-plane air gaps. The prediction results coincide well with the experimental data, the mean absolute percentage error of the 260 spheresphere gaps is within 2% and that of the 16 sphere-plane gaps is 3.2%. The results preliminarily validate the validity and accuracy of the proposed method for air gap breakdown voltage prediction.

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Power Frequency Breakdown Voltage Prediction of Air Gaps

2019

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Hybrid prediction of the power frequency breakdown voltage of short air gaps based on orthogonal design and support vector machine

Cited by 21 publications

References 28 publications

Application of a SVR model to predict lightning impulse flashover voltages of parallel gaps for insulator strings

Application of a SVR model to predict lightning impulse flashover voltages of parallel gaps for insulator strings

Electric Field Features and Its Application for Air Gap Breakdown Voltage Prediction

Power Frequency Breakdown Voltage Prediction of Air Gaps

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