Abstract
In order to study the arc process of the SF6 circuit breaker during the current breaking process, it is necessary to calculate the physical parameters of the arc discharge plasma. However, the calculation of plasma physical parameters is very difficult and complicated and generally requires solving dozens of differential equations. Based on the machine learning method, this paper constructs a learning prediction model of physical property parameters in a local thermodynamic equilibrium state without solving a large number of differential equations so as to perform a rapid prediction of physical property parameters in other scenarios based on the existing physical parameter database. This paper uses the support vector machine, K-nearest neighbor algorithm, gradient boosting regression, decision tree, and random forest algorithm to predict and calculate the thermodynamic parameters and transport characteristics of SF6 at different pressures and temperatures. At the same time, this paper also predicts and calculates the parameters of the SF6–Cu mixed gas at different mixed concentrations. The results show that the machine learning algorithm can predict and generate consistent gas property parameter data, indicating that the model has good generalization performance. Finally, by comparing the error measures of the prediction results of various algorithms, the algorithm suitable for predicting the physical parameters is found to improve the prediction accuracy.
Highlights
As one of the core equipment of flexible DC grids, hybrid DC circuit breakers have a wide range of application prospects in the field of UHV power transmission.1 The SF6 circuit breaker is an important part of a 500 kV hybrid DC circuit breaker
In the analysis of arc plasma, it is usually assumed that it is in a local thermodynamic equilibrium (LTE), and the electron temperature is approximately equal to the temperature of the heavy particles
The five machine learning algorithms used in this article are the K-Nearest Neighbor (KNN) algorithm, Support Vector Machine Regression (SVMR), Gradient Boosting Regression (GBR), Decision Tree (DT), and Random Forest (RF)
Summary
As one of the core equipment of flexible DC grids, hybrid DC circuit breakers have a wide range of application prospects in the field of UHV power transmission. The SF6 circuit breaker is an important part of a 500 kV hybrid DC circuit breaker. To determine the thermodynamic properties and transport coefficients, the first thing to calculate is the particle composition of the gas. In the analysis of arc plasma, it is usually assumed that it is in a local thermodynamic equilibrium (LTE), and the electron temperature is approximately equal to the temperature of the heavy particles.. The transport coefficients (electrical conductivity, thermal conductivity, viscosity coefficient, etc.) are an important basis for studying the macroscopic properties of the arc, which are usually calculated by Chapman–Enskog theory.. In order to improve the calculation speed of arc plasma in the local equilibrium state, this paper uses machine learning as a tool to quickly calculate the thermodynamic parameters and transmission characteristics of thermal plasma. By constructing a model to predict the physical parameters of SF6 gas at unknown pressure and temperature, the feasibility of the machine learning calculation method is verified. This article compares the error between the predicted value and the actual value of five machine learning algorithms and finds a numerical prediction method that is relatively suitable for each physical characteristic
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