Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique
Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique
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DOI：
10.3390/en13153889
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发表时间：
2020-08-01
期刊：
影响因子：
3.2
通讯作者：
Nekahi, Azam
中科院分区：
文献类型：
--
作者：
Arshad;Ahmad, Jawad;Nekahi, Azam
至关重要的是要了解安全可靠的电源系统操作的聚合物绝缘体的闪存过程。本文对基于环境和使用机器学习的环境条件进行了严格的研究，以预测高温硫化（HTV）硅橡胶的闪存参数。基于IEC 60507标准的改良实心层方法用于制备实验室中的样品。研究了各种因素的影响，包括等效盐沉积密度（ESDD），非溶质盐沉积密度（NSDD），相对湿度和环境温度，在弧构构电压，闪光电压和表面电阻方面进行了研究。实验结果用于设计基于机器学习的智能系统，以预测上述闪存参数。在对Flashover参数的预测中探索了许多机器学习算法，例如人工神经网络（ANN），多项式支持矢量机（PSVM），高斯SVM（GSVM），决策树（DT）和最小二乘增强集合（LSBE）。该模型的预测准确性通过许多错误成本函数验证，例如均方根误差（RMSE），归一化RMSE（NRMSE），平均绝对百分比误差（MAPE）和R。为了提高预测准确性，使用引导程序来增加样品空间。提出的PSVM技术证明了与其他机器学习模型相比的性能准确性最佳。提出的机器学习模型提供了有希望的结果，并证明了在各种受污染且潮湿的条件下，有机硅橡胶绝缘子的弧量电压，闪存电压和表面电阻的高度准确预测。
There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions.