数据驱动的绝缘子积污特征量识别与污秽度预测

为准确预测复杂环境下输电线路绝缘子污秽度,实现污闪的有效预警,提出数据驱动的绝缘子积污特征量识别与污秽度预测方法。该方法结合改进粗糙集与样本加权方法,基于粒子群算法优化的误差反向传播神经网络,得到绝缘子积污特征量综合量化模型,对影响积污程度的因素进行量化识别。在识别的基础上,构建基于改进粗糙集的特征加权支持向量机,来预测绝缘子污秽程度,识别污闪风险。实例分析表明,该方法完全基于数据驱动,避免人为干预,能实现不同运行环境下绝缘子积污特征量的准确识别。相较于其他方法,所提污秽度预测和风险识别方法更精确,误差更小,具有良好的应用前景。

Identification of pollution characteristics of transmission line insulator and pollution prediction based on data driven

In order to accurately predict the pollution of transmission line insulators in complex environment and to achieve warning of pollution flashover, a method for identification of pollution characteristics of transmission line insulator and pollution prediction based on data driven is proposed.Combined with improved rough set and sample weighting method, a comprehensive quantitative model of insulator pollution characteristics based on PSO-BP neural network, and important characteristics affecting pollution can be quantified.On the basis of identification, a weighted support vector machine based on improved rough set is constructed to predict the pollution of the insulator and identify the risk of pollution flashover.The results show that the method is completely based on data driving and the charac-teristics of insulators with different operating environments and complex data types can be accurately identified.Compared with other methods, the proposed pollution prediction and risk identification method is more accurate and has smaller error due to the importance of the characteristics.The method proposed has good application prospects.