基于多维特征与随机森林的低压用电安全隐患预警方法

A warning method for low-voltage electrical safety hazard based on multi-dimensional features and random forests

故障电弧作为低压用电常见安全隐患,因存在隐蔽性和随机性,导致难以在故障前夕有效感知,现有保护方法通常于故障发生后采取措施,容易造成电气火灾发生。针对这些问题,提出基于多维特征与随机森林的低压用电安全隐患预警方法。首先,搭建真型实验平台复现多种负载故障电弧真实场景,采集实验数据进行去噪、归一化和样本化等预处理;接着分析故障前、后波形变化情况,进而实行特征有效性分析,提取多维与故障相关程度较高的特征值,提高特征普适性。然后,搭建随机森林模型并进行超参数优化,以最小化节点信息熵为目标完成模型训练,提升模型整体性能和学习效率。最后,通过实验验证表明,所提方法在多种不同负载运行工况下预测准确率可高达99.4%以上,且预测准确率高于4种主流分类预测模型。

As a common safety hazard in low-voltage electricity use,fault arcs are difficult to perceive effectively on the eve of failure due to their concealment and randomness.Existing protection methods usually take measures after the occurrence of faults,which can easily cause electrical fires.To address these issues,a warning method for low-voltage electrical safety hazard is proposed on basis of multi-dimensional features and random forests.Next,a random forest model is built and hyper-parameters are optimized,with the goal of minimizing node information entropy to complete model training,so that enhances the overall performance and learning efficiency of the model.Finally,experimental verification shows that the proposed method achieves a prediction accuracy of over 99.4%with different loads,and its prediction accuracy is higher than that of four traditional classification prediction models.