为了快速、准确地对高压断路器发生的故障进行分析和诊断,确定故障的性质、类别和部位,提出了一种高压断路器故障诊断的新方法。首先对高压断路器分合闸线圈电流进行分析,提取电流和时间特征量形成特征向量,然后用遗传算法对最小二乘支...为了快速、准确地对高压断路器发生的故障进行分析和诊断,确定故障的性质、类别和部位,提出了一种高压断路器故障诊断的新方法。首先对高压断路器分合闸线圈电流进行分析,提取电流和时间特征量形成特征向量,然后用遗传算法对最小二乘支持向量机(least square support vector machine,LS-SVM)参数进行优化,最后,将特征向量输入到优化后的最小二乘支持向量机中进行故障识别、分类。试验表明,该方法可以准确地识别断路器的多种故障类型,为断路器故障定位和状态检修提供了依据。与广泛使用的神经网络方法相比,该方法在样本较少时仍能获得较好的诊断效果,更适用于高压断路器等小样本设备的故障诊断。展开更多
Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied...Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus,SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application.展开更多
文摘为了快速、准确地对高压断路器发生的故障进行分析和诊断,确定故障的性质、类别和部位,提出了一种高压断路器故障诊断的新方法。首先对高压断路器分合闸线圈电流进行分析,提取电流和时间特征量形成特征向量,然后用遗传算法对最小二乘支持向量机(least square support vector machine,LS-SVM)参数进行优化,最后,将特征向量输入到优化后的最小二乘支持向量机中进行故障识别、分类。试验表明,该方法可以准确地识别断路器的多种故障类型,为断路器故障定位和状态检修提供了依据。与广泛使用的神经网络方法相比,该方法在样本较少时仍能获得较好的诊断效果,更适用于高压断路器等小样本设备的故障诊断。
文摘Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus,SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application.
