针对滚动轴承故障振动信号在特征提取时出现的信息丢失、误动等不确定性问题以及故障诊断准确性不理想的问题,提出了一种基于概率盒理论和改进灰狼算法(grey wolf optimization,GWO)优化支持向量机(support vector machine,SVM)的混合...针对滚动轴承故障振动信号在特征提取时出现的信息丢失、误动等不确定性问题以及故障诊断准确性不理想的问题,提出了一种基于概率盒理论和改进灰狼算法(grey wolf optimization,GWO)优化支持向量机(support vector machine,SVM)的混合智能机械故障诊断方法。利用直接建模的方法得到概率盒,再采用累积不确定性测量方法提取其特征,构建出用于故障诊断的特征向量集;利用改进的灰狼算法对支持向量机进行优化;利用优化后的支持向量机实现对特征集的分类诊断。所提方法充分利用了概率盒在处理不确定性问题的优势和支持向量机在解决小样本、非线性模式识别中优秀的分类性能,可对不同故障类型的振动信号进行更加精准的辨识。通过对滚动轴承振动信号的试验验证与对比试验分析表明,该方法在滚动轴承故障诊断方面具有一定的有效性。展开更多
Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the developme...Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.展开更多
为了增强变压器故障诊断模型对不平衡样本的学习能力从而提高少数类故障样本的识别精度,提出了一种基于样本扩充和特征优选的融合多策略改进灰狼算法(improved grey wolf optimizer with multi-strategy,IGWO)优化支持向量机(support ve...为了增强变压器故障诊断模型对不平衡样本的学习能力从而提高少数类故障样本的识别精度,提出了一种基于样本扩充和特征优选的融合多策略改进灰狼算法(improved grey wolf optimizer with multi-strategy,IGWO)优化支持向量机(support vector machine,SVM)的变压器故障诊断技术。首先,使用基于K最近邻过采样方法及核密度估计自适应样本合成算法的混合过采样技术对少数类样本进行扩充得到均衡数据集,并在此基础上采用方差分析对变压器候选比值征兆进行特征优选。然后,通过改进灰狼优化算法(grey wolf optimizer,GWO)初始化策略、参数及位置更新公式,并引入差分进化策略调整种群,提出了融合多策略的改进灰狼算法。最后,构建了一种基于混合过采样技术的IGWO优化SVM的变压器故障诊断模型,并通过多组对比实验验证了所提方法能够有效增强模型对少数类故障样本的识别能力,并提升模型的整体分类性能。展开更多
文摘针对滚动轴承故障振动信号在特征提取时出现的信息丢失、误动等不确定性问题以及故障诊断准确性不理想的问题,提出了一种基于概率盒理论和改进灰狼算法(grey wolf optimization,GWO)优化支持向量机(support vector machine,SVM)的混合智能机械故障诊断方法。利用直接建模的方法得到概率盒,再采用累积不确定性测量方法提取其特征,构建出用于故障诊断的特征向量集;利用改进的灰狼算法对支持向量机进行优化;利用优化后的支持向量机实现对特征集的分类诊断。所提方法充分利用了概率盒在处理不确定性问题的优势和支持向量机在解决小样本、非线性模式识别中优秀的分类性能,可对不同故障类型的振动信号进行更加精准的辨识。通过对滚动轴承振动信号的试验验证与对比试验分析表明,该方法在滚动轴承故障诊断方面具有一定的有效性。
基金supported by Science and Technology Project of State Grid Corporation of ChinaNational Natural Science Foundation of China (No.51777104)China State Key Laboratory of Power System (No.SKLD16Z08)
文摘Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.
文摘为了增强变压器故障诊断模型对不平衡样本的学习能力从而提高少数类故障样本的识别精度,提出了一种基于样本扩充和特征优选的融合多策略改进灰狼算法(improved grey wolf optimizer with multi-strategy,IGWO)优化支持向量机(support vector machine,SVM)的变压器故障诊断技术。首先,使用基于K最近邻过采样方法及核密度估计自适应样本合成算法的混合过采样技术对少数类样本进行扩充得到均衡数据集,并在此基础上采用方差分析对变压器候选比值征兆进行特征优选。然后,通过改进灰狼优化算法(grey wolf optimizer,GWO)初始化策略、参数及位置更新公式,并引入差分进化策略调整种群,提出了融合多策略的改进灰狼算法。最后,构建了一种基于混合过采样技术的IGWO优化SVM的变压器故障诊断模型,并通过多组对比实验验证了所提方法能够有效增强模型对少数类故障样本的识别能力,并提升模型的整体分类性能。
