支持向量机在变压器状态评估中的应用研究
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摘要
变压器是电力系统和电气化铁路牵引供电系统的重要设备,其安全可靠性对电力系统和铁路的安全运营影响重大。变压器的状态评估是实现其状态维修的基础。
支持向量机很好地执行了统计学习理论的结构风险最小化原则,在小样本情况下具有较好的泛化能力,避免了陷入局部极小值,解决了故障诊断领域面临的典型故障样本严重不足的主要难题,逐渐成为智能诊断的有力工具。
支持向量机的研究虽已取得了一定进展,但如何将这些研究成果应用到对电力系统和电气化铁路供电系统具有重大影响的变压器的状态评估中去,还是一个新颖的、富有挑战性的课题。论文围绕支持向量机在变压器故障诊断、故障预测和运行状态评估中的应用进行了研究,作了一些有意义的工作,具体内容包括以下几个方面:
1.用支持向量机的多分类方法对基于油中溶解气体的变压器故障诊断进行了研究。主要使用了两种典型的多分类算法即一对一的分类方法和M-ary分类方法,提出了一种改进的M-ary支持向量机模型,通过该模型的具体应用证明了算法的正确性和有效性;并且对支持向量机中的参数选择问题进行了研究,使用改进的遗传算法求解了支持向量机中的参数,实验表明,与基本遗传算法相比,该方法能够在较大范围内准确地找到相应的优化参数,并能有效地进行变压器的故障诊断。
2.研究了支持向量机的回归方法在变压器故障预测中的应用。首先引入最小二乘支持向量机(LS-SVM)作为预测器进行了变压器油中气体体积分数的预测,分析研究了LS-SVM模型的适应性和用不同数据建立的LS-SVM模型参数的变化情况;然后对灰色模型进行优选,引入了灰色多变量模型,提出了改进的离散灰色模型和改进的灰色多变量模型,进一步提高了预测精度;最后提出了一种以LS-SVM作为组合器的优选灰色、LS-SVM组合预测模型,该模型综合考虑了优选灰色模型及LS-SVM的特点;应用实例分析表明了所提模型的有效性和优越性。
3.研究了支持向量机在变压器运行状态评估中的应用。针对目前的变压器运行状态评估模型主要采用模糊综合评价的方法,提出了基于评分法的模糊综合支持向量机模型,该方法使用模糊综合评价结果作为支持向量机的输入,综合考虑了各结果之间的非线性关系,判断准确率有所提高;为了减少运行状态评估中的人为因素,消除伪特征的干扰,提出了基于成分分析和支持向量机的变压器运行状态评估模型,对比研究了PCA、KPCA、ICA和PCA+ICA共4种特征提取方式,说明了在运行状态评估问题中进行数据预处理有利于分类算法的实现,KPCA为变压器的运行状态评估提供了一种有效可行的数据预处理方式;为了进一步提高评估的正判率,获得更加稳定的评估效果,本文还提出了改进的KPCA+SVM评估模型,采用了混合核函数和并行优化策略,变压器的运行评估实例分析证明了改进算法的有效性。
The transformer is a major apparatus in the power system and the traction power supply system.Failure of a transformer will definitely harm the safety and stability of the power system and the electrified railway.Transformer condition assessment is the foundation of the transformer condition based maintenance.
Support vector machine(SVM) based on statistical learning theory(SLT) accomplishes the structural risk minimization principle,avoids being trapped into local minima and has fine performance to limited samples.It solves the difficulty that fault intelligent diagnosis system faces,which is terrible lack of typical fault samples,and is becoming gradually one of the powerful tools in intelligent diagnosis.
Although the application research of SVM in diagnosis has obtained certain achievement,how to perform the novel method effectively in transformer condition assessment is a great challenge.Therefore,the application of SVM to the transformer intelligent fault diagnosis,fault forecasting and running condition assessment are developed in this thesis.The main contributions of this dissertation are as follows:
1.The intelligent diagnosis of transformer based on gases dissolved in transformer oil is studied with the support vector classification.The method for conversion from two-class to multi-class is analyzed.Two kinds of multi-class algorithms are studied, i.e.the one-against-one method and the M-ary method.An improved M-ary SVM algorithm is proposed.The test results prove the effectiveness and superiority of the improved algorithm.Furthermore,the selection of kernel function parameters is discussed.The parameters are optimized using improved genetic algorithm.Compared with general genetic algorithm,the optimum can be found accurately in a wide range using the proposed method and the value can be used to diagnose the transformer effectively.
2.The prediction of gases dissolved in transformer oil is investigated using support vector regression.The least square support vector machine(LS-SVM) is introduced into the concentration prediction of dissolved gases.The adaptability of the LS-SVM model and the situation of the parameters varied with the data for building the model are analyzed.In order to get optimized grey model,the multi-variable grey model is recommended to the concentration prediction of dissolved gases in transformer oil,an improved discrete grey model and an improved multi-variable grey model with higher accuracy are proposed.Moreover,a combined forecasting model is put forward using the LS-SVM combination.In the combined model the advantages of the optimized grey model and the LS-SVM model are integrated.The effectiveness and superiority of the mentioned models have been verified with the results of the actual case.
3.The running condition assessment of transformer is realized with support vector machine.Aiming at the status that fuzzy synthetic evaluation is a main method for transformer running condition assessment,a fuzzy synthetic SVM model based on scores is proposed.The results of the fuzzy synthetic evaluation are the inputs of the SVM,the actual states of the transformer are the outputs.The examples show that,the running condition assessment for transformer using the new model is more accurate.In order to reduce the human factors,eliminate the misleading features and retain the genuine information,some condition assessment models for transformer based on component analysis and SVM are proposed.Four methods for extracting features are studied,viz.PCA,KPCA,ICA,and PCA+ICA.From the results it is concluded that the feature data after extracted lead to better separation,which suggests that data preprocessing in practical running condition assessment is favorable to the realization of classification algorithm,KPCA is an effectively feasible data preprocessing method for the transformer running condition assessment.In order to improve the accuracy and mend the effect of evaluation,an improved KPCA+SVM model is proposed.Mixtures of kernels and parallel optimized strategy are used.
支持向量机很好地执行了统计学习理论的结构风险最小化原则,在小样本情况下具有较好的泛化能力,避免了陷入局部极小值,解决了故障诊断领域面临的典型故障样本严重不足的主要难题,逐渐成为智能诊断的有力工具。
支持向量机的研究虽已取得了一定进展,但如何将这些研究成果应用到对电力系统和电气化铁路供电系统具有重大影响的变压器的状态评估中去,还是一个新颖的、富有挑战性的课题。论文围绕支持向量机在变压器故障诊断、故障预测和运行状态评估中的应用进行了研究,作了一些有意义的工作,具体内容包括以下几个方面:
1.用支持向量机的多分类方法对基于油中溶解气体的变压器故障诊断进行了研究。主要使用了两种典型的多分类算法即一对一的分类方法和M-ary分类方法,提出了一种改进的M-ary支持向量机模型,通过该模型的具体应用证明了算法的正确性和有效性;并且对支持向量机中的参数选择问题进行了研究,使用改进的遗传算法求解了支持向量机中的参数,实验表明,与基本遗传算法相比,该方法能够在较大范围内准确地找到相应的优化参数,并能有效地进行变压器的故障诊断。
2.研究了支持向量机的回归方法在变压器故障预测中的应用。首先引入最小二乘支持向量机(LS-SVM)作为预测器进行了变压器油中气体体积分数的预测,分析研究了LS-SVM模型的适应性和用不同数据建立的LS-SVM模型参数的变化情况;然后对灰色模型进行优选,引入了灰色多变量模型,提出了改进的离散灰色模型和改进的灰色多变量模型,进一步提高了预测精度;最后提出了一种以LS-SVM作为组合器的优选灰色、LS-SVM组合预测模型,该模型综合考虑了优选灰色模型及LS-SVM的特点;应用实例分析表明了所提模型的有效性和优越性。
3.研究了支持向量机在变压器运行状态评估中的应用。针对目前的变压器运行状态评估模型主要采用模糊综合评价的方法,提出了基于评分法的模糊综合支持向量机模型,该方法使用模糊综合评价结果作为支持向量机的输入,综合考虑了各结果之间的非线性关系,判断准确率有所提高;为了减少运行状态评估中的人为因素,消除伪特征的干扰,提出了基于成分分析和支持向量机的变压器运行状态评估模型,对比研究了PCA、KPCA、ICA和PCA+ICA共4种特征提取方式,说明了在运行状态评估问题中进行数据预处理有利于分类算法的实现,KPCA为变压器的运行状态评估提供了一种有效可行的数据预处理方式;为了进一步提高评估的正判率,获得更加稳定的评估效果,本文还提出了改进的KPCA+SVM评估模型,采用了混合核函数和并行优化策略,变压器的运行评估实例分析证明了改进算法的有效性。
The transformer is a major apparatus in the power system and the traction power supply system.Failure of a transformer will definitely harm the safety and stability of the power system and the electrified railway.Transformer condition assessment is the foundation of the transformer condition based maintenance.
Support vector machine(SVM) based on statistical learning theory(SLT) accomplishes the structural risk minimization principle,avoids being trapped into local minima and has fine performance to limited samples.It solves the difficulty that fault intelligent diagnosis system faces,which is terrible lack of typical fault samples,and is becoming gradually one of the powerful tools in intelligent diagnosis.
Although the application research of SVM in diagnosis has obtained certain achievement,how to perform the novel method effectively in transformer condition assessment is a great challenge.Therefore,the application of SVM to the transformer intelligent fault diagnosis,fault forecasting and running condition assessment are developed in this thesis.The main contributions of this dissertation are as follows:
1.The intelligent diagnosis of transformer based on gases dissolved in transformer oil is studied with the support vector classification.The method for conversion from two-class to multi-class is analyzed.Two kinds of multi-class algorithms are studied, i.e.the one-against-one method and the M-ary method.An improved M-ary SVM algorithm is proposed.The test results prove the effectiveness and superiority of the improved algorithm.Furthermore,the selection of kernel function parameters is discussed.The parameters are optimized using improved genetic algorithm.Compared with general genetic algorithm,the optimum can be found accurately in a wide range using the proposed method and the value can be used to diagnose the transformer effectively.
2.The prediction of gases dissolved in transformer oil is investigated using support vector regression.The least square support vector machine(LS-SVM) is introduced into the concentration prediction of dissolved gases.The adaptability of the LS-SVM model and the situation of the parameters varied with the data for building the model are analyzed.In order to get optimized grey model,the multi-variable grey model is recommended to the concentration prediction of dissolved gases in transformer oil,an improved discrete grey model and an improved multi-variable grey model with higher accuracy are proposed.Moreover,a combined forecasting model is put forward using the LS-SVM combination.In the combined model the advantages of the optimized grey model and the LS-SVM model are integrated.The effectiveness and superiority of the mentioned models have been verified with the results of the actual case.
3.The running condition assessment of transformer is realized with support vector machine.Aiming at the status that fuzzy synthetic evaluation is a main method for transformer running condition assessment,a fuzzy synthetic SVM model based on scores is proposed.The results of the fuzzy synthetic evaluation are the inputs of the SVM,the actual states of the transformer are the outputs.The examples show that,the running condition assessment for transformer using the new model is more accurate.In order to reduce the human factors,eliminate the misleading features and retain the genuine information,some condition assessment models for transformer based on component analysis and SVM are proposed.Four methods for extracting features are studied,viz.PCA,KPCA,ICA,and PCA+ICA.From the results it is concluded that the feature data after extracted lead to better separation,which suggests that data preprocessing in practical running condition assessment is favorable to the realization of classification algorithm,KPCA is an effectively feasible data preprocessing method for the transformer running condition assessment.In order to improve the accuracy and mend the effect of evaluation,an improved KPCA+SVM model is proposed.Mixtures of kernels and parallel optimized strategy are used.
引文
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