变压器故障诊断与预测集成学习方法及维修决策模型研究
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摘要
变压器是电力系统的核心设备之一,其运行状态与电力系统的安全可靠运行具有紧密联系。随着超特高压输变电技术的发展以及电网规模的扩大,变压器故障对电力系统将会造成重大的危害。因此,有效诊断和预测变压器的潜伏性故障,制定科学合理的变压器维修策略,对提高电力系统的安全可靠性具有重要意义。本文在研究电力变压器故障模式分类基础上,对变压器故障特征量与故障模式的相关程度、变压器故障诊断与预测方法的泛化能力以及变压器维修决策中的故障风险评估方法等关键问题进行了深入研究。论文研究工作的主要内容包括:
①研究了以可信度量化表征故障特征量与故障模式的相关程度的方法,提出了分析变压器故障特征量可信度的相关规则分析方法,研究了提取变压器故障特征量与故障模式关联规则的Apriori算法,并对样本数据的多值离散化方法进行了研究,分析了样本数据多值离散化方法对关联规则分析的作用和影响。实例分析结果表明,经过多值离散化处理,关联规则分析具有量化分析变压器故障特征量与故障模式关系的能力。
②研究了以变压器故障特征量可信度作为先验知识的多特征量变压器故障诊断方法,提出了聚类算法与支持向量机结合的变压器故障诊断组合模型,分析了组合模型中的故障分类决策树的建立方法。实例表明,多特征量的变压器故障诊断,采用组合模型比单一的聚类或支持向量机的准确率更高。
③研究了变压器故障诊断集成学习方法,阐述了采用样本信息熵表征样本参考价值的方法,通过样本重采样建立了多个训练样本子集,提出了基于样本信息熵的Bagging(IE-Bagging)算法,用以集成单一故障诊断模型。实例分析结果表明,IE-Bagging提高了单一故障诊断模型的泛化能力和诊断准确性。
④研究了变压器故障特征量的预测模型的提高泛化能力的方法,分析了用结合样本熵和样本主观信息计算样本采样概率的方法,提出了基于综合样本熵的改进Bagging算法(E-Bagging),阐述了E-Bagging算法集成支持向量回归机,组合预测算法的过程,实例表明,E-Bagging集成算法能提高单一或组合预测模型的准确度和泛化能力。
⑤研究了基于贝叶斯信息标准(BIC)故障特征量预测模型评价方法,分析了评价预测模型的准确性和复杂性指标以及样本数量与复杂性因素的关系,比较不同样本条件下的集成算法的BIC评价指标。实例表明,大样本条件下,E-Bagging算法集成组合预测模型最优;而在小样本条件下, E-Bagging算法集成支持向量回归机预测模型最优。
⑥研究了变压器故障风险评估所需的故障后果评估和故障概率估计的方法,分析了故障情况下变压器自身损失、人身损失、电力系统损失和社会损失等四个货币损失因素,提出采用货币损失作为变压器故障后果的量化评估指标的方法;研究了采用信息熵作为先验知识表征样本数据分布情况并改进模糊聚类算法的目标函数的方法,提出了改进的变压器故障概率估计模糊聚类算法。实例表明,上述方法比由专家打分评估故障后果和威布尔仿真估计故障概率更准确。
⑦研究了建立基于风险评估的变压器维修决策模型的基本过程和基本功能单元,分析了用维修费用期望量化维修策略的方法,提出了以维修费用的数学期望最小为维修决策目标函数建立变压器维修决策模型的方法。最后,通过实例阐述了该维修决策模型的建立过程。
The transformer is one of the core equipments of the power system, which operating state has close contact with power system safe and reliable operation. With the development of UHV/EHV and the expansion of interconnected power system, the transformer faults will cause great damage and impact on the power system. Therefore, it is significant for improving the safety and reliability of power system to effectively predict and diagnose the potential faults of transformers and develop scientific and rational transformer maintenance strategy. In this dissertation, based on the classification of the main transform fault modes, the key issues are researched in depth including the relevance amount between transformer features and failure modes, the generalization ability of transformer fault prediction models and fault diagnosis models, and the failure impact assessment and the failure probability estimate required by the risk assessment for the transformer faults. The main research work in this dissertation includes:
①The method to quantify the relevance between the failure modes and the fault features of transformers is researched, and the association rules analysis method to obtain the confidence value of fault features of transformers is proposed. The Apriori algorithm for extracting the association rules between the failure modes and the fault features of transformers is studied. The multi-valued discretization method of sample data is researched. Then the role and influence of the multi-valued discretization method for the association rules analysis are analyzed. Example results show that, after the multi-valued discrete processing, association rules analysis has the capacity to quantify the relationship between the failure modes and the fault features of transformers.
②The faults diagnosis methods for transformer of multi-features by regarding the confidence values of the fault features of the transformer as a priori knowledge is studied. Then a transformer fault diagnosis model is proposed integrated the clustering analysis with the support vector machine. The method to construct the decision-making tree for the faults classification is analyzed. Examples show that, for the fault diagnosis of multi-features on the transforms, the accuracy of diagnosis based on the combination model is higher then the single clustering or support vector machine.
③The ensemble learning algorithm for fault diagnosis of transformers is researched. The method for representing the reference value of the samples by sample information entropy is described. A number of training samples subsets are constructed by the resampling process and the Information Entropy-Based Bagging algorithm is proposed for ensembling those single fault diagnosis models. Example results show that, IE-Bagging is able to improve the generalization ability and diagnostic accuracy of the single fault diagnosis models.
④Methods to improve the generalization ability of the feature prediction model for fault features are studied. Sample probability is calculated by combining the sample entropy with its subjective information; then, an improved Bagging algorithm (E-Bagging) is proposed based on comprehensive sample entropy. Moreover, E-Bagging integrated with both support vector regression machine and combinatorial forecasting algorithms are also presented. Results indicate that this E-Bagging integration strategy can improve accuracy and generalization ability of solo or combinatorial forecasting models.
⑤An evaluation method for fault features prediction model is proposed based on the Bayesian information criterion (BIC). Model accuracy and its complexity index as well as the relationship between sample number and complexity factors are then analyzed, followed by comparative study of BIC evaluation index for integrated algorithm in different sample scenarios. Experiments indicate that the E-Bagging integrated with combinatorial forecasting is the optimal model for large sample sets whereas the E-Bagging incorporated with SVRM outperforms other forecasting models for small sample sets.
⑥Aiming at risk evaluation of transformer faults, methods to estimate faults’consequence and probabilities are studied. The fault loss is classified into four kinds of monetary loss including transformer’s own damage, personnel loss, grid loss and social loss; total loss is used to evaluate the fault consequence quantitatively. Information entropy is introduced as the priori-knowledge to measure distributions of samples and objective function of fuzzy clustering is refined, then an improved fuzzy clustering algorithm for fault probability estimation is proposed accordingly. Examples show that the above methods for consequence and probability estimation can obtain more accurate results than that of expert score and Weibull distribution, respectively.
⑦Based on risk evaluation, fundamental procedure and functional units of a decision-making model for transformer maintenance are established. First, strategy of using expectation of maintenance expense to quantitatively guide the maintenance plan is analyzed; then, this expected maintenance expense is used as the objective function of decision-making to establish the optimal maintenance model. Finally, examples illustrate the detailed steps of model establishment.
①研究了以可信度量化表征故障特征量与故障模式的相关程度的方法,提出了分析变压器故障特征量可信度的相关规则分析方法,研究了提取变压器故障特征量与故障模式关联规则的Apriori算法,并对样本数据的多值离散化方法进行了研究,分析了样本数据多值离散化方法对关联规则分析的作用和影响。实例分析结果表明,经过多值离散化处理,关联规则分析具有量化分析变压器故障特征量与故障模式关系的能力。
②研究了以变压器故障特征量可信度作为先验知识的多特征量变压器故障诊断方法,提出了聚类算法与支持向量机结合的变压器故障诊断组合模型,分析了组合模型中的故障分类决策树的建立方法。实例表明,多特征量的变压器故障诊断,采用组合模型比单一的聚类或支持向量机的准确率更高。
③研究了变压器故障诊断集成学习方法,阐述了采用样本信息熵表征样本参考价值的方法,通过样本重采样建立了多个训练样本子集,提出了基于样本信息熵的Bagging(IE-Bagging)算法,用以集成单一故障诊断模型。实例分析结果表明,IE-Bagging提高了单一故障诊断模型的泛化能力和诊断准确性。
④研究了变压器故障特征量的预测模型的提高泛化能力的方法,分析了用结合样本熵和样本主观信息计算样本采样概率的方法,提出了基于综合样本熵的改进Bagging算法(E-Bagging),阐述了E-Bagging算法集成支持向量回归机,组合预测算法的过程,实例表明,E-Bagging集成算法能提高单一或组合预测模型的准确度和泛化能力。
⑤研究了基于贝叶斯信息标准(BIC)故障特征量预测模型评价方法,分析了评价预测模型的准确性和复杂性指标以及样本数量与复杂性因素的关系,比较不同样本条件下的集成算法的BIC评价指标。实例表明,大样本条件下,E-Bagging算法集成组合预测模型最优;而在小样本条件下, E-Bagging算法集成支持向量回归机预测模型最优。
⑥研究了变压器故障风险评估所需的故障后果评估和故障概率估计的方法,分析了故障情况下变压器自身损失、人身损失、电力系统损失和社会损失等四个货币损失因素,提出采用货币损失作为变压器故障后果的量化评估指标的方法;研究了采用信息熵作为先验知识表征样本数据分布情况并改进模糊聚类算法的目标函数的方法,提出了改进的变压器故障概率估计模糊聚类算法。实例表明,上述方法比由专家打分评估故障后果和威布尔仿真估计故障概率更准确。
⑦研究了建立基于风险评估的变压器维修决策模型的基本过程和基本功能单元,分析了用维修费用期望量化维修策略的方法,提出了以维修费用的数学期望最小为维修决策目标函数建立变压器维修决策模型的方法。最后,通过实例阐述了该维修决策模型的建立过程。
The transformer is one of the core equipments of the power system, which operating state has close contact with power system safe and reliable operation. With the development of UHV/EHV and the expansion of interconnected power system, the transformer faults will cause great damage and impact on the power system. Therefore, it is significant for improving the safety and reliability of power system to effectively predict and diagnose the potential faults of transformers and develop scientific and rational transformer maintenance strategy. In this dissertation, based on the classification of the main transform fault modes, the key issues are researched in depth including the relevance amount between transformer features and failure modes, the generalization ability of transformer fault prediction models and fault diagnosis models, and the failure impact assessment and the failure probability estimate required by the risk assessment for the transformer faults. The main research work in this dissertation includes:
①The method to quantify the relevance between the failure modes and the fault features of transformers is researched, and the association rules analysis method to obtain the confidence value of fault features of transformers is proposed. The Apriori algorithm for extracting the association rules between the failure modes and the fault features of transformers is studied. The multi-valued discretization method of sample data is researched. Then the role and influence of the multi-valued discretization method for the association rules analysis are analyzed. Example results show that, after the multi-valued discrete processing, association rules analysis has the capacity to quantify the relationship between the failure modes and the fault features of transformers.
②The faults diagnosis methods for transformer of multi-features by regarding the confidence values of the fault features of the transformer as a priori knowledge is studied. Then a transformer fault diagnosis model is proposed integrated the clustering analysis with the support vector machine. The method to construct the decision-making tree for the faults classification is analyzed. Examples show that, for the fault diagnosis of multi-features on the transforms, the accuracy of diagnosis based on the combination model is higher then the single clustering or support vector machine.
③The ensemble learning algorithm for fault diagnosis of transformers is researched. The method for representing the reference value of the samples by sample information entropy is described. A number of training samples subsets are constructed by the resampling process and the Information Entropy-Based Bagging algorithm is proposed for ensembling those single fault diagnosis models. Example results show that, IE-Bagging is able to improve the generalization ability and diagnostic accuracy of the single fault diagnosis models.
④Methods to improve the generalization ability of the feature prediction model for fault features are studied. Sample probability is calculated by combining the sample entropy with its subjective information; then, an improved Bagging algorithm (E-Bagging) is proposed based on comprehensive sample entropy. Moreover, E-Bagging integrated with both support vector regression machine and combinatorial forecasting algorithms are also presented. Results indicate that this E-Bagging integration strategy can improve accuracy and generalization ability of solo or combinatorial forecasting models.
⑤An evaluation method for fault features prediction model is proposed based on the Bayesian information criterion (BIC). Model accuracy and its complexity index as well as the relationship between sample number and complexity factors are then analyzed, followed by comparative study of BIC evaluation index for integrated algorithm in different sample scenarios. Experiments indicate that the E-Bagging integrated with combinatorial forecasting is the optimal model for large sample sets whereas the E-Bagging incorporated with SVRM outperforms other forecasting models for small sample sets.
⑥Aiming at risk evaluation of transformer faults, methods to estimate faults’consequence and probabilities are studied. The fault loss is classified into four kinds of monetary loss including transformer’s own damage, personnel loss, grid loss and social loss; total loss is used to evaluate the fault consequence quantitatively. Information entropy is introduced as the priori-knowledge to measure distributions of samples and objective function of fuzzy clustering is refined, then an improved fuzzy clustering algorithm for fault probability estimation is proposed accordingly. Examples show that the above methods for consequence and probability estimation can obtain more accurate results than that of expert score and Weibull distribution, respectively.
⑦Based on risk evaluation, fundamental procedure and functional units of a decision-making model for transformer maintenance are established. First, strategy of using expectation of maintenance expense to quantitatively guide the maintenance plan is analyzed; then, this expected maintenance expense is used as the objective function of decision-making to establish the optimal maintenance model. Finally, examples illustrate the detailed steps of model establishment.
引文
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