论文标题
使用计算智能对变压器衬套的条件监测
Condition Monitoring of Transformer Bushings Using Computational Intelligence
论文作者
论文摘要
溶解的油脂分析（DGA）用于监测大型电源变压器上衬套的状况。从收集的数据中确定条件有不同的技术，但是在这项工作中，研究了人工智能技术。这项工作调查了DGA中哪些气体彼此相关，哪些气体对于做出决策很重要。当确定相关气体和关键气体时，将其他气体丢弃，从而减少DGA中的属性数量。因此，进行了进一步的研究，以查看这些新数据集如何影响用于对完整属性的DGA进行分类的分类器的性能。这些实验中使用的分类器是反向传播神经网络（BPNN）和支持向量机（SVM），而主组件分析（PCA），粗糙集（RS），增量颗粒状排名（GR ++）和决策树（DT）用于减少数据集的属性。训练BPNN和SVM分类器时使用的参数在研究减少气体数量的效果时固定以创建受控的测试环境。这项工作进一步引入了一个新的分类器，该分类器可以处理高维数据集和嘈杂的数据集，粗神经网络（RNN）。
Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of bushings on large power transformers. There are different techniques used in determining the conditions from the data collected, but in this work the Artificial Intelligence techniques are investigated. This work investigates which gases in DGA are related to each other and which ones are important for making decisions. When the related and crucial gases are determined, the other gases are discarded thereby reducing the number of attributes in DGA. Hence a further investigation is done to see how these new datasets influence the performance of the classifiers used to classify the DGA of full attributes. The classifiers used in these experiments were Backpropagation Neural Networks (BPNN) and Support Vector Machines (SVM) whereas the Principal Component Analysis (PCA), Rough Set (RS), Incremental Granular Ranking (GR++) and Decision Trees (DT) were used to reduce the attributes of the dataset. The parameters used when training the BPNN and SVM classifiers are kept fixed to create a controlled test environment when investigating the effects of reducing the number of gases. This work further introduced a new classifier that can handle high dimension dataset and noisy dataset, Rough Neural Network (RNN).
