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).
翻译:气体溶解在石油中的气体分析(DGA)用于监测大型变压器的灌木情况。根据收集的数据,在确定条件时使用不同的技术,但人工智能技术受到调查。这项工作调查了DGA中哪些气体彼此相关,哪些气体对决策很重要。当相关和关键气体确定后,其他气体被丢弃,从而减少DGA中的属性。因此,进一步调查了这些新的数据集如何影响用于对全属性DGA进行分类的分类员的性能。这些实验中使用的分类员是反向推进神经网络(BPNNN)和支助矢量机器(SVM),而主要成分分析(PCA)、粗制、增压格拉纳定级(GN+)和决定树(DT)被用来减少数据集的属性。培训BPNNN和S分类员时使用的参数被固定下来,以便在调查减少气体数量时创造受控测试的环境。这项工作采用新的数据分类,可以进一步处理高压网络。