非等间隔GM(1,1)幂模型在变压器故障气体预测中的应用

电力变压器运行的安全可靠性对于电网稳定有着关键影响。以油浸式变压器为例，考虑到变压器故障气体监测中存在的采集技术局限与完备性差的现状，对IEC三比值法所需要的五种主要故障特征气体溶解度大小进行预测，为后续的故障诊断提供数据分析基础。针对变压器故障气体色谱分析中气体浓度数据采集的不完备性与小样本特征，引入非等间隔GM(1,1)幂模型，并基于遗传算法对背景值及幂指数进行协同优化，分别建立变压器内不同种气体的气体溶解度灰色预测模型。实验证明:相较现有常见基于灰色模型的变压器预测方法，例如基于GM(1,1)模型与Verhulst模型的方法，所提方法能有效地提高模拟精度及预测精度，而且模型不拘泥于基础数据的等间隔连贯性，具有较好的实用性及适应性。

Application of unequal interval GM (1,1) power model in prediction of dissolved gases for power transformer failure

The safe and reliable operation of transformer plays a crucial role in power system. In case of the oil-immersed transformer, considering the fault characteristics and data monitoring situation of transformer, the solubility of five main fault characteristic gases, which are critical elements in IEC three ratio codes, is predicted to provide the basis for data analysis for the subsequent fault diagnosis. For incomplete and small sample characteristics of gas concentration data acquisition in gas chromatographic analysis of transformer faults, the unequal interval GM (1,1) power prediction models of gas solubility of different gases are established respectively. The coordinated optimization for background value and power exponent based on genetic algorithm is applied. According to the experiment, the proposed method can effectively improve the fitting and prediction accuracy, and the model has no limit to the equidistant coherence of the basic data, with better practicality and adaptability, when compared to some existing in the literature, such as the GM (1,1) model and Verhulst model. This work is supported by Sino-US International Science and Technology Cooperation Project (No. 2016YFE0105300).