In this work we present two new models based on Evolving Multivariable Gaussian approach and on the Set-Membership/Enhanced Set-Membership adaptive filtering framework to model the thermal behavior of power transformers. In these new models, adaptive filtering approaches work to adjust the learning rates of the evolutionary model, while the use of multivariable Gaussian membership functions, instead of single-variable as evolving models in general, makes information about interactions between input variables preserved and used in the training process. In addition, the evolving structure of the proposed models make these models more adaptable to changes in the operational conditions of power transformers (like insulation aging, environmental changes, load profile changes, among others) than its non-evolving counterparts. To evaluate the performance of the proposed models they were applied to two benchmark problems and to the thermal modeling of real power transformers problem under two load conditions: with and without an overload condition. The obtained results are compared with the performance of the original evolving Multivariable Gaussian and with other classical models suggested in the literature. Both proposed models obtained significantly higher performances than all the other tested models, suggesting that the models are flexible and efficient approaches in these scenarios representing a promising approach in the modeling of power transformers, especially for real-time applications.

在这项工作中，我们提出了两个基于演化多变量高斯方法和集成员资格/集成员资格增强自适应滤波框架的新模型，以对电力变压器的热行为进行建模。在这些新模型中，自适应过滤方法可用于调整进化模型的学习率，而使用多变量高斯隶属函数代替一般的单变量作为演化模型，则可以保留和使用输入变量之间的交互信息。培训过程。此外，所提出的模型的演化结构使这些模型比非演化模型更能适应电力变压器运行条件的变化（例如绝缘老化，环境变化，负荷曲线变化等）。为了评估所提出模型的性能，将它们应用于两个基准问题，以及在两个负载条件下（有和没有过载条件下）的有功功率变压器的热模型问题。将获得的结果与原始演化的多变量高斯模型的性能以及文献中建议的其他经典模型进行比较。两种提议的模型均比所有其他测试模型获得了显着更高的性能，这表明这些模型在这些情况下是灵活而有效的方法，代表了电力变压器建模中的一种有希望的方法，特别是对于实时应用。有无过载情况。将获得的结果与原始演化的多变量高斯模型的性能以及文献中建议的其他经典模型进行比较。两种提议的模型均比所有其他测试模型获得了显着更高的性能，这表明这些模型在这些情况下是灵活而有效的方法，代表了电力变压器建模中的一种有希望的方法，特别是对于实时应用。有无过载情况。将获得的结果与原始演化的多变量高斯模型的性能以及文献中建议的其他经典模型进行比较。两种提议的模型均比所有其他测试模型获得了显着更高的性能，这表明这些模型在这些情况下是灵活而有效的方法，代表了电力变压器建模中的一种有希望的方法，特别是对于实时应用。