Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.

瞬态信号是产生它们的潜在现象的特征，这使得它们的分析在许多领域都非常有用。瞬态是两个稳态状态之间的突然变化，会持续很短的时间，并且会随着时间的流逝而衰减。因此，叠加阻尼正弦曲线（SDS）被广泛用于瞬态建模，因为它们足以描述衰减现象。但是，SDS不适用于对电器的开启瞬态电流进行建模，因为它倾向于衰减到与之前的状态不同的稳态。在本文中，我们针对这些信号提出了一种新的且更合适的模型，以用于表征设备。我们还提出了一种用于模型参数估计的算法，并验证了其在模拟和真实数据上的性能。而且，我们提供了一个示例，说明如何使用模型参数作为使用受控的开/关负荷库（COOLL）数据集对设备进行分类的功能。结果表明，所提出的算法是有效的，并且对于真实数据，必须估计网络基本频率以解决其在标称值附近的变化。最后，实际数据实验表明，用作特征的模型参数产生了98％的分类精度。