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Conventionally, the 2-parameter Weibull model, Arrhenius-Weibull model, has been used vastly for transformer aging-dependent unavailability modeling. However, this model only uses the lifetime feature to describe the transformer's degradation process and to calibrate the Weibull parameters, which harms the accuracy of aging-dependent unavailability forecasting. In response, this paper develops a 3-calibratable-parameter Weibull model for evaluating the transformer aging-dependent unavailability. In the proposed model, both the individualized aging threshold and lifetime are taken into the calibration of the Weibull parameters to accurately characterize the heterogeneity in transformer populations. First, a degree of polymerization (DP) analysis and Monte Carlo Simulation (MCS) based approach is proposed for estimating the transformers’ uncertain aging thresholds and lifetimes. Then, the Maximum Likelihood Estimate and Particle Swarm Optimization are jointly adopted to model the relationship among the calibratable Weibull parameters, aging threshold, and lifetime. Finally, the analytical formula of aging-dependent unavailability is derived from the established 3-calibratable-parameter Weibull model using an integral-discretization method. A real utility application example in China's Chongqing power system has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

The insulation paper loss of life (LOL) of a distribution transformer (DT) is largely determined by its winding hot-spot temperature. It is mainly estimated via the transformer load, ambient temperature, and related physical parameters. Fortunately, advanced metering infrastructure (AMI) can provide load profiles of DTs, allowing a cost-effective LOL estimation solution. However, the AMI dataset contains measurement errors that significantly reduce LOL estimation accuracy. A forecasting method is needed to recover the erroneous AMI data. This is a challenging problem, as the load profiles of DTs are nonstationary and volatile. We propose an ensemble of stacked long short-term memory (ES-LSTM) networks to simultaneously capture load consumption patterns of DTs and their correlations. The ES-LSTM consists of two independent LSTM networks and a feed-forward neural network (FFNN) stacked on top of them. A two-stage training procedure is applied. In the first stage, two LSTM networks are trained to capture the daily or weekly load patterns. In the second stage, the FFNN is trained to optimally combine the last hidden outputs of the two LSTM networks. Field verification is conducted using real AMI data obtained from an urban distribution utility in China. The test results confirmed the superiority of the proposed method. [ABSTRACT FROM AUTHOR]