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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]

This paper proposes a load transfer optimization (LTO) model involving hot-spot temperature (HST) and top-oil temperature (TOT) limits of transformers. This work aims to address the overload problem and transformer HST and TOT excursions via LTO. First, the equivalent thermal model for the HST and TOT of transformers under external cooling conditions are discussed, and then the corresponding linearized HST and TOT constraints with varied thermal resistances are derived using the discretization method. Moreover, the proposed LTO model is formulated on the reconfigurable topology of high voltage distribution networks (HVDNs) in China. This proposed LTO model can achieve the optimal network reconfiguration scheme subject to temperature limits and system operation constraints, with the least switching operation cost and the maximum margins of reactive power exchange between HVDNs and transmission systems. Finally, the effectiveness of the proposed LTO model is validated by the modified IEEE-14 node system and the practical HVDNs in China. [ABSTRACT FROM AUTHOR]

Consumers’ transformers play an important role in power systems, and they are essential for operation reliability and commercial benefits. In the past, maintenance personnel had to spend plenty of time on examining consumers’ transformers one by one. Nowadays, with the wide deployment of power user electric energy data acquire system (PUEEDAS), informative metering data are becoming available, which can be utilized for further condition monitoring. Thus, this paper proposes a data-driven abnormal condition monitoring algorithm of data acquisition for consumers’ transformers, which could timely send abnormal condition alerts to operators and maintenance personnel. In the proposed algorithm, Spearman's rank correlation coefficient is utilized to show the degree of correlation among phase currents, and its t-Statistics is used to determine whether abnormal condition of data acquisition exists based on the hypothesis testing. Finally, actual acquisition data from Zhejiang power system in China are employed to validate the effectiveness of the proposed algorithm, and to analyze the characteristics of normal and abnormal conditions, respectively. Sensitive analyses on different significant levels and sampling rates are performed for considering its impact on monitoring results; the application in real power systems is also given to demonstrate the practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]