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.

This paper proposes a novel repair rule set (RRS) for scheduling the power system infrastructure repair after the occurrence of extreme events. RRS is made up of multiple repair rules, each of them can be applied in arbitrary post-disaster failure scenarios to rank the repair actions by priority. A learning-to-rank technique called AdaRank is used to train the repair rules by combining the weak learners derived from the dynamic repair scheduling model. Then, RRS is constructed by iteratively clustering the training cases and retraining the repair rule for each cluster. Increasing the number of repair rules within RRS allows it to differentiate various types of failure scenarios, thereby improving its performance. Further combined with multi-label K nearest neighbor (ML-KNN) technique, RRS is able to schedule the repair without the full knowledge of real-time failure information, such as the estimated repair time. The results of case studies on IEEE-118 test systems show that the proposed method has a desirable performance compared to the exact mathematical optimization model. Moreover, it reduces the requirement for failure information while significantly improving the computational efficiency.

Evaluating the real-time failure rate of transformers can effectively guide the planning of mainte-nance and reduce their failure risk. This paper proposed a novel transformer failure rate model that considers the impact of maintenance based on daily oil chromatographic monitoring data mining. Firstly, to ensure the quality of the modeling data, an improved k-nearest neighbor (KNN) algorithm based on genetic algorithm (GA) is proposed to repair the missing monitoring data. The repaired data is then mapped to the equivalent state duration (ESD) by the M-BPNN proposed, which is used to modify the multistate Markov process of transformers so as to quantify the impact of maintenance on failure rate. Considering the changing characteristics of the dissolved gases’ content in the short period, the ESD is further merged in sequential periods to obtain the merged equivalent state duration (MESD). Finally, an analytical function of the transformer failure rate with respect to the MESD is obtained. Case studies on a typical substation demonstrate that the proposed approach has the ability to characterize the impact of maintenance and the actual failure rate, thereby improving the accuracy of the substation reliability assessment.

High-voltage circuit breakers (HVCBs) are important components for reliable operation of power systems. Due to irreversible degradation, effective estimation of the aging-dependent failure rates of HVCBs is crucial for fit-for-purpose maintenance strategies. Many data-intensive models previously proposed for the aging-dependent failure rate evaluation cannot be extended to CBs without sufficient aging-related condition monitoring data. This article proposes a hybrid knowledge-based and data-driven approach for estimating the heterogeneous aging-dependent failure rate of CBs using a well-established Evaluation-Forecasting-Individualization framework. First, the proposed approach employs a hybrid knowledge-based and data-driven method to evaluate the degradation index (DI) that quantifies the aging degrees of individual CB. Second, a Block Hankel Tensor Autoregressive Integrated Moving Average (BHT-ARIMA) based multiple sequence prediction model is developed to improve the DI forecasting accuracy by combining hidden structure information among correlated CBs. Finally, a DI-based relative degradation function is formulated and integrated into a 2-parameter Weibull distribution to evaluate the relative deterioration level of in-group individual CBs towards the entire group. Evaluation results for a 550 kV CB dataset from an actual power company (Chongqing grid) are used to demonstrate the effectiveness and efficiency of the proposed approach.

The installed capacity of renewable energy has continued to increase in recent years, causing excess power to be transmitted to neighboring provinces by DC transmission with wind and thermal power bundling. However, with increasing coal cost and decreasing investment benefit of thermal power, the economic benefit of thermal power investment has decreased. Therefore, this paper proposes a method that replaces the thermal power unit with renewable energy to assume the peak regulating function of the power system. First, a novel heat storage strategy for concentrating solar power (CSP) stations is established. On this basis, the CSP station is included in the wind-thermal bundling model to replace the peak regulating function of some thermal power units, and the planning method of wind-thermal-CSP bundling with an adjustable heat storage strategy is developed. By adjusting the heat storage strategy and comparing the economic parameters under the different strategies, the optimal ratio of the installation of different powers is obtained.