Artificial intelligence of things (AIoT)-enabled intelligent automatic train operation (iATO) is an urgently needed technology to expand the capability of ATO in addressing the real-time responsiveness and dynamic online challenges to energy-efficient train trajectory optimization (TTO) and its associated ride-comfort, punctuality, and safety issues in modern urban rail transit networks. This paper proposes a three-step supervised reinforcement learning-based intelligent energy-efficient train trajectory optimization (SRL-IETTO) approach for iATO by hybrid-integrating deep reinforcement learning (DRL) and supervised learning. First, multiple objectives are formulated based on real-time train operation and systematically integrated into the RL algorithm by a binary function-based goal-directed reward design method. Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. Finally, numerical simulations are implemented to validate the effectiveness of the SRL-IETTO using in-service subway line data. The results demonstrate the superiority and improved energy saving of the proposed approach, and confirm its adaptability to online trip time adjustments within the practical running time range under uncertain disturbances with less trip time error compared to other intelligent TTO algorithms.

Crustal movement and land subsidence will cause tower foundation settlement, which seriously affects the safety of transmission line operation. This paper proposed a short-term failure warning method for transmission tower under land subsidence condition based on finite element method, strain monitoring and time series prediction. By simulating the stress distribution of the tower in actual foundation settlement, the weak components of the settlement tower were founded. Combined with the finite element simulation results in various conditions, the reliability of simulation results is verified, and the strain variation range of the corresponding measuring point is obtained. By installing the real-time strain monitoring system and establishing the autoregressive integrated moving average (ARIMA) time series forecast model, warning of the failure of the tower can be sounded in short time according to the strain monitoring and forecasting results. The field results show that the tower is in good condition and with no risk in short term. The method proposed by this paper is economical and easy to operate, which can be used to conduct the emergency treatment of the dangerous tower under land subsidence condition.

The resistivity of oil impregnated paper will decrease during its aging process. This paper takes paper resistivity as an assessment index to evaluate the insulation condition of oil impregnated paper in power transformer. The feasibility of this method are discussed in two aspects: reliability and sensitivity. Iterative inversion of paper resistivity was combined with finite element simulation. Both the bisection method and Newton’s method were used as iterative methods. After the analysis and comparison, Newton’s method was selected as the first option of paper resistivity iteration for its faster convergence. In order to consider the spatial distribution characteristic of paper aging and enhance the calculation accuracy, the resistivity calculation is expanded to a multivariate iteration based on Newton’s method, in order to consider the spatial distribution characteristic of paper aging and improve the calculation accuracy. This paper presents an exploratory research on condition assessment of oil impregnated paper insulation, and provides some reference to the security and economy operation of power transformers.

The fully insulated busbar has been extensively used in power and shipboard applications due to its favorable economic efficiency and excellent performance. Because of contact resistance and larger insulation thermal resistance, the joint, in certain circumstances, forms a hotspot in the circuit. For hotspot temperature monitoring, therefore, this paper proposed an indirect approach which consists of radial direction temperature calculation (RDTC) in the busbar and axial direction temperature calculation (ADTC) in the conductor. RDTC is to calculate the conductor temperatures from the surface temperatures of busbar in two different spots near the joint through transient thermal network. ADTC is to estimate the hotspot temperature from the conductor temperatures obtained in RDTC using a fitted functional expression. The influence of solar radiation on the temperature distribution is greatly reduced by averaging the surface temperature in RDTC and adjusting the functional form in ADTC. A temperature-rise test on a practical insulated busbar taped joint was performed outdoors in hot summer to validate this approach. The calculated hotspot temperature agrees well with the measured result with maximum error of only 3.8 K, indicating the high model precision and strong robustness to the solar radiation impact. Some factors in the application of temperature monitoring are also discussed. This method is expected to be applied to the engineering application, thus improving the condition monitoring of the fully insulated busbar.

Passenger flow fluctuation and delay-induced traffic regulation bring considerable challenges to cost-efficient regenerative braking energy utilization of hybrid electric storage systems (HESSs) in urban rail traction networks. This paper proposes a synergistic HESS sizing and energy management optimization framework based on multi-task reinforcement learning (MTRL) for enhancing the economic operation of HESSs under dynamic spatio-temporal urban rail traffic. The configuration-specific HESS control problem under various spatio-temporal traction load distributions is formulated as a multi-task Markov decision process (MTMDP), and an iterative sizing optimization approach considering daily service patterns is devised to minimize the HESS life cycle cost (LCC). Then, a dynamic traffic model composed of a Copula-based passenger flow generation method and a real-time timetable rescheduling algorithm incorporating a traction energy-passenger-time sensitivity matrix is developed to characterize multi-train traction load uncertainty. Furthermore, an MTRL algorithm based on a dueling double deep Q network with knowledge transfer is proposed to simultaneously learn a generalized control policy from annealing task-specific agents and operation environments for solving the MTMDP effectively. Comparative studies based on a real-world subway have validated the effectiveness of the proposed framework for LCC reduction of HESS operation under urban rail traffic