This paper proposes an efficient DC voltage control scheme in rail transit traction power systems with reversible converters for DC voltage regulation and power sharing. The proposed DC voltage control scheme is executed in a hierarchical structure based on the optimal power flow. Unlike conversional constant DC voltage control applied in traction power systems, this scheme allows the reversible converters operating in both rectifier and inverter regions and considers regenerative braking power sharing amongst multiple traction substations. The steady- state equivalent models of traction power systems including reversible converters are established, and the AC/DC sequential power flow algorithm based on Newton–Raphson method is presented. Finally, a multi-trains dynamic operation simulation is carried out with a subway line. The results prove the validity of proposed scheme and indicate that the system energy saving effect in traction power systems with reversible converters is obvious with proposed scheme.

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods.

This article proposes a condition monitoring scheme for the dc-link capacitors used in ac/dc/ac pulsewidth-modulation converters based on the signal injection, which can estimate capacitance in real time when both the rectifier and the inverter are in normal operation. First, a low-frequency signal is injected into the dc-link voltage, and its effects on the total harmonic distortion (THD) of the input current are analyzed. Accordingly, a suitable injection frequency is selected to keep the THD of input current within limits prescribed by the IEEE standard. Then, a novel calculation method of capacitor current without any dc-link current sensor is given, where the converter system is still in normal operation and different inverter operations are both taken into consideration. Finally, capacitance estimation based on the slide discrete Fourier transform is introduced. At the end of this article, experiments have been done to validate the correctness of the conclusion and effectiveness of the proposed scheme.

The modeling of stator and rotor faults is the basis of the development of online monitoring techniques. To obtain reliable stator and rotor fault models, this paper focuses on dynamic modeling of the stator and rotor faults in real-time, which adopts a multiple-coupled-circuit method by using a winding function approach for inductance calculation. Firstly, the model of the induction machine with a healthy cage is introduced, where a rotor mesh that consists of a few rotor loops and an end ring loop is considered. Then, the stator inter-turn fault model is presented by adding an extra branch with short circuit resistance on the fault part of a stator phase winding. The broken rotor bar fault is then detailed by merging and removing the broken-bar-related loops. Finally, the discrete models under healthy and faulty conditions are developed by using the Tustin transformation for digital implementation. Moreover, the stator and rotor mutual inductances are derived as a function of the rotor position according to the turn and winding functions distribution. Simulations and experiments are performed on a 2.2-kW/380-V/50-Hz three-phase and four-pole induction motor to show the performance of the stator and rotor faults, where the saturation effect is considered in simulations by exploiting the measurements of a no load test. The simulation results are in close agreement with the experimental results. Furthermore, magnitudes of the characteristic frequencies of 2f₁ in torque and (1 ± 2s)f₁ in current are analyzed to evaluate the stator and rotor fault severity. Both indicate that the stator fault severity is related to the short circuit resistance. Further, the number of shorted turns and the number of continuous broken bars determines the rotor fault severity.

This paper proposes a potential distributed reactive power compensation method in dc traction power systems with reversible substations. It utilizes the entire capacity of reversible substations during night hours and the reminding capacity except for regenerative braking energy feedback during day hours. A unified dispatch scheme is developed to allocate reactive power compensation capacity for each reversible substation. During night hours the entire capacity is used to accomplish the optimal power flow objective. During day hours the excess capacity after active power production is used to improve the system reactive power factor dynamically. The different objectives throughout the day are implemented in a unified formula which avoids manually change. In addition the outage of reversible substations and the low dc voltage conditions are considered and an over-modulation suppression strategy is included in the unified dispatch scheme to adjust the reactive power allocation among reversible substations. This novel utilization method of reversible substations is able to provide significant improvements in the power factor of dc traction power systems which would otherwise require expensive additional equipment such as static synchronous compensator. Finally the effectiveness of the proposed method is verified with MATLAB/SIMULINK.