4 research outputs found
DSSRM Design With Multiple Pole Arcs Optimization for High Torque and Low Torque Ripple Applications
No full textMMC-Based SRM Drives with Decentralized Battery Energy Storage System for Hybrid Electric Vehicles
No full textThis paper proposes a modular multilevel converter (MMC) based switched reluctance motor (SRM) drive with decentralized battery energy storage system for hybrid electric vehicle applications. In the proposed drive, a battery cell and a half-bridge converter is connected as a submodule (SM), and multiple SMs are connected together for the MMC. The modular full-bridge converter is employed to drive the motor. Flexible charging and discharging functions for each SM are obtained by controlling switches in SMs. Multiple working modes and functions are achieved. Compared to conventional and existing SRM drives, there are several advantages for the proposed topology. A lower dc-bus voltage can be flexibly achieved by selecting SM operation states, which can dramatically reduce the voltage stress on the switches. Multilevel phase voltage is obtained to improve the torque capability. Battery state-of-charge balance can be achieved by independently controlling each SM. Flexible fault-tolerance ability for battery cells is equipped. The battery can be flexibly charged under both running and standstill conditions. Furthermore, a completely modular structure is achieved by using standard half-bridge modules, which is beneficial for market mass production. Experiments carried out on a three-phase 12/8 SRM confirm the effectiveness of the proposed SRM drive
A Universal Two-Sensor Current Detection Scheme for Current Control of Multiphase Switched Reluctance Motors with Multiphase Excitation
No full textIn this paper, a universal two-sensor current detection scheme is proposed for the current control of different multiphase switched reluctance motors (SRMs) under multiphase excitation. In the proposed scheme, only two current sensors are utilized to separate the phase currents by selecting the currents flowing through each sensor, and detect the excitation current flowing through the lower switch of each phase. The current overlapping states are analyzed for SRM drives with different phase numbers. Compared to existing schemes, the proposed method is applicable for all three-phase, four-phase, five-phase, and six-phase drives, without external circuits and any changes in converter topologies, which is much easier to implement. For three-phase and four-phase SRM drives, the phase currents can be simply calculated from the two sensor currents, without pulse injection and voltage penalty, which improves the sampling accuracy and system performance. Furthermore, with the proposed scheme, the five-phase and six-phase systems both can be made equivalent to two independent single-sensor systems, where all the phase currents can also be obtained by employing pulse injection. Therefore, it provides a promising solution to phase current detection for different SRMs with multiphase excitation by using only two current sensors. Simulation and experimental results are provided to verify the proposed schemes
Experiments and modelling of phase fraction in gas-liquid two-phase flow using a microwave resonant cavity sensor
No full textGas-liquid two-phase flow is prevalent in the natural gas industry, and accurate phase fraction measurement is crucial for enhancing productivity and energy efficiency in industrial processes. However, achieving high-precision, in-situ measurement remains challenging. To address this issue, this study proposes novel prediction models based on the microwave cylindrical resonant cavity (MCRC) sensor. Firstly, the MCRC sensor was implemented, and the experiments were conducted by incorporating a quick-closing valve calibration system into an existing gas-water reference system, capturing a multi-parameter dataset. The analysis indicated that a complex nonlinear relationship existed among phase fraction, relative frequency shift, pressure, and superficial gas velocity. Then, phase fraction prediction models, including void fraction and gas volume fraction (GVF) model, were developed using the empirical and machine learning modelling methods. The results revealed that empirical models without intermediate dielectric constant complex calculation achieved relative errors within ±5 %. Among the 5 machine learning models compared, the XGBoost model performed the best, with over 95 % of data points within ±2 %. Additionally, extended experiments were used to estimate the generalization ability of the GVF prediction models, demonstrating excellent performance. Finally, the comparative error analysis confirmed the superior accuracy of the proposed models. The findings suggest that the proposed models offer notable improvements in prediction accuracy and practical applicability, making them promising methods for phase fraction prediction in gas-liquid flow using the MCRC sensor in the natural gas industry
