基于移相加占空比控制策略的三有源桥TAB(triple active bridge)DC-DC变换器具有效率高和软开关范围可扩展等优点,但其小信号建模过程复杂、闭环控制环路参数整定困难。针对该问题,提出1种TAB工作在移相加占空比控制下的全阶连续广义状...基于移相加占空比控制策略的三有源桥TAB(triple active bridge)DC-DC变换器具有效率高和软开关范围可扩展等优点,但其小信号建模过程复杂、闭环控制环路参数整定困难。针对该问题,提出1种TAB工作在移相加占空比控制下的全阶连续广义状态平均建模和PI控制器设计方法。首先,分析TAB的运行原理和Y型等效结构;然后,结合移相加占空比控制的特点和交流方波源等效方法,推导出TAB的广义状态空间平均模型;接着,在推得模型的基础上求得输入到输出的传递函数,设计出PI控制器参数。最后,结合数字仿真及样机实验验证了所提方法的正确性及有效性。展开更多
This paper presents the complete mathematical model and predicts the performance of switched reluctance generator with time average and small signal models. The complete mathematical model is developed in three stages...This paper presents the complete mathematical model and predicts the performance of switched reluctance generator with time average and small signal models. The complete mathematical model is developed in three stages. First, a switching model is developed based on quasi-linear inductance profile. Next, based on the switching behaviour, a time average model is obtained to measure the difference between the excitation and generation time in each switching cycle. Finally, to track control voltage and current wave shapes, a small signal model is designed. The effectiveness of the complete multilevel model combining electrical machine, power converter, load and control with programming language is demonstrated through simulations. A PI controller is used for controlling the voltage of the generator. The results presented show that the controller exhibits accurate tracking control of load voltage under different operating conditions. This demonstrates that the proposed model is able to perform an accurate control of the generated output voltage even in transient situations. The simulation is performed to choose the control parameters and study the performance of switched reluctance generator prior to its actual implementation. Initial experimental results are presented using NI-Data acquisition card to control the output power according to load requirements.展开更多
Conventional spouted beds have been extensively used in many real-life applications but are not suited for all types of materials, especially fine particles, which require internal devices to improve their motion in t...Conventional spouted beds have been extensively used in many real-life applications but are not suited for all types of materials, especially fine particles, which require internal devices to improve their motion in the spouted bed. However, unlike conventional spouted beds, there are almost no mechanistic or empirical models available for the design of spouted beds with internals. Given the availability of an extensive but not experimentally designed database, the main purpose of this study is to present an analysis of neural networks and empirical models in terms of their suitability to fit and predict average cycle times in conical spouted beds with and without draft tubes. The parameters investigated are particle size, density, contactor angle, gas inlet diameter, static bed height, and draft tube features. Although the amount of information is always a key factor when fitting models, the size of the database used in this study strongly affects the fitting performance of empirical models, whereas artificial neural networks are more influenced by how the data are scaled. Results of model verification show that both techniques are suitable for predicting average cycle times for data outside the range covered by the database.展开更多
文摘基于移相加占空比控制策略的三有源桥TAB(triple active bridge)DC-DC变换器具有效率高和软开关范围可扩展等优点,但其小信号建模过程复杂、闭环控制环路参数整定困难。针对该问题,提出1种TAB工作在移相加占空比控制下的全阶连续广义状态平均建模和PI控制器设计方法。首先,分析TAB的运行原理和Y型等效结构;然后,结合移相加占空比控制的特点和交流方波源等效方法,推导出TAB的广义状态空间平均模型;接着,在推得模型的基础上求得输入到输出的传递函数,设计出PI控制器参数。最后,结合数字仿真及样机实验验证了所提方法的正确性及有效性。
文摘This paper presents the complete mathematical model and predicts the performance of switched reluctance generator with time average and small signal models. The complete mathematical model is developed in three stages. First, a switching model is developed based on quasi-linear inductance profile. Next, based on the switching behaviour, a time average model is obtained to measure the difference between the excitation and generation time in each switching cycle. Finally, to track control voltage and current wave shapes, a small signal model is designed. The effectiveness of the complete multilevel model combining electrical machine, power converter, load and control with programming language is demonstrated through simulations. A PI controller is used for controlling the voltage of the generator. The results presented show that the controller exhibits accurate tracking control of load voltage under different operating conditions. This demonstrates that the proposed model is able to perform an accurate control of the generated output voltage even in transient situations. The simulation is performed to choose the control parameters and study the performance of switched reluctance generator prior to its actual implementation. Initial experimental results are presented using NI-Data acquisition card to control the output power according to load requirements.
文摘Conventional spouted beds have been extensively used in many real-life applications but are not suited for all types of materials, especially fine particles, which require internal devices to improve their motion in the spouted bed. However, unlike conventional spouted beds, there are almost no mechanistic or empirical models available for the design of spouted beds with internals. Given the availability of an extensive but not experimentally designed database, the main purpose of this study is to present an analysis of neural networks and empirical models in terms of their suitability to fit and predict average cycle times in conical spouted beds with and without draft tubes. The parameters investigated are particle size, density, contactor angle, gas inlet diameter, static bed height, and draft tube features. Although the amount of information is always a key factor when fitting models, the size of the database used in this study strongly affects the fitting performance of empirical models, whereas artificial neural networks are more influenced by how the data are scaled. Results of model verification show that both techniques are suitable for predicting average cycle times for data outside the range covered by the database.
