Research on the improvement of stator current reconstruction quality in CS-FTC drive with on-line estimation of rotor and stator winding resistance in the induction motor drive

Research results concern an active current sensor (CS) fault-tolerant control (FTC) strategy for induction motor (IM) drive with adaptation of rotor and stator resistances. The stator current estimator with online adaptation of resistance parameters was applied for the reconstruction of missing current signals. A model reference adaptive system (MRAS), based on a neural network (NN), was used to estimate the rotor resistance. Additionally, stator resistance estimation was applied based on ratio index. The use of such a solution allowed for a significant increase in the quality of stator current reconstruction, which is particularly important for the design of CS fault detection (FD) and compensation algorithms. A wide range of simulation studies have been carried out for different operating conditions of the IM drive. The results showed that applying rotor and stator resistance estimation can improve the quality of stator current estimation by up to approximately 95% under rated operating point. The study was carried out for nominal and low speeds, with two, one, and without healthy CSs.The files are saved in the form:1. const - tests for constant speeds and loads;2. var - testing for speed and load changes;3. mea - measured, VCS - estimated, c - used in the control structure;4. is_A, is_B - phase currents;5. is_alfa, is_beta - currents in the alpha and beta axes;6. mo_ref - reference load;7. wm_ref - reference speed;8. psir_ref - reference rotor flux amplitude;9. rr - rotor resistance, rs - stator resistance;10. IM - in an induction motor, e - estimated;file name:trans - transient state (2) - load changetrans_sp - transient state (2) - speed changethe index "r" means that the estimated resistances were used in the control systemsp10, sp100 - reference speeds in (1) - 10% or 100%tq25, tq75 - reference loads in (1) - 25% or 75%the index "r" means that the estimated resistances were used in the control system本研究围绕考虑转子与定子电阻自适应的感应电机（induction motor, IM）驱动系统有源电流传感器（current sensor, CS）容错控制（fault-tolerant control, FTC）策略展开。研究采用具备电阻参数在线自适应能力的定子电流观测器对缺失的电流信号进行重构；基于神经网络（neural network, NN）的模型参考自适应系统（model reference adaptive system, MRAS）用于转子电阻估计，同时结合比率指标实现定子电阻估计。该方案可显著提升定子电流重构质量，这对于电流传感器故障检测（fault detection, FD）与补偿算法的设计尤为关键。 针对感应电机驱动系统的多种运行工况开展了大量仿真研究，结果显示：在额定运行点下，采用转子与定子电阻自适应估计的方案可将定子电流估计质量提升最高约95%。本次研究覆盖额定转速与低速工况，测试场景包含2个健康电流传感器、1个健康电流传感器以及无健康电流传感器三类。 数据集文件存储格式说明如下： 1. const：恒速恒负载测试场景； 2. var：变速变负载测试场景； 3. mea：实测数据，VCS：估计电流数据，c：用于控制结构的信号； 4. is_A、is_B：A相、B相电流； 5. is_alfa、is_beta：α轴与β轴电流； 6. mo_ref：参考负载转矩； 7. wm_ref：参考转速； 8. psir_ref：参考转子磁链幅值； 9. rr：转子电阻，rs：定子电阻； 10. IM：感应电机相关数据，e：估计值。 文件名命名规则： - trans：暂态工况（2）——负载变化； - trans_sp：暂态工况（2）——转速变化； 后缀索引"r"表示该文件对应的控制系统中采用了估计得到的电阻参数； p10、p100：参考转速，分别为额定值的10%与100%； tq25、tq75：参考负载转矩，分别为额定值的25%与75%； 后缀索引"r"表示该文件对应的控制系统中采用了估计得到的电阻参数。