Stator flux oriented vector-controlled induction motor drive with space vector PWM and flux vector synthesis by neural networks

A stator flux oriented vector-controlled induction motor drive is described where the space vector PWM (SVM) and stator flux vector estimation are implemented by artificial neural networks (ANN). Artificial neural networks, when implemented by dedicated hardware ASIC chips, provide extreme simplification and fast execution for control and feedback signal processing functions in high performance ac drives. In the proposed project, a feedforward ANN-based SVM, operating at 20 kHz sampling frequency, generates symmetrical PWM pulses in both undermodulation and overmodulation regions covering the range from dc (zero frequency) up to square-wave mode at 60 Hz. In addition, a programmable cascaded low-pass filter (PCLPF), that permits dc offset-free stator flux vector synthesis at very low frequency using the voltage model, has been implemented by a hybrid neural network which consists of a recurrent neural network (RNN) and a feedforward neural network (FFANN). The RNN-FFANN based flux estimation is simple, permits faster implementation, and gives superior transient performance when compared with a standard DSP-based PCLPF. A 5-hp open loop volts/Hz. controlled drive incorporating the proposed ANN-based SVM and RNN-FFANN based flux estimator was initially evaluated in the frequency range of 1.0 Hz to 58 Hz to validate the performances of SVM and flux estimator. Next, the complete 5-hp drive with stator flux oriented vector control was evaluated extensively using the PWM modulator and flux estimator. The drive performances in both volts/Hz control and vector control were found to be excellent.