Baburaj Karanayil

Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive

This paper presents a new method of online estimation for the stator and rotor resistances of the induction motor for speed sensorless indirect vector controlled drives, using artificial neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The rotor speed is synthesized from the induction motor state equations. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural network in a vector controlled induction motor drive. Data on tracking performances of these estimators are presented. With this speed sensorless approach, the rotor resistance estimation was made insensitive to the stator resistance variations both in simulation and experiment. The accuracy of the estimated speed achieved experimentally, without the speed sensor clearly demonstrates the reliable and high-performance operation of the drive

Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks

This paper presents a new observer for the rotor resistance of an indirect vector controlled induction motor drive using artificial neural networks supplemented by a fuzzy logic based stator resistance observer. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. The error between the measured stator current and its corresponding estimated value is mapped to a change in stator resistance with a proposed fuzzy logic. The stator resistance observed with this approach is used to correct the rotor resistance observer using neural networks. The performance of these observers and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations. Both modeling and experimental data on tracking performances of these observers are presented. With this approach accurate rotor resistance estimation was achieved and was made insensitive to stator resistance variations both in modeling and experiment.

PI and fuzzy estimators for on-line tracking of rotor resistance of indirect vector controlled induction motor drive

This paper presents two methods of estimation of the rotor resistance in the indirect vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed using a PI controller and a fuzzy logic. The performance of both estimators and torque and flux responses of the drive are investigated with simulations for variations in the rotor resistance values from their nominal values. When either of the estimators are added to the drive system, the drive system performance does not deteriorate with the variation of the rotor resistance. The two estimation algorithms are designed, one with a PI controller and the other with fuzzy logic. The effectiveness of both algorithms is demonstrated with simulations.

Performance evaluation of three-phase grid-connected photovoltaic inverters using electrolytic or polypropylene film capacitors

In grid-connected photovoltaic (PV) power stations, improving the life expectancy and long-term reliability of three-phase PV inverters is urgently needed to match the significantly higher lifetime of the PV modules. A key contribution toward such improvement is replacing the conventional electrolytic film capacitors by metallized polypropylene film ones. This paper presents a detailed evaluation of a conventional three-phase grid-connected PV inverter performance when replacing the electrolytic capacitor with a minimum value of metallized polypropylene film capacitor. Although the minimum dc bus capacitance leads to higher voltage ripples, such ripples were found to be within acceptable limits to operate the inverter satisfactorily. Simulation and experimental results are presented for a 5-kW grid-connected inverter prototype with a nominal dc voltage of 457 V to confirm the theoretical considerations.

Rotor resistance identification using artificial neural networks for an indirect vector controlled induction motor drive

This paper presents a new method of estimation for the rotor resistance of the induction motor in the indirect vector controlled drive. The back propagation neural network technique is used for the real time adaptive estimation. The error between the desired state variable of an induction motor and the actual state variable of a neural model is back propagated to adjust the weights of the neural model, so that the actual state variable tracks the desired value. The performance of the neural estimator and torque and flux responses of the drive, together with this estimator, are investigated with simulations for variations in the rotor resistance from their nominal values.

Resonant Versus Conventional Controllers in Grid-Connected Photovoltaic Power Plants Under Unbalanced Grid Voltages

This paper discusses the control of large-scale grid-connected photovoltaic power plant (GCPPP) operating under unbalanced grid voltages. The positive and negative sequences of the grid currents need to be controlled to regulate the power injected into the grid during unbalanced grid voltages. This paper shows that the use of conventional proportional-integral-based controllers compromises stability and dynamic performance of the inverter. The reason is the delays introduced by the filters needed to extract the sequences of the transformed grid currents. Because of such delays, there is a strong restriction on choosing the parameters for the current and voltage controllers, which forces the GCPPP to perform slowly. This can be improved by using resonant controllers instead, which avoid the need for filtering the transformed grid currents. Additionally, a new overcurrent protection is proposed for the GCPPP when it is providing grid voltage support during voltage sags. Simulation and experimental results are presented to evaluate and compare the performance of the GCPPP when operating with the different controllers.

Evaluation of DC-link decoupling using electrolytic or polypropylene film capacitors in three-phase grid-connected photovoltaic inverters

The life expectancy and long term reliability of grid-connected three-phase photovoltaic (PV) inverters can be increased by replacing the conventional electrolytic film capacitors by metallized polypropylene film capacitors. This paper presents a detailed evaluation of a three-phase grid-connected PV inverter performance when replacing the electrolytic capacitor with a minimum value of metallized polypropylene film capacitor-one. The minimum dc bus capacitance leads to larger voltage ripples. However, such ripples were found to be within acceptable limits to run the inverter satisfactorily. Simulation results are presented for a 15-kW grid-connected inverter at nominal voltage of 700V dc and experimental results are provided for a 3.0-kW system at a nominal voltage of 400V dc, built in the laboratory.

Bi-directional isolated multi-port power converter for aircraft HVDC network power transfer

High-voltage direct-current (HVDC) based power distribution networks are an attractive alternative to classical AC distribution networks in more electric aircrafts (MEAs). This paper presents a bi-directional high-frequency transformer isolated multi-port power converter that controls the power flow between three HVDC aircraft networks. Power converter analysis and control laws are provided. Theoretical analyses are verified by extensive simulations. Finally, the converter fault tolerance is tested. Simulation results prove that the transformer isolation reduces the effect of a faulted network to the other networks thus enhancing the fault tolerance of the entire power distribution network.

An implementation of a programmable cascaded low-pass filter for a rotor flux synthesizer for an induction motor drive

Digital implementation of integrators for the estimation of the rotor flux of an induction motor from the stator voltages and stator currents, poses problems associated with the offset in the sensor amplifiers. Traditional low-pass filters can replace an integrator, however the poor dynamic response of the estimator calls for a programmable cascaded low-pass filter (PCLPF) implemented in software. The use of a cascaded low pass filter for stator flux synthesis has previously been applied to stator flux oriented vector controlled drives. In this paper, the performance of such an estimator is investigated with simulation studies and experimental results, implemented for the rotor resistance identification of a rotor flux oriented induction motor drive. The three stage programmable cascaded low-pass filters discussed in this paper have resulted in excellent steady-state and transient responses, such that they are successfully used for the on-fine rotor resistance identification with PI/fuzzy estimators and using artificial neural networks.

Investigation of an on-line rotor resistance identification with a new stator resistance observer for induction motor drive using artificial neural networks

This paper presents a new method of on-line estimation for the stator and rotor resistances of the induction motor in the indirect vector controlled drive, using artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and the actual state variable of a neural model is back propagated to adjust the weights of the neural model, so that the actual state variable tracks the desired value. The performance of the neural estimator and torque and flux responses of the drive, together with this estimator, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both these resistances are estimated experimentally, in a vector controlled induction motor drive and found to give accurate estimates. The rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.