There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Artificial intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics and motor drives. Neural networks have created a new and advancing frontier in power electronics, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. This paper gives a comprehensive introduction and perspective of neural network applications in the intelligent control and estimation for power electronics and motor drives area. The principal topologies of neural networks that are currently most relevant for applications in power electronics have been reviewed including the detailed description of their properties. Both feedforward and feedback or recurrent architectures have been covered in the description. The application examples that are discussed in this paper include nonlinear function generation, delayless filtering and waveform processing, feedback signal processing of vector drive, space vector PWM of two-level and multilevel inverters, adaptive flux vector estimation, and some of their combination for vector-controlled ac drive. Additional selected applications in the literature are included in the references. From the current trend of the technology, it appears that neural networks will find widespread applications in power electronics and motor drives in future

Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective

In this paper, a detailed study on the model reference adaptive controller (MRAC) utilizing the reactive power is presented for the online estimation of rotor resistance to maintain proper flux orientation in an indirect vector controlled induction motor drive. Selection of reactive power as the functional candidate in the MRAC automatically makes the system immune to the variation of stator resistance. Moreover, the unique formation of the MRAC with the instantaneous and steady-state reactive power completely eliminates the requirement of any flux estimation in the process of computation. Thus, the method is less sensitive to integrator-related problems like drift and saturation (requiring no integration). This also makes the estimation at or near zero speed quite accurate. Adding flux estimators to the MRAC, a speed sensorless scheme is developed. Simulation and experimental results have been presented to confirm the effectiveness of the technique.

Model Reference Adaptive Controller-Based Rotor Resistance and Speed Estimation Techniques for Vector Controlled Induction Motor Drive Utilizing Reactive Power

This paper provides a comprehensive review and analyses on the state-of-the-art and future trends for high-power conductive on-board chargers (OBCs) for electric vehicles. To provide a global context, a summary of global charging standards and electric vehicle (EV) related trends are presented, which demonstrates momentum toward the OBCs with higher power rating. High-power OBCs are either unidirectional or bidirectional, and they have either an integrated or non-integrated system architecture. Non-integrated high-power OBCs are studied both from industry and academia, and the former are used to illustrate the current state of the art. The latter are classified on the basis of the converter design approach, studied for their principle of operation, and compared over power density, weight, efficiency, and other metrics. In addition to non-integrated OBCs, recent advancements in propulsion-machine integrated OBC solutions are also presented. Other integrated OBC techniques, such as system integration with the EV's auxiliary power module and wireless charging systems, are also discussed. Finally, future charging strategies and functionalities in charging infrastructures are addressed, and global OBC trends are summarized.

Global Trends in High-Power On-Board Chargers for Electric Vehicles

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

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

High-voltage direct-current (HVDC) based power distribution networks have become an attractive alternative to classical ac distribution networks in more electric aircrafts (MEAs). This paper proposes a power flow management system for an isolated multiport power converter (MPC), which includes a high-frequency transformer, that controls the power flow between three HVDC electric networks in MEAs. The operation of the MPC system and the control stability analysis are presented. The proposed power flow management is tested under normal operation mode and different fault conditions of the power generators. The experimental results demonstrate that the isolated MPC reduces the effect of a power generator failure to the other networks, thus enhancing the fault tolerance of the entire power distribution network of the aircraft.

Power Flow Management of Isolated Multiport Converter for More Electric Aircraft

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.

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

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.

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

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.

B. Karanayil

Papers

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

Power Flow Management of Isolated Multiport Converter for More Electric Aircraft

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

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

Performance Evaluation of Three-Phase Grid-Connected Photovoltaic Inverters Using Electrolytic or Polypropylene Film Capacitors