Krishna Vasudevan

Bio: Krishna Vasudevan is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Harmonic & Torque. The author has an hindex of 10, co-authored 69 publications receiving 690 citations. Previous affiliations of Krishna Vasudevan include Indian Institutes of Technology.

Sensorless Brushless DC Motor Drive Based on the Zero-Crossing Detection of Back Electromotive Force (EMF) From the Line Voltage Difference

Abstract: This paper describes a position sensorless operation of permanent magnet brushless direct current (BLDC) motor. The position sensorless BLDC drive proposed, in this paper, is based on detection of back electromotive force (back EMF) zero crossing from the terminal voltages. The proposed method relies on a difference of line voltages measured at the terminals of the motor. It is shown, in the paper, that this difference of line voltages provides an amplified version of an appropriate back EMF at its zero crossings. The commutation signals are obtained without the motor neutral voltage. The effectiveness of the proposed method is demonstrated through simulation and experimental results.

Real-Time Estimation of Power System Frequency Using Nonlinear Least Squares

Abstract: This paper presents a nonlinear least squares method for measuring the power system frequency, wherein the voltage at the measurement point is modeled by using the Fourier series. The estimation of the fundamental frequency is a nonlinear problem in this formulation and is solved by performing a 1-D search over the range of allowed frequency variation. The voltage signal is used for frequency estimation because it is typically less distorted than the line current, resulting in computational efficiency. The robustness of this algorithm with respect to change in various parameters is studied through simulation and the results are validated by hardware implementation using a Virtex IV field-programmable gate array. An application of this algorithm to a shunt active power filter is also presented.

Online cage rotor fault detection using air-gap torque spectra

Abstract: This paper discusses use of air-gap torque spectra as a means of identifying faults in cage rotors. Being dependent on both stator and rotor currents, the torque is very sensitive to faults in the rotor. Through a comparative study using a detailed machine model and the standard dq model, the paper shows that the characteristic frequencies generated by a particular fault are preserved even if the standard dq model is used for estimation of air-gap torque. This is validated through a practical hardware implementation for online spectrum estimation of air-gap torque using TMS320C31, where several faulted cage rotors were used for study.

Simple position sensorless starting method for brushless DC motor

Abstract: Position sensorless methods for brushless DC motors based on back-EMF zero crossing suffer from a starting problem since there is no back-EMF at standstill. A simple method by which the motor is started from standstill up to a speed wherein sensorless methods will be able to detect the correct commutation instants is proposed. The proposed method relies on a difference of line voltages measured at the terminals of the motor. It is shown that this difference of line voltages provides an amplified version of an appropriate back-EMF at its zero crossings. It is further demonstrated that this information can be used to trigger devices so as to develop an accelerating torque from zero speed. This method is simple to implement and it can reliably start the motor even with load. The effectiveness of the proposed method is demonstrated through simulation and hardware results.

Indirect Back-EMF Zero Crossing Detection for Sensorless BLDC Motor Operation

Abstract: Brushless DC (BLDC) Motors are widely used in industrial and consumer appliances due to their high efficiency, high power density, low maintenance and silent operation However the need for position sensors makes the drive less reliable and expensive Sensorless control is therefore gaining importance now-a-days This paper proposes an indirect way of detecting the zero crossing instant of the back-EMF from the three terminal voltages without using the neutral potential Inaccuracies due to device drops have been eliminated This scheme is simple to implement Simulation results show the validity of this scheme

I and i

Abstract: 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 …

A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

Abstract: Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.

A survey of condition monitoring and protection methods for medium voltage induction motors

Abstract: Medium voltage induction motors are widely used in industry and are essential to industrial processes. The breakdown of these medium voltage motors not only leads to high repair expenses, but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for medium voltage motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air-gap eccentricity detection. For each category, the related features of medium voltage motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy and implementation complexity; recommendations for the future research in these areas are also presented.

Position and speed control of brushless DC motors using sensorless techniques and application trends.

Abstract: This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.