Sensorless Speed Estimation of Induction Motors through Signal Analysis Based on Chaos Using Density of Maxima

Area of Science:

• Electrical Engineering
• Control Systems
• Nonlinear Dynamics

Background:

• Three-phase induction motors are critical industrial components consuming significant energy.
• Accurate speed estimation is vital for efficient motor operation and control.
• Conventional speed sensors are often expensive, unreliable, and unsuitable for harsh environments.

Purpose of the Study:

• To develop a sensorless algorithm for estimating the rotational speed of three-phase induction motors.
• To utilize the chaotic variable of maximum density for speed estimation.
• To improve upon existing speed estimation techniques in terms of response time and reliability.

Main Methods:

• Analysis of motor current signals from the power supply.
• Implementation of a novel algorithm based on the chaotic variable of maximum density.
• Comparison of the proposed method with classical Fourier Transform-based techniques.

Main Results:

• The proposed algorithm accurately estimates motor shaft speed without invasive sensors.
• Achieved a significantly lower response time compared to traditional Fourier Transform methods.
• Demonstrated effective speed estimation under variable load conditions.

Conclusions:

• The developed chaotic variable-based algorithm provides an efficient and reliable sensorless method for induction motor speed estimation.
• This technique offers a cost-effective alternative to traditional speed sensors.
• The method is suitable for applications requiring fast and accurate speed feedback, even in challenging industrial settings.