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Abstract
Induction motors are the most widely used motors for appliances,
industrial control, and automation. i.e. many electric loads are primarily
induction motors. Hence they are often called the prime workhorse of the
industry motion. They are robust, reliable, and durable. Motors consume
more than 50 % of the total energy generated. For this reason it is
important to optimize the efficiency of motor drive systems if significant
energy savings are to be obtained. The induction motor (IM), especially
the squirrel - cage type, is widely used in electrical drives and is
responsible for most of the energy consumed by electric motors.
The induction motor losses can be classified as follows:
Stator copper losses.
Rotor copper losses.
Iron losses.
. Stray losses.
• Mechanical (friction + windage) losses.
The main losses, are copper (stator + rotor) and iron losses. The focus
of this research is to minimize these losses or maximizing the efficiency,
that achieves a proper suitable deal of power factor improvement. The
concept of energy saving has always been an attention grabber, especially
when the promised savings are high and the potential for a reduction in
running costs appears high. It is experienced that, the induction motor at
fractional loads, is an inherently inefficient device, because its efficiency falls at light loads. So the aim here is to improve the efficiency especially at light loads. Some words should be putted into our consideration that, Only energy that is being wasted could be saved.
In this research, it is hopeful to give a point of view on efficiency
optimization problem and so the power factor improvement. By using
sensorless speed control of I.M. Because controlled induction motor
drives without mechanical speed sensors at the motor shaft have the
attractions of low cost and high reliability. To replace the sensor, the
information on the rotor speed is extracted from measured stator voltages
and currents at the motor terminals. The artificial neural networks are
used here to achieve goals of this research.