Power factor correction technique for induction motors based on artificial neural network / Mohamad Adha Mohamad Idin, Mohd Halim Mohd Noor and Khairul Azman Ahmad

Mohamad Idin, Mohamad Adha and Mohd Noor, Mohd Halim and Ahmad, Khairul Azman (2011) Power factor correction technique for induction motors based on artificial neural network / Mohamad Adha Mohamad Idin, Mohd Halim Mohd Noor and Khairul Azman Ahmad. [Research Reports] (Unpublished)

Abstract

Energy conservation is a hot topic these days and everybody knows that low power factor can mean waste of electrical energy. For that reason, many questions are asked about the power factor of induction motors. Induction motors are only one of the kinds of electrical equipment that tend to reduce a plant's power factor. And it's the overall plant system’s power factor that counts. There are ways of correcting a low system power factor, so maximum motor power factor isn't vital. System power factor correction is often the better way. Therefore, the need to correct the power factor to approach unity for an induction motor is required. This means correct sizing of power factor capacitors for that motor. If we size the power factor capacitors correctly, the voltage stability and efficiency will be increased while energy losses and electrical cost will be reduced. If the power factor capacitors are undersized or oversized, it can cause adverse effects on voltage stability and system efficiency. To overcome this problem, the new method of power factor correction is introduced. In this research, an artificial neural network will be used as a method to solve it. This is because the artificial neural network can give almost accurate result with the minimum error.

Metadata

Item Type: Research Reports
Creators:
Creators
Email / ID Num.
Mohamad Idin, Mohamad Adha
UNSPECIFIED
Mohd Noor, Mohd Halim
UNSPECIFIED
Ahmad, Khairul Azman
UNSPECIFIED
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Dynamoelectric machinery and auxiliaries.Including generators, motors, transformers
Divisions: Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus
Keywords: Energy Conservation, Induction Motors, Artificial Neural Network
Date: November 2011
URI: https://ir.uitm.edu.my/id/eprint/39957
Edit Item
Edit Item

Download

[thumbnail of 39957.PDF] Text
39957.PDF

Download (83kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:
On Shelf

ID Number

39957

Indexing

Statistic

Statistic details