NDLI: Estimation of the consumer peak load for the Iraqi distribution system using intelligent methods

Content Provider	IEEE Xplore Digital Library
Author	Al-Nama, M.A. Al-Hafid, M.S. Al-Fahadi, A.S.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, Mosul University, Iraq (Al-Hafid, M.S.; Al-Fahadi, A.S.) \|\| Department of computer Engineering Al-Hadbaa University, Iraq (Al-Nama, M.A.)
Abstract	The drastic increase of residential load consumption in recent years result in over loading feeder lines and transformers for the Iraqi northern area distribution system especially in the city of Mosul. Solution for this problem requires up to date research consumers load study to find the proper solution to stop excess overload in the transformers and the feeders. This paper include the regional survey for samples of consumers representing typical types of different standard of living and energy consumption by distributing questioners contain list of information such as load type in daily use. Also current readings are recorded for the individual consumer for the months of the year 2006. In addition to those readings, energy consumption is recorded once every two months. The registered readings are used in conjunction with the list of questionnaires to find a sample (for different loads) that coincide with the list of questionnaires for current and energy readings. Resulting in the feasibility of using the sample to know the peak value of current for any consumer even if he is not included in the list of questionnaires and for any new consumer, since it become possible to decide the size of the transformers and feeder lines, to overcome the problem of overloading in any part of the distribution system. The Artificial Neural Network (ANN) is used in this paper to find the above mentioned sample.
Starting Page	374
Ending Page	378
File Size	360320
Page Count	5
File Format	PDF
ISBN	9780956833006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-30
Publisher Place	Iraq
Access Restriction	Subscribed
Rights Holder	Universtiy of Basrah
Subject Keyword	Training Distribution System Energy consumption ANN Accuracy Consumer Peak Load Water heating Estimation Artificial neural networks Artificial intelligence
Content Type	Text
Resource Type	Article

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