Electrical Load Manageability Factor Analyses by Artificial Neural Network Training

Document Type : Research paper

Authors

Department of Electrical Engineering, University of Zanjan, Zanjan, Iran

Abstract

On typical medium voltage feeder, Load side management means power energy consumption controlling at connected loads. Each load has various amount of reaction to essential parameters variation that collection of these reactions is mentioned feeder behavior to each parameter variation. Temperature, humidity, and energy pricing variation or major event happening and power utility announcing to the customers are essential parameters that are considered at recent researches. Depends on amount of improvement that each changeable parameters effect on feeder load consumption, financial assets could be managed correctly to gain proper load side management. Collecting feeder loads behavior to all mentioned parameters will gain Load Manageability Factor (LMF) that helps power utilities to manage load side consumption. Calculating this factor needs to find out each types of load with unique inherent features behavior to each parameters variation. This paper and future works will help us to catch mentioned LMF. In this paper analysis of typical commercial feeder behavior due to temperature and humidity variation with training artificial neural network will be done. Load behavior due to other essential parameters variations like energy pricing variation, major event happening, and power utility announcing to the customers, and etc will study in future works

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References

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Journal of Operation and Automation in Power Engineering
Volume 7, Issue 2
October 2019
Pages 187-195

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History

  • Receive Date: 28 October 2018
  • Revise Date: 16 March 2019
  • Accept Date: 26 March 2019
  • First Publish Date: 01 October 2019

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