Discrimination of Inrush Currents from Faults Current in Power Transformers using Gravitational Search Algorithm (GSA)

Document Type : Research Article

Authors

1 1Department of electrical engineering, Faculty of Engineering, University of Birjand, Birjand, Iran

2 Department of electrical engineering, Faculty of Engineering, University of Birjand, Birjand, Iran

Abstract

The magnetizing inrush current phenomenon is a large transient condition, which occurs when a transformer is energized. The inrush current magnitude may be as high as ten times of transformer rated current that causes mal-operation of protection systems. Indeed, the similarity between signatures of Inrush current and internal fault condition make this failure. So, for safe running of a transformer, it is necessary to distinguish inrush current from fault currents. In this project, an Artificial Neural Network (ANN) which is trained by two different swarm based algorithms; Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) have been used to discriminate inrush current from fault currents in power transformers. GSA works based on gravity laws and in opposite of other swarm based algorithms, particles have identity and PSO is based on behaviors of bird flocking. Proposed approach has two general stages, in first step, obtained data from simulation have been processed and applied to ANN, and then in step two, using training data considered ANN has been trained by GSA & PSO. Proposed method has been compared with one of the common training approach which is called Back Propagation (BP) and Results show that proposed method is so quick and can do discrimination very accurate.
 

Keywords

Full Text

در این پروژه، شبکه عصبی برای تشخیص جریان هجومی از جریان خطای داخلی استفاده شده است. یک الگوریتم جمعی جدید که GSA نامیده می‌شود، برای آموزش شبکه عصبی ارائه شده است. به منظور نشان دادن کیفیت وتوانایی الگوریتم ارائه شده، نتایج به دست آمده از الگوریتم GSA با الگوریتم PSO و BP که یکی از رایج‌ترین روش‌های آموزش شبکه عصبی است، مقایسه شده است. نتایج نشان می‌دهد که الگوریتم ارائه شده (GSA) زمان تست و آموزش را کاهش می‌دهد.

 

 

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Computational Intelligence in Electrical Engineering
Volume 1, Issue 1
February 2011
Pages 43-58

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History

  • Receive Date: 14 June 2016
  • Revise Date: 23 October 2017
  • Accept Date: 14 June 2016

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