Artificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers

International Journal of Smart Electrical Engineering

مقاله 6، دوره 03، شماره 01، اردیبهشت 2014، صفحه 37-46   

نویسندگان

A. R Moradi؛ Y Alinejad Beromi؛ K Kiani؛ Z Moravej

Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.

چکیده

Protection systems have vital role in network reliability in short circuit mode and proper operating for relays. Current transformer often in transient and saturation under short circuit mode causes mal-operation of relays which will have undesirable effects. Therefore, proper and quick identification of Current transformer saturation is so important. In this paper, 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 between Current transformer saturation and fault currents in power transformers. In fact, GSA operates based on gravity law 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 an ANN, and then in second step, using training data considered ANN has been trained by GSA & PSO. Finally, a proposed technique has been compared with one of the common training approach which is called Genetic algorithm (GA).

کلیدواژه‌ها

Artificial Neural Network؛ Current transformer saturation؛ Genetic Algorithm؛ Gravitational Search Algorithm؛ Internal Faults؛ particle swarm optimization؛ Power transformers

مراجع

[1]

M. S. Sachdev, T. S. Sidhu, and H. S. Gill, “A Busbar Protection Technique and Its Performance during CT Saturation and CT Ratio-Mismatch”, IEEE Transaction on Power Delivery, Vol.15, No.3, pp. 895–901, July 2000.

[2]

C. Fernández, “An Impedance-Based CT Saturation Detection Algorithm for Bus-Bar Differential Protection”, IEEE Transaction on Power Delivery, Vol.16, No.4, pp.468–472, October 2001.

[3]

Y. Kang, S. Ok, and S. Kang, "A CT Saturation Detection Algorithm”, IEEE Transaction on Power Delivery, Vol.19, No.1, pp.78–85, January 2004.

[4]

N. Villamagna and P. A. Crossley, “A CT Saturation Detection Algorithm Using Symmetrical Components for Current Differential Protection”, IEEE Transaction on Power Delivery, Vol.21, No.1, pp.38–45, January 2006.

[5]

Chi-Shan Yu, “Detection and Correction of Saturated Current Transformer Measurements Using Decaying DC Components”, IEEE Transaction on Power Delivery, Vol.25, No.3, pp.1340–1347, July 2010.

[6]

Z. Lu, J. S. Smith, and Q. H. Wu, “Morphological Lifting Scheme for Current Transformer Saturation Detection and Compensation”, IEEE Transaction on Circuits and Systems, Vol.55, No.10, pp.3349–3357, November 2008.

[7]

F.B. Ajaei, M. Sanaye-Pasand, M. Davarpanah, A. Rezaei-Zare, R. Iravani, “ Compensation of the Current-Transformer Saturation Effects for Digital Relays”, IEEE Transaction on Power Delivery, Vol.26, No.4, pp.2531-2540, October 2011

[8]

Waldemar Rebizant, and Daniel Bejmert, “Current Transformer Saturation Detection with Genetically Optimized Neural Networks”, IEEE Transaction on Power Delivery, Vol.22, No.2, pp.820–827, April 2007.

[9]

Z. Moravej, D. N. Vishwakarma and S. P. Singh, “Protection and Conditions Monitoring of Power Transformer Using ANN”, Electric Power Components and Systems, Vol.30, Issue 3, pp.217–231, March 2002.

[10]

M. Geethanjali, S. M. R. Slochanal, and R. Bhavani, “PSO trained ANN-based differential protection scheme for power transformers”, Neurocomputing, Vol.71, pp.904–918, April 2007.

[11]

E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm”, Information Sciences, Vol.179, pp.2232–2248, March 2009.

[12]

C. Li, and J. Zhou, “Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm”, Energy Conversion and Management, Vol.52, pp.374–381, March 2011.