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

الموضوعات : مهندسی هوشمند برق

A. R Moradi ¹ , Y Alinejad Beromi ² , K Kiani ³ , Z Moravej ⁴

1 - Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
2 - Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
3 - Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
4 - Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran.

تاريخ الإرسال : 02 الثلاثاء , رجب, 1436 تاريخ التأكيد : 02 الثلاثاء , رجب, 1436 تاريخ الإصدار : 07 الأربعاء , ذو الحجة, 1435

الکلمات المفتاحية: Genetic Algorithm, particle swarm optimization, Artificial Neural Network, Current transformer saturation, Gravitational Search Algorithm, Internal Faults, Power transformers,

ملخص المقالة :

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).

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