Zero-sequence-based relaying technique for protecting power transformers and its performance assessment using unsupervised learning ANN

Abstract

SUMMARYIn this paper a simple and robust new relaying technique for protecting transformers from internal winding faults is proposed. Based on the measurement of zero sequence current inside a delta winding, the technique greatly simplifies the conventional differential relaying arrangement when a delta winding is available. Despite the number of windings of the transformer and the location of the fault, only measurement of induced zero sequence current within the delta winding is needed. Since the proposed technique has been shown to be prone to generate false pick-up signals during inrush, a simple restraining criterion is proposed and analysed. Additionally, use of projection techniques based on self-organizing maps (SOM) is proposed in this paper as a valuable tool for analysing multivariable data which are generated from the huge number of possible combinations existing between switching instant and fault location. Finite element simulations and laboratory tests have been combined into SOM to validate the proposed relaying technique and the restraining criterion.

Laboratory investigation for power transformer protection technique based on positive sequence admittance approach

“…An SLG or LL fault imbalances phase currents of a number of protection zones of a transformer. For the analysis of unbalanced currents, the concept of symmetrical components is usually employed [17]–[19]. The symmetrical component analysis uses a transformation matrix which converts phase components of an unbalanced three‐phase current to equivalent positive‐, negative‐, and zero‐sequence current components.…”

“…References and used the differential current and the flux to discriminate between faults and magnetizing inrush cases for large power transformer. References [17]–[19] utilized the concept of symmetrical components instead of the voltage and current quantities to recognize different internal faults on the transformer. Reference used the concept of the neural network to distinguish between inrush and fault cases on large power transformer.…”