A hybrid wavelet–APSO–ANN-based protection scheme for six-phase transmission line with real-time validation

Abstract

The ever-increasing load demand motivates the adoption of the six-phase transmission system as a viable alternative to the conventional three-phase double-circuit line because of its enhanced power transfer capability. However, the acceptance of six-phase transmission is highly dependent on its protection scheme, which is a challenging task due to the large number of possible faults. In this regard, this paper describes a novel concept based on the hybridization of wavelet transform, adaptive particle swarm optimization (APSO) and ANN for fault classification and location in the six-phase transmission line. The proposed scheme overcomes the difficulties associated with existing ANN-based protection schemes related to the selection of architecture and convergence to the non-global optimal set of weight vectors and biases post-training. From the features/attributes extracted using wavelet transform, the hybrid algorithm utilizes the strong exploration and mapping capability of APSO and ANN, respectively, for input–output mapping. It significantly increases the probability of global convergence for ANN training, thereby improving the performance of the protection scheme in terms of fault detection, classification and location estimation accuracy. The proposed scheme has been extensively tested for wide variation in different fault attributes. In addition to the offline simulations, the effectiveness of the proposed scheme has been validated in real-time environment by carrying out hardware in loop simulations using OPAL-RT digital simulator.