Grey wolf optimization-tuned convolutional neural network for transmission line protection with immunity against symmetrical and asymmetrical power swing

Abstract

The similar current–voltage profile during power swing and fault quite often leads to maloperation of distance relays. As compared to symmetrical power swings, discriminating a swing scenario from a fault becomes more challenging during asymmetrical swings arising due to single-pole tripping. Unlike symmetrical power swings, the presence of zero-sequence and negative-sequence current during asymmetrical swing scenarios hinders the application of classical power swing blocking schemes. In this regard, a convolutional neural network (CNN)-based protection scheme has been proposed in this paper, which, in addition to detecting, classifying, and locating faults, is also able to discriminate between power swing (both symmetrical and asymmetrical) and faults. The discrimination avoids possible maloperation during the non-faulty stressed conditions, thereby overcoming the limitation of the existing protection scheme. With the convolutional neural network, the raw signals are directly fed to the classifier, thus avoiding the computational cost associated with feature extraction in time and frequency domains. With the aim of achieving improved input–output mapping capability of CNN for larger datasets, an evolutionary optimization technique, i.e., grey wolf optimization, has been utilized for determining the optimal values of CNN tuning parameters. The performance of the proposed scheme has been extensively validated for a wide range of fault and power swing conditions in terms of standard indices, i.e., dependability, security, and accuracy. The effectiveness of the proposed scheme has also been evaluated for practical setting by performing real-time simulation on OPAL-RT digital simulator.