Grey wolf optimization-tuned convolutional neural network for transmission line protection with immunity against symmetr
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ORIGINAL ARTICLE
Grey wolf optimization-tuned convolutional neural network for transmission line protection with immunity against symmetrical and asymmetrical power swing Sunil K. Shukla1 • Ebha Koley2 • Subhojit Ghosh2 Received: 10 April 2019 / Accepted: 8 April 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Transmission line protection Power swings Convolutional neural network Grey wolf optimization Monte Carlo simulations Real-time validation
1 Introduction The involvement of a large number of components and interconnections increases the vulnerability of the power system to uncertain disturbances arising due to switching of transmission lines, line loading variation, loss of generation, clearance of short-circuit faults, and load shedding. Such disturbances often lead to fluctuations in the
& Ebha Koley [email protected] 1
Department of Electrical Engineering, G H Raisoni Institute of Engineering and Technology, Nagpur, MH, India
2
Department of Electrical Engineering, National Institute of Technology, Raipur, CG, India
generator rotor angle with respect to the stator field, causing oscillations in power flow (variation in voltage and current), commonly referred to as power swing (symmetrical). The power system may experience stabl
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