Abstract

The smart grid aims to improve the quality and reliability of power at the generation, transmission, and distribution levels. The transmission lines can be considered the arteries of the power system, as they carry power over long distances and are exposed to difficult terrain. Transmission line protection philosophy is undergoing a change of paradigm with the advent of digital relays and high-speed broadband communication with the global positioning system (GPS). This paper explores the possibility of transmission line protection for a multigenerator system using wavelet multiresolution analysis (MRA) and computational intelligence techniques in conjunction with GPS. The inputs for the wavelet transform are the synchronized currents measured from remote telemetry units (RTUs) using GPS technology on different buses. The smart location technique uses a wavelet MRA technique to extract the features of the transient current signals based on the harmonics generated at the instant of occurrence of faults due to the abrupt changes of currents in a three-phase transmission line. These extracted features, with such computational intelligence techniques as an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), lead the grid toward smarter strategies for locating the fault distance. A comparative study establishes the superiority of ANFIS over ANNs for more accurate and reliable smart fault location. Furthermore, the efficacy of the proposed method is validated through a Monte Carlo simulation to incorporate the stochastic (random) nature of fault occurrence in a real-time transmission line. The most significant contribution of this paper is that the proposed smart location technique is immune to the effects of the fault inception angle (FIA), fault impedance, fault distance, and power angle. The results contained in this paper validate the use of the proposed algorithm for the real-time smart grid operation of transmission lines.

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