Autoregressive Coefficients based Intelligent Protection of Transmission Lines Connected to Type-3 Wind Farms

TL;DR

This paper proposes an intelligent protection scheme for transmission lines connected to Type-3 wind farms using autoregressive coefficients and deep learning, effectively handling various fault conditions and system complexities.

Contribution

It introduces an adaptive fuzzy inference system combined with deep learning for fault detection, localization, and classification in wind farm-connected transmission lines, considering numerous system factors.

Findings

Effective fault detection across diverse conditions

Robust localization and classification accuracy

Handles high impedance and evolving faults

Abstract

Protective relays can mal-operate for transmission lines connected to doubly fed induction generator (DFIG) based large capacity wind farms (WFs). The performance of distance relays protecting such lines is investigated and a statistical model based intelligent protection of the area between the grid and the WF is proposed in this article. The suggested method employs an adaptive fuzzy inference system to detect faults based on autoregressive (AR) coefficients of the 3-phase currents selected using minimum redundancy maximum relevance algorithm. Deep learning networks are used to supervise the detection of faults, their subsequent localization, and classification. The effectiveness of the scheme is evaluated on IEEE 9-bus and IEEE 39-bus systems with varying fault resistances, fault inception times, locations, fault types, wind speeds, and transformer connections. Further, the impact of factors like the presence of type-4 WFs, double circuit lines, WF capacity, grid strength, FACTs devices, reclosing on permanent faults, power swings, fault during power swings, voltage instability, load encroachment, high impedance faults, evolving and cross-country faults, close-in and remote-end faults, CT saturation, sampling rate, data window size, synchronization error, noise, and semi-supervised learning are considered while validating the proposed scheme. The results show the efficacy of the suggested method in dealing with various system conditions and configurations while protecting the transmission lines that are connected to WFs.