Fault Identification in Renewable Energy Transmission Lines Using Wavelet Packet Decomposition and Voltage Waveform Analysis

The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods. To address these challenges, this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines, leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms. First, the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems, and examines the voltage characteristics of faulted phases in renewable energy scenarios. The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults, whereas permanent faults do not exhibit such variations. Building on this observation, the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition, wherein low-frequency waveform components are selected to characterize fault features. Subsequently, a fault nature identification method is introduced, based on a voltage waveform time-frequency morphology comparison. By employing a windowing technique to quantify waveform differences before and after arc extinction, this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time. Finally, a 220 kV renewable energy grid connection line model is developed using PSCAD for verification. The results demonstrate that the proposed method is highly adaptable across various fault locations, transition resistances, and renewable energy control strategies, and can reliably identify fault nature in renewable energy grid connection scenarios.