Wavelet Analysis of a High Impedance Fault Modeled as a Stochastic Hybrid SystemProducts
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Introduction: electrical networks are sensitive to faults, and among the most common types, one of the most difficult to detect is the High Impedance Fault (HIF). This type of fault can go unnoticed due to its particular characteristics, such as the ground resistance and voltage drops in the electric arc.
Objective: to propose a stochastic hybrid model for simulating high impedance faults, using the Ornstein-Uhlenbeck process to describe the random nature of these faults. The model focuses on key HIF parameters: ground resistance and voltage drops in the electric arc.
Methodology: the Ornstein-Uhlenbeck process is used to model the randomness of high impedance faults. Simulations are conducted to compare the numerical results with experimental signals reported in the literature. Additionally, both continuous and discrete wavelet transforms are applied to the line current signal to analyze fault characteristics.
Results: the simulations show a qualitative similarity between the numerical results obtained and the experimental signals available in the literature. Both continuous and discrete wavelet transforms reveal typical features of high impedance faults, validating the effectiveness of the proposed model.
Conclusions: the proposed stochastic hybrid model for high impedance faults is effective in simulating this type of fault, and wavelet transform analyses demonstrate its ability to identify distinctive characteristics of HIFs, which can improve the detection and diagnosis of these faults in electrical networks.
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Accepted 2024-10-24
Published 2024-11-21
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