An AI-integrated adaptive pilot protection strategy using hierarchical Euclidean distance for transmission lines
TL;DR
Simulation results in PSCAD/EMTDC demonstrate that the proposed method significantly outperforms traditional differential protection in sensitivity and reliability, effectively mitigating maloperation risks under highimpedance faults and weak-infeed conditions, offering a viable software upgrade path for existing infrastructures.
Abstract
Large-scale renewables are weak-feed and their output current is controlled. That doesn't play nice with traditional pilot protection, which only looks at power-frequency components. When a fault happens, the current from these sources has a "low fundamental, high transient" signature—so the protection often ends up tripping when it shouldn't. This paper proposes a novel adaptive protection scheme utilizing a hierarchical weighted Euclidean distance. It constructs a duallayer feature vector from low-frequency (50Hz) and high-frequency (1kHz) current components measured at both line ends. A composite fault indicator is calculated by adaptively weighting the Euclidean distances within each layer based on real-time signal-to-noise ratio and line attenuation. This approach leverages complementary fault information across frequency bands. Simulation results in PSCAD/EMTDC demonstrate that the proposed method significantly outperforms traditional differential protection in sensitivity and reliability, effectively mitigating maloperation risks under highimpedance faults and weak-infeed conditions, offering a viable software upgrade path for existing infrastructures.