Multi‑Center Prototype Enhanced Multimodal Low‑Rank Prompting for Phase-Resolved Partial Discharge Recognition

Phase-Resolved Partial Discharge (PRPD) recognition aims to identify insulation defects using partial‑discharge patterns of power equipment. Existing methods suffer from significant intra-class morphological diversity, inter-class feature overlap, as well as the efficiency‑accuracy trade-off. To tackle these issues, we propose MCP-MMLoP, a dual-module framework consisting of a MMLoP module and a multi-center prototype (MCP) module. Specifically, the MMLoP module realizes parameter‑efficient model adaptation via multimodal low‑rank prompting to mitigate the efficiency‑accuracy trade‑off. Additionally, the MCP module equips each discharge class with multiple sub‑prototypes to explicitly model diverse intra‑class morphological patterns and provide complementary discriminative cues, alleviating recognition difficulties stemming from intra‑class morphological diversity and inter‑class feature similarity. Experiments on our self‑constructed PRPD dataset covering six discharge classes show that MCP‑MMLoP adds merely 0.0092M additional trainable parameters over the MMLoP baseline and achieves state‑of‑the‑art performance, which validates that our framework enhances discriminative capacity for complex PRPD samples while retaining the parameter‑efficiency benefits of low‑rank prompt learning.