## 1 Introduction  
The most recent years have witnessed the development of a new technology for wireless communications. The first, the last decade has been the development of a new technology for wireless communications. The second, the last decade has been the development of a new technology for wireless communications. The second, the last decade has been the development of a new technology for wireless communications. The second, the last decade has been the development of a new technology for wireless communications. The third, the last decade has been the development of a new technology for wireless communications. ## 1 Introduction  
High-voltage transmission lines form the critical infrastructure for modern power systems, enabling efficient long-distance electricity transfer, essential for contemporary energy demands. Within these networks, insulators serve a dual function, providing mechanical support for overhead conductors while offering reliable electrical isolation between the conductors and the grounding structure [1; 2; 3]. However, prolonged exposure to electromechanical stress, environmental weathering, and cyclic electrical loads inevitably degrades insulator materials, leading to the formation of \"zero-value defects\", which is a failure mode where insulators lose resistive properties due to internal cracks, surface contamination, or dielectric aging [4; 5; 6; 7]. Such defects transform insulators into conductive pathways, causing leakage currents, localized heating, and flashover events that jeopardize grid stability and risk widespread power outages [8; 9; 10; 11]. Conventional defect detection methods predominantly employ spark gap measurements, relying on manual auditory or visual identification of discharge phenomena [12; 13; 14]. While straightforward, this approach suffers from critical limitations: labor-intensive implementation, subjective interpretation vulnerable to human error, and susceptibility to ambient noise in operational environments [12; 13; 14]. Furthermore, its qualitative nature fails to quantify defect severity, hindering predictive maintenance strategies [15; 16]. Recent advancements in electric field distribution analysis have emerged as a promising alternative. By correlating insulator defects with distortions in local electric field patterns, researchers aim to create a more objective diagnostic framework. However, existing electric field-based techniques often require sophisticated instrumentation, such as capacitive probes or computational simulations, which are impractical for large-scale deployment or real-time monitoring [17; 18; 19]. Additionally, these methods lack the ability to provide intuitive visual feedback, an essential feature for on-site decision-making by maintenance personnel [20; 21]. The limitations of traditional approaches have spurred interest in novel sensing paradigms that combine high sensitivity, real-time operation, and user-friendly diagnostics. Among these, electroluminescent (EL) materials have gained attention due to their unique ability to convert electric field energy directly into visible light. This phenomenon, driven by the recombination of electron-hole pairs in semiconductor materials under an applied electric field, offers a compelling opportunity to visualize voltage variations without external power sources or complex electronics [22; 23; 24]. However, as shown in Table S1 [25; 26; 27; 28; 29], standard EL technologies primarily operate under low-voltage and benign environmental conditions, with a focus on luminous efficiency or color tuning. Conventional EL systems, including perovskite LEDs (CsPb(Br/Cl)\({}_{3}\)), quantum dot devices (MoS\({}_{2}\)@MoO\({}_{3}\)), and organic emitters (B/N-doped hydrocarbons), operate exclusively at low voltages and require precisely engineered electrode interfaces to generate luminescence.