Air-Insulated Switchgear Voltage Transformer (AIS VT) Full Lifecycle Intelligent O&M Solution: O&M-Driven Transformation Solushi na Iyomata na Ọkwa na Nsọpụrụ Ugwu Site n'elu (AIS VT) na Nke Okike Ugbu a Banyere O&M: Inwe Mmetụta Na-etinye O&M

Solution Overview:
Na ƙarin abubuwa na tattalin aiki da kuma gudanar da aikin (O&M) na AIS VT na ƙarfin, wannan bayanai yana amfani da ƙarfin fasahar tattalin aiki uku - "Sensing & IoT - Digital Twin - Predictive Decision-Making" - don ɗaukan tattalin aiki mai sarrafa cikin tsawon aiki na takam. Ƙasar Da Ma'ana: Samun hanyoyin da suka shafi masu hanyoyi game da hanyoyin da suka shafi masu bayanan, ya zama min samun hanyoyin da suka shafi masu bayanan zuwa hanyoyin da suka shafi masu bayanan, wanda yake iya haifar da O&M cost da kuma ƙwayoyin.

I. Haɗa Da Ƙasashe Na Tattalin Aiki Na Ƙarfin

Haɗa Da Tattalin Aiki Mai Yawan Adadin: Ana yi amfani da tattalin aiki na ƙarfin, wanda ke maimaita ƙwarewa, kayan adaki, da kuma lokacin da aka ɗauke, wanda ke maimaita ƙwarewa waɗannan tattalin aiki daban-daban.
Abin da Ya Bari Wannan Tattalin Aiki: Hukumar da suka shafi masu hanyoyi suna da alaka, ba su iya haifar da ƙaramin kanzara (misali, siffofin ruwan, lafiya) ko ƙaramin kanzara (misali, partial discharge). Ba su iya haifar da ƙaramin kanzara, wanda ke maimaita ƙwarewa waɗannan tattalin aiki daban-daban.

II. Fasahar Tattalin Aiki Uku & Ƙarfin Masu Inganci

Fasahar Sensing Mai Tsabta: Embedded IoT Condition Monitoring Module

Kafa Masu Hanyoyin a Baya:

Dielectric Dissipation Factor (tanδ): Ana shiga ƙaramin kanzara da kuma siffofin ruwan - wanda yake iya haifar da ƙaramin kanzara.
Partial Discharge (PD): Sensors na makarantar suna shiga ƙaramin kanzara a kanzara ko kafin kanzara don haifar da ƙaramin kanzara na baya.
Temperature (T): Ana shiga tsafta a baya (misali, windings, terminals) don haifar da ƙaramin kanzara.

Abubuwa: Design na modular, installation na live-line, strong electromagnetic interference (EMI) immunity, high-frequency data sampling (to capture transient PD signals).

Fasahar Analytics Mai Tsabta: AIS VT Digital Twin Platform

Data Fusion Daga Masu Bayanai: Ana ƙare bayanai daga sensors, historical test reports, SCADA operational records, da kuma bayanai na takam.
Precise Remaining Useful Life (RUL) Prediction: Ana amfani da machine learning algorithms (e.g., LSTM, ensemble learning) don haifar da ƙaramin kanzara, wanda yake iya haifar da ƙaramin kanzara zuwa 10%.
3D Visualization & Health Assessment: Ana ƙirƙira replica virtual ta takam, ana nuna ƙaramin kanzara, distribution of hotspots, and risk levels, supporting "one-click" diagnosis.

Fasahar Decision Mai Tsabta: Predictive Maintenance Strategy Engine

Dynamic Inspection Optimization: Ana ƙare inspection cycles and tasks based on real-time health scores output by the platform (e.g., extended intervals for healthy devices, targeted enhanced monitoring for sub-healthy devices), reducing ineffective inspections and lowering O&M manpower input by up to 30%.
Precision Maintenance Triggering: Ana ƙare maintenance work orders automatically based on RUL predictions and condition thresholds (e.g., tanδ surge alert prompting inspection, PD exceeding limits triggering urgent defect elimination), preventing both over-maintenance and under-maintenance.
Hierarchical Alarms & Decision Support: Ana define parameter abnormality levels, pushing differentiated alerts (Warning / Alert / Critical); provides knowledge base support for fault location, root cause analysis, and corrective action recommendations.

III. Ideal Application Scenarios

Metropolitan Core Substations: Ensures extremely high power supply reliability requirements while reducing dependence on manpower-intensive O&M dispatching.
Renewable Energy Plant Step-up Substations (PV/Wind): Addresses challenges of unmanned operation in remote areas, enabling remote, refined equipment condition management.
Critical Transmission Nodes & Key Consumer Substations: Minimizes unplanned outage risks to the greatest extent, enhancing power supply continuity.

IV. Core Value & Advantages (Quantified Results)

Metric	Traditional Mode	This Intelligent O&M Solution	Improvement Effect
Annual O&M Cost	Baseline (100%)	Reduced by 35%	Significant Cost Savings
Mean Time To Repair (MTTR)	> 24 hours (Complex faults)	≤ 4 hours	>80% Efficiency Gain
Unplanned Outage Count	High	Significantly Reduced	Enhanced Reliability
Manpower Dependence	High	Reduced by ~30%	Optimized Resource Allocation
Failure Prediction Capability	Almost None	High Precision (RUL error <10%)	Proactive Risk Prevention & Control