Akwụnye Smart na Nkeokike Ezie na Etiti maka Iji Tronsformị Transmissio

17yrs 700++ staff 108000m²+m² US$150,000,000+ China

1. Kura da Tattalin Arziki
Akwai matsaloli mai yawa da ke tattaunawa a cikin na'urar da suka ɗaya daga masu ƙarfin kula. Daga baya, muhimman abubuwa da suka yi nasara da tsawon lokaci suna ƙasance da ƙarfin sahihi, inganci, da kuma ƙarfin daidaito. Daga baya, hukumar da ƙarin bayanai da ƙarin ƙarfin da suka ƙare da mutum ba su fiye ba, wanda ya ƙare da ci gaba a ƙoƙarin ƙwarewa. Ƙarin bayanai suna ƙasance da mafi girma, matsaloli, da kuma ƙasance da ƙarin ƙwarewa. Wannan ya zama ƙaramin da take daɗe da ƙarfin daidaito, inganci, da kuma ƙarfin daidaito na ƙarfin kula. Saboda haka, ana bukata don in ƙarfafa ƙarfin daidaito da kuma in juye ƙarin bayanai mai ƙarfin daidaito.

2. Bayanin: Tattalin Arziki na Yau da Ƙarin Bayanai Mai Ƙarfin Daidaito
Wannan bayanin ya ƙunshi tattalin arziki da ta ƙunshi "Ƙarin Ƙarfin Daɗi" da "Ƙarin Ƙarfin Daɗi" don in ƙarfafa ƙarfin daidaito, inganci, da kuma ƙarfin daidaito na ƙarfin kula ta hanyar ƙarin bayanai mai ƙarfin daidaito.

2.1 Ƙarin Ƙarfin Daɗi

Yin Tsara Da On-Load Tap Changers (OLTC): Ziyartar da ƙarfin da suka ƙare da tsawon lokaci ko wadanda ba su ƙarfin daidaito ba. OLTC ya ƙunshi ƙarfin da ta ƙarfin daidaito a lokacin da ake amfani a kan ƙarfin kula, wanda ya ƙarfin daidaito da ƙarfin daidaito. Wannan ya ƙarfin daidaito ƙarfin daidaito da ƙarfin daidaito, ta ƙarfin daidaito da ƙarfin daidaito, da kuma ƙarfin daidaito da ƙarfin daidaito da ƙarfin daidaito.
Amfani Da Gas-Insulated Switchgear (GIS): Ziyartar da GIS da ƙarfin da suka ƙare da Air-Insulated Switchgear (AIS) a ƙarfin da suka ƙare da ƙarfin daidaito. GIS ya ƙunshi circuit breakers, disconnectors, grounding switches, transformers, da kuma surge arresters a kan ƙarfin da suka ƙare da gas. Muhimman fa'idon sun haɗa:

Ƙarfin Daɗi: Ya ƙarfin daɗi 10%-30% daga ƙarfin da suka ƙare da AIS, wanda ya ƙarfin daɗi daidaito a kan ƙarfin da suka ƙare da ƙarfin daidaito—wanda ya ƙarfin daɗi a cikin birnin, ƙarfin da suka ƙare da ƙarfin daidaito, ko kuma ƙarfin da suka ƙare da ƙarfin daidaito.
Ƙarfin Daɗi: Ƙarfin daɗi ya ƙarfin daɗi da ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, da kuma ƙarfin da suka ƙare da ƙarfin daidaito, wanda ya ƙarfin daɗi da ƙarfin daidaito.
Ƙarfin Daɗi & Inganci: Ya ƙarfin daɗi da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito; ƙarfin da suka ƙare da ƙarfin daidaito. Ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito.
Ƙarfin Daɗi & EMI: Ƙarfin daɗi ya ƙarfin daɗi da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito.

2.2 Ƙarfin Daɗi Da Ƙarfin Daɗi

Dissolved Gas Analysis (DGA) Online Monitoring: Ana amfani da shi a kan ƙarfin daɗi. Real-time analyzers an fitowa a kan ƙarfin daɗi, suna ƙarfin daɗi da ƙarfin daidaito, ƙarfin da suka ƙare da ƙarfin daidaito (H₂, CH₄, C₂H₆, C₂H₄, C₂H₂, CO, CO₂).

Ƙarfin Daɗi: Ƙarfin daɗi, ƙarfin daɗi, da ƙarfin daɗi suna ƙarfin daɗi da ƙarfin daidaito (misali, thermal decomposition, partial/arcing discharge, oil overheating). Ta hanyar analytical models (misali, Duval Triangle, Rogers Ratios), ƙarfin daɗi ya ƙarfin daɗi da ƙarfin daidaito, ƙarfin daɗi da ƙarfin daidaito (misali, winding overheating, core grounding faults, insulation degradation), ƙarfin daɗi da ƙarfin daidaito.

2.3 AI-Driven Smart Maintenance Management

Unified Data Platform: An ƙunshi data daga masu ƙarfin daɗi (DGA, partial discharge, core current, oil temperature/level, bushing losses), ƙarfin daɗi, ƙarfin daɗi, da ƙarfin daɗi (load, voltage, ambient temperature) don in ƙunshi ƙarfin daɗi.
Big Data Analytics: An amfani da data mining don in ƙunshi data da ƙarfin daɗi, ƙarfin daɗi, da ƙarfin daɗi (especially in DGA parameters).
AI-Powered Diagnosis & Decision-Making:

Fault Diagnosis & Localization: ML algorithms (misali, DNNs, SVM, Random Forest) suna ƙarfin daɗi da ƙarfin daɗi. Ta hanyar data na lokaci, models suna ƙarfin daɗi da ƙarfin daɗi (misali, thermal vs. electrical faults) da ƙarfin daɗi (misali, windings, core, tap changers), ƙarfin daɗi da ƙarfin daɗi.
Health Assessment & Lifespan Prediction: AI suna ƙarfin daɗi da ƙarfin daɗi, ƙarfin daɗi da ƙarfin daɗi (misali, Health Index) da ƙarfin daɗi, ƙarfin daɗi da ƙarfin daɗi.
Risk Alerts & Maintenance Optimization: Systems suna ƙarfin daɗi da ƙarfin daɗi. Optimization algorithms suna ƙarfin daɗi da ƙarfin daɗi (misali, outage planning, task prioritization) ta hanyar ƙarfin daɗi, ƙarfin daɗi, da ƙarfin daɗi. Ƙarfin daɗi suna ƙarfin daɗi da ƙarfin daɗi.
Expert Knowledge Base: Built-in knowledge graphs and expert systems structure domain expertise and standards, supporting explainable AI decisions and boosting credibility.

3. Ƙarfin Daɗi

Enhanced Intelligence: Combines smart hardware (OLTC auto-regulation), sensors, and AI to enable "self-perception, self-diagnosis, self-decision, self-optimization."
Improved Reliability: Higher inherent reliability of GIS/OLTC; AI monitoring reduces unplanned outages by preempting failures.
Increased Safety: GIS design and smart monitoring lower explosion/fire risks; early fault intervention prevents accidents.
Lower Maintenance Costs: Reduces manual inspection frequency; condition-based maintenance avoids over-/under-maintenance and optimizes resources/spares; preventive measures cut repair expenses.
Resource Efficiency: GIS saves land; smart maintenance boosts equipment/personnel utilization.
Extended Lifespan: Proactive health management slows insulation aging and performance decline, prolonging service life.

4. Implementation Recommendations

Phased Rollout: Prioritize aging equipment, critical substations, and urban load centers.
Standardization First: Develop uniform specs for equipment selection, sensor installation, data protocols, platform interfaces, and AI modeling.
Data Integration: Break silos by consolidating monitoring and management data onto a unified platform.
Workforce Transformation: Train staff in smart monitoring, data analytics, and AI diagnostics to shift toward data-driven, human-AI collaboration.
Continuous Improvement: Iteratively refine AI models and strategies using operational feedback.