ಕಾರ್ಯನಿರ್ವಹಣ ಮತ್ತು ವಿಕಾರ ನಿರೋಧನ ಉಪಾಯಗಳು ಔದ್ಯೋಗಿಕ ಮತ್ತು ವಾಣಿಜ್ಯ ಶಕ್ತಿ ಸಂಚಯನ ವ್ಯವಸ್ಥೆಗಳಿಗೆ ಯಾವುದು?
kommerṣiyaile mūlāvaikaḷa vīḍiyalakke paṭṭa prathama sādhaka āṇḍa, nānnavaru sarvēśvara matthu rakkhaṇe adhyavasāya śāstra patanakke tarataraka sthitiya kāraṇa idaṁ mahatva avagati madenu. Nānnavaru yela varṣagaḷa akṣepa anubhava pāṭina idaṁ karigāraka samikṣa:
1. Dōṣa Rakkhaṇe mattu Adhyavasāya Śāstra Gāḷuvina Mūlādhāra
Akku Parijñāna
Dina caritra maḍida, nānnavaru nicaya parametara niyantra madena bāhukāra tattva avagati madenu. Akku taapasati 25±2℃ gāḷuvini rakkhi, ±15℃ gāḷuvina viṣama avagati hāgu sandēśa prārthanegi. Niḥśaṅka charge-discharge cycles ≤1 dinada, voltage viṣama 30mV gāḷuvina hogu equalization charging prārambha madenu. Rutuvinamana SOC calibration trimāsada baraha, akku parivartana pañca varṣada baraha, mattu ṭṛṭa coolant tests (conductivity/pH value).
Table 1: Common Faults and Maintenance Cycles for Commercial and Industrial Energy Storage Equipment
BMS (Battery Management System)
Prāyoga saṃsthāpana sāndra sanādhāra bhāva mattu anti-interference rekha uparacchana madenu. Dual Ethernet/GOOSE communication 20ms self-healing gāḷuvini prārambha, shielded cables, filter capacitors, mattu TVS surge protection gāḷuvina sampādane. Maintenance includes quarterly firmware upgrades, monthly protocol compatibility checks, and semi-annual sensor calibration (four-wire resistance measurement).
PCS (Power Conversion System)
Nānnavaru sadā high-reliability IGBT/SiC modules and optimize PWM parameters with enhanced protection functions. Regular upkeep should involve quarterly IGBT module inspections, semi-annual heat sink cleaning, and annual efficiency testing.
Temperature Control System
Vibration protection (optimized piping, 10x volume buffers, natural frequency adjustment) and high-seal corrosion-resistant materials are key. Maintenance includes quarterly seal inspections, semi-annual pressure testing, and annual coolant replacement.
EMS (Energy Management System)
Leverage machine learning for algorithm optimization and deploy SM4 encryption with access control. Regular tasks include quarterly model updates, monthly data integrity checks, and annual disaster recovery drills.
2. Best Practices for Maintenance System Construction
Preventive Maintenance Framework
Adopt a "detect-first" approach using tools like Huawei Smart PCS's health checks. Data analysis should drive maintenance prioritization to ensure proactive issue resolution.
Professional Competency Development
Maintenance teams need interdisciplinary knowledge (electrical, electronics, automation). Regular training on equipment principles, fault diagnosis, and emergency response is vital—for instance, mastering data analysis for CATL's BMS 4.0, which predicts battery anomalies 14 days in advance.
Spare Parts Management
Maintain dynamic inventories for critical components (BMS chips, IGBT modules, liquid-cooling seals). Strict classification, stock control, and usage records ensure rapid replacement, minimizing downtime for projects like Sungrow's 550kW/1145kWh system (90% cycle efficiency).
Environmental Adaptability Management
Customize protection measures according to local climates: implement moisture control during Guangdong's "humid return" season and apply anti-corrosion treatments in coastal areas. Regular environmental monitoring and protective inspections can extend equipment service life.
Data-Driven Maintenance Trend
Emulate Tencent's "Energy Brain" by building a battery health database with machine learning. This enables early fault warning and precise diagnosis, reducing response time from 2 hours to 15 minutes and cutting O&M costs by 40%.
3. Industry Trends and Practical Insights
Years of fieldwork have proven that in-depth fault analysis and targeted solutions for each subsystem form the foundation of a comprehensive maintenance system. As energy storage technology evolves, fault types and solutions continue to update.
Future advancements in AI, IoT, and big data will drive intelligent and precise fault diagnosis. New technologies like multi-modal grid-forming energy storage diagnosis, unsupervised reconstruction error methods, and data-driven SOC calibration will significantly enhance accuracy and efficiency. Higher system integration and intelligence will also streamline maintenance workflows.
It’s critical to adopt a systematic perspective—never isolate equipment or subsystems. Only through scientific strategies and professional teams can we ensure long-term stable operation, maximizing economic and social value for commercial and industrial energy storage in the global energy transition.
