NEWS INFORMATINO
YunNeng Magic Cube equips energy storage power stations with a "smart, safety-optimized control brain," enabling proactive early warning and fault waveform recording.
Published:2024-08-09
“Safety” is the most critical factor in the application of energy storage systems, and energy storage batteries are at the heart of safety management for the entire system. Recently, while Yunneng Magic Cube’s intelligent grid-based digital energy management platform has garnered widespread market acclaim, its brand-new subsystem—the “Energy Storage Power Station Battery Early Warning System”—has also attracted significant attention.
Energy storage batteries are the core components of energy storage systems, and their safety directly determines the secure operation of the entire system. However, due to potential safety risks during operation—such as overcharging, overdischarging, and microshort circuits—these hidden hazards cannot be detected through conventional operational data monitoring. Moreover, traditional battery-testing methods currently rely only on simple assessments of parameters like temperature and voltage, failing to provide an effective and accurate evaluation of critical metrics such as battery consistency, degradation levels, and overall health. As a result, these limitations greatly increase the risk of latent battery issues worsening over time, ultimately leading to potentially catastrophic safety incidents. Therefore, it is now more crucial than ever to implement a robust battery early-warning system that continuously monitors the operational status and trends of energy storage batteries in real time, enabling timely alerts and preventive measures to mitigate risks before they escalate.
As a result, Yunneng Magic Cube, in collaboration with Wuhan Weine, has launched the industry-leading "Energy Storage Power Station Battery Early Warning System." Built on the electrochemical mechanisms of batteries and leveraging big data analytics, this system employs advanced intelligent algorithm models. By integrating massive operational data from electric vehicle power batteries and energy storage batteries used in commercial and industrial energy storage stations, the system has undergone more than five years of rigorous algorithmic learning, iteration, and validation—achieving an accuracy and recall rate exceeding 95%. It can identify potential safety risks more than 28 days in advance and enable proactive, tiered diagnostic alerts as early as 24 hours ahead of time.
This system is easy to deploy, highly compatible, delivers accurate early warnings, and boasts a wide range of application scenarios. It features comprehensive monitoring data, meticulously designed logic algorithms, precise warning analysis, and well-rounded diagnostic solutions. The system can achieve:
Multidimensional Data Monitoring: The system continuously monitors cell voltage, temperature, SOC, SOH, SOE, and other key parameters around the clock, providing real-time assessments of metrics such as voltage differences, temperature variations, and consistency levels—enabling prompt detection of any abnormal cell behavior.
Early warning analysis and localization: Based on monitoring data and leveraging advanced early warning algorithms, the system provides real-time alerts for abnormal battery cells, along with detailed diagnostic reports that help operations and maintenance personnel quickly identify the faulty cells and pinpoint the underlying causes.
Safety Alert Classification: Based on the battery safety rating algorithm and battery alert analysis results, the big data model provides corresponding safety alert classification outcomes (High-risk level: Immediate attention and handling of abnormal cell issues are required; Low-risk level: Battery operation is safe and no action is needed).
One-click Cloud Inspection for Power Plants: Integrated with the power plant grading results, this feature enables a seamless one-click cloud inspection, providing a comprehensive health assessment of high-risk plants and delivering a detailed cloud inspection report. This makes plant risk factors and key risk indicators instantly clear at a glance.
Intelligent Diagnosis and Maintenance: By integrating early warning classification results with battery alert information, the system automatically generates equipment maintenance records and seamlessly connects with the work order system, providing end-to-end service support—from battery cell warnings to timely repairs.
In addition, this battery early warning system can visually display the analysis results, clearly presenting the diagnosis of alarm causes, warning levels, and recommended solutions—enabling maintenance personnel to efficiently address safety risks, thereby boosting operational efficiency while reducing labor costs.
Embrace diversity and draw from the strengths of others.
In addition to battery safety risks, electrical safety and environmental safety are also key factors affecting the security of energy storage systems. Energy storage systems involve a large number of electrical devices, such as PCS units, switchgear, and more, which may pose electrical safety risks during operation—like electrical fires or electric shocks. Moreover, the operating environment of these systems can also influence their safety; for instance, harsh conditions like high temperatures or humidity may degrade equipment performance, potentially leading to safety incidents.
At this point, the energy storage power station fault recording system—acting as the "keystone" of Yuneng Magic Cube's security management and control tools—also plays a crucial role in collaborative management and control.
Compared to battery early warning systems, the fault recording system offers more comprehensive monitoring capabilities. With its independent data recording framework, it can seamlessly collect a wide range of data—including PCS, environmental metrics, video feeds, and fire protection systems—ensuring high-precision data acquisition, superior anti-interference performance, and millisecond-level data logging. This approach fundamentally integrates data collection across all critical points of energy storage equipment, breaking down data silos and enabling a true, full-fledged retrospective analysis of safety-related fault data. Ultimately, this provides even stronger support for the safe operation of energy storage power stations.
Throughout the years, Yunneng Magic Cube has consistently prioritized the safe management of energy storage devices as both the starting point and ultimate goal of its product technology R&D. By continuously integrating digital safety management tools with cutting-edge energy technologies, the company seeks innovative solutions through technological convergence to achieve the optimal approach for ensuring the secure operation of its energy storage systems.
Whether it's the highly anticipated "Energy Storage Station Battery Early Warning System" or the industry-exclusive, invention-patented "Energy Storage Station Fault Recorder System," both represent just a small stepping stone on Yuneng Magic Cube's journey to build the "intelligent safety management brain" for energy storage products. Yet, this seemingly simple touchpoint is bound to open up an entirely new realm of safety applications for the entire energy storage industry.
Related News
Contact Information
Service Hotline
Customer Service Email
Company Address
Room 1005, Building 30, Suzhou Nano City, 99 Jinji Lake Avenue, Suzhou City, Jiangsu Province
follow us
Powered by:www.300.cn | Hefei | Tag