Method for determination of status of oil immersed type transformer and apparatus for using the method
10-2020-0121940
Method for failure detection of OLTC(on load tab changer) and apparatus for performing the same
10-2020-0136009
Method for diagnosis and prediction of substation and apparatus for performing the method
10-2022-0140207
Apparatus and method for predicting trasformer state in consideration of whether oil filtering is performed
16/949,809
Method for determining integrity factor through machine learning, and device for performing such method
PCT/KR2022-004541
AWS PARTNER ONEPREDICT
Build a Data Lake with Amazon Storage Services
Data management will be safer and efficient.
Leverage cloud computing on AWS
You can optimaize the management of your key industrial assets in the AWS cloud.
Experience digital twin technology on your computer
With a virtual physical system on the cloud, you can analyze data and monitor the facility status in real time.
Before deployment
Diagnosing the condition of a transformer with the rule-based method made it difficult to predict potential failures, even resulting in major accidents and downtime.
After deployment
An AI and big data-based guardione® which can diagnose conditions and predict failures of transformers up to 5 years in advance empowers to ensure onsite safety.
Method for determination of status of oil immersed type transformer and apparatus for using the method
10-2020-0121940
Method for failure detection of OLTC(on load tab changer) and apparatus for performing the same
10-2020-0136009
Method for diagnosis and prediction of substation and apparatus for performing the method
10-2022-0140207
Apparatus and method for predicting trasformer state in consideration of whether oil filtering is performed
16/949,809
Method for determining integrity factor through machine learning, and device for performing such method
PCT/KR2022-004541
Industrial
AI technology
A couple of
thousand data
98% of
diagnostic accuracy
Industrial AI
technology
A couple of
thousand data
98% of diagnostic accuracy
GUARDIONE® SUBSTATION is provided through a certain structure. It analyzes offline DGA (Dissolved Gas Analysis) data obtained periodically from transformers using the algorithms of GUARDIONE® SUBSTATION, and provides insights for transformer management such as condition diagnosis, prediction, and similarity.
It can be provided in either SaaS (Software as a Service) or on-premise mode, depending on the customer’s requirements. However, for continuous service improvement and customer convenience, the SaaS model is recommended.
In the case of the cloud-based approach, there is no need for additinal sensor or software installation. For the on-premise mode, additional servers or other infrastructure may need to be installed depeding on the customer’s requirements.
No, there are no security issues. GUARDIONE® SUBSTATION provides web services through the AWS cloud, ensuring the highest level of security.
The product usage period can be renewed annually through a contract.
No, during the agreed contract period, all measured data can be uploaded to GUARDIONE® SUBSTATION without any limitations. For example, if there were 20 DGA measurements in a month due to deteriorating transformer conditions, all 20 can be uploaded.
Predicition and similarity algorithms utilize the trend of DGA data, so a minimum of four measurement data points are required for confirmation. However, for the diagnosis algorithm, it can function with just one set of data.