https://store-images.s-microsoft.com/image/apps.5150.620b8862-948f-44ea-ac5f-1a6f391218e6.1030cb85-9317-4696-87e4-0f5cae3703de.6b11d765-0658-4110-8cd8-8f4495712ca5
Network Monitoring / Niederspannungs-Monitoring
Robotron Datenbank-Software GmbH
Network Monitoring / Niederspannungs-Monitoring
Robotron Datenbank-Software GmbH
Network Monitoring / Niederspannungs-Monitoring
Robotron Datenbank-Software GmbH
A cloud based electricity network monitoring
Auch für die speziellen Anforderungen der deutschen Energiewirtschaft als Niederspannungs-Monitoring "NiSMo" verfügbar.
Condition monitoring of distribution grid station
Problem
– Rapidly growing expansion of volatile renewable energies increasingly leads to grid bottlenecks in the transmission and distribution grid
– Especially in networks of the lower voltage levels
Implication
– Surplus or shortage of energy today can only be detected in higher network levels due to poorly monitored lower network levels
– Increasing requirements regarding the tasks of a network operator
• Maintaining voltage band at grid connection point
• Avoidance of overloads
– Improved monitoring of low voltage feeders and local network station
Business focus
– Real-time measurement of relevant energetic quantities in (low)-voltage nets with non-regulated measurement technology
– Monitoring of local networks and stations including condition monitoring & alerting
– Analysis, calculation and forecast of live and historical time series data
– Need-based mapping of utilities-relevant master data objects and geolocation data
– Monitoring of local networks and stations including condition monitoring & alerting
– Analysis, calculation and forecast of live and historical time series data
– Need-based mapping of utilities-relevant master data objects and geolocation data
Technical focus
– High scalability in terms of data volume and performance
– Modern architecture with Azure PaaS
– Attractive single-page application frontend: Node.js&React
– High scalability in terms of data volume and performance
– Modern architecture with Azure PaaS
– Attractive single-page application frontend: Node.js&React
Maintenance of distribution grid station
Further benefits of continuous monitoring
– Early detection of short circuits already in the initiation phase
– Precise recognition of triggered fuses in the service center
– Detection of permanently heavily loaded stations or stations that are about to be overloaded
– Supervision of main fuse
– Connection of short-circuit indicators at medium voltage level
– Precise recognition of triggered fuses in the service center
– Detection of permanently heavily loaded stations or stations that are about to be overloaded
– Supervision of main fuse
– Connection of short-circuit indicators at medium voltage level
Integration of monitoring into existing processes
– Automatic notification and visualization of anomalies due to fixed or learned limits
Forecasting & simulation
– Improvement of forecast quality at low voltage level
in day-to-day business
– Early identification of critical connection points
– Extraction of information for network development planning
– Maintenance planning
This application is available in English & German.
https://store-images.s-microsoft.com/image/apps.7635.620b8862-948f-44ea-ac5f-1a6f391218e6.3f28b404-6923-4093-8f6f-e6566476f1e0.81092975-8815-4ebb-a1c8-0603bda1dfc4
https://store-images.s-microsoft.com/image/apps.7635.620b8862-948f-44ea-ac5f-1a6f391218e6.3f28b404-6923-4093-8f6f-e6566476f1e0.81092975-8815-4ebb-a1c8-0603bda1dfc4
https://store-images.s-microsoft.com/image/apps.52781.620b8862-948f-44ea-ac5f-1a6f391218e6.3f28b404-6923-4093-8f6f-e6566476f1e0.c22cd433-08a7-46fe-a7d2-43c3acae6d3a
https://store-images.s-microsoft.com/image/apps.30478.620b8862-948f-44ea-ac5f-1a6f391218e6.3f28b404-6923-4093-8f6f-e6566476f1e0.0f984fe4-179e-4b1d-bf6f-babae557447c
https://store-images.s-microsoft.com/image/apps.41234.620b8862-948f-44ea-ac5f-1a6f391218e6.3f28b404-6923-4093-8f6f-e6566476f1e0.835c3a14-b66e-4441-9672-f18a4c308eea
https://store-images.s-microsoft.com/image/apps.36628.620b8862-948f-44ea-ac5f-1a6f391218e6.f7868e29-e7da-4ff8-8a16-13f256463506.1655235e-fcfc-4c01-92dc-ea5f55645c08