Security and regulatory compliance
Ensures the security, privacy and integrity of industrial data with encryption, access controls and compliance with industry regulations.
Anticipate risks in the electrical grid before they become incidents
Datastruc is an innovation project to improve the security, efficiency and resilience of transformer substations through advanced monitoring, data analysis and artificial intelligence.
Caractersticas principales
Interoperability and collaboration
Encourages collaboration and data sharing among stakeholders within the industrial ecosystem.
Scalability and flexibility
Scalable and flexible infrastructure, using solutions that are today a standard within data platform projects.
Funcionalidades
Unified data integration
Seamlessly integrates data from disparate sources, such as sensor-equipped IoT devices, other production equipment and business systems, providing a single source for all operational data.
Real-time information and analytics
The primary source of information is real-time sensor data, enabling data-driven decision-making and process optimisation.
Predictive maintenance
Proactively identifies and prevents equipment failures, reduces downtime and optimises maintenance schedules with artificial intelligence-driven algorithms.
Use cases
Fire and overheating prevention
Flood detection in transformer substations
Early identification of electrical faults
Detection of intrusions or unauthorised access
Monitoring of sensor and connectivity failures
Sensors
Air quality
Air quality monitoring in transformer substations.
Partial discharge
Detection of partial electrical discharges.
Gas
Detection of harmful or hazardous gases.
Humidity
Monitoring of ambient humidity levels.
Presence
Detection of unauthorised human presence.
Temperature
Real-time temperature monitoring.
Thermal camera
Thermal imaging for hot spot detection.
Ultrasound
Anomaly detection via ultrasound.
Water level
Water level monitoring for flood detection.
Technologies
Data capture directly in critical infrastructures
DataStruc relies on industrial sensing and IoT technologies to collect near real-time information (every second) from transformer substations, capturing electrical, thermal, environmental and presence variables.
Real-time data platform
Processing and management of large volumes of operational data. The solution uses a scalable data platform capable of handling continuous information flows, structuring data and making it available for analysis and operational exploitation processes.
Advanced analytics and artificial intelligence
Early anomaly detection beyond simple thresholds. DataStruc incorporates advanced analytics and machine learning techniques to identify anomalous patterns, reducing false positives and enabling an anticipatory response to potential incidents.
Interoperability and Data Spaces
Data prepared for safe reuse and collaboration. The DataStruc architecture is designed following interoperability principles, preparing data and metadata for future integration into energy sector Data Spaces, fostering reuse, collaboration and new data-driven services.
Participating entities
Several entities with complementary technical responsibilities participate in the DATASTRUC project, aligned with the objectives of system design, development and validation.
Technology 2 Client (T2C) acts as the applicant entity and is responsible for the overall technical integration of the solution, including architecture design, the data platform and the exploitation application.
Masense, participating in the project as a subcontracted entity by T2C, provides specialised knowledge in the field of sensing and data capture, including IoT technologies, signal definition and sensor deployment in industrial infrastructures. Its contribution focuses on the technical aspects associated with field data acquisition.
The Universitat Politècnica de Catalunya (UPC) participates as an expert entity in advanced data analysis and artificial intelligence, collaborating in defining models for anomaly detection, signal interpretation and analytical validation of results.
This combination of technical capabilities allows the project to be approached from a multidisciplinary perspective, coherently covering the aspects of data capture, processing, analysis and exploitation.
