Network Monitoring (Smart Grids)
A distribution network operator today runs a fully rolled-out fleet of communicating meters. Each meter transmits data at regular intervals: consumption readings, load curves, technical alerts, connection status. This represents several billion measurement points per day. Operations teams must detect in real time voltage drifts, potential fraud, insulation faults, phase imbalances.
Regulatory obligations (TURPE, European network codes) impose strict response times and complete traceability. Meanwhile, legacy systems: designed for half-yearly readings: become saturated. Conventional relational databases struggle beyond a certain volume per table. Data teams spend more time maintaining fragile pipelines than analysing.
And when a network incident occurs, reconstructing the chronology of events takes hours instead of minutes. Fine control of smart grids is not a luxury: it is an operational and regulatory obligation.
This solution belongs to the Data, Master Records & Reporting family.
Challenges holding back your performance
Current approaches demonstrate their limitations when confronted with data volume and velocity. Traditional relational databases collapse beyond a certain volume of temporal rows. Teams cobble together overnight extractions to flat files, followed by spreadsheet macros for analysis.Result: anomalies detected far too late to act on. Generic BI tools are not designed for queries on sliding temporal windows of several terabytes. Generic service providers propose oversized architectures, calibrated for web giants, with infrastructure and maintenance costs incompatible with regional utilities’ budgets.
Our Technical Approach
Our approach rests upon a clear separation between ingestion, storage and analysis. Ingestion utilises distributed message brokers capable of absorbing peaks of several hundred thousand events per second: typically during synchronised readings at midnight. Storage employs databases specialised in time series, designed to efficiently compress repetitive data and enable queries across time ranges without scanning entire tables.
Anomaly detection combines explicit business rules (voltage thresholds, consumption variances) with statistical models that learn the normal profiles of each delivery point. Alerting is hierarchical: operators only see what requires action, not a continuous stream of false positives. Governance incorporates encryption of personal data, GDPR-compliant pseudonymisation, and audit trails for CRE controls.
Network Supervision (Smart Grids)nnTime-Series Fundamentals
Optimised storage for timestamped data, with retention policies (Downsampling).
Massive ingestion
Queuing of messages to handle peaks in readings without data loss.
Anomaly Detection
Algorithms identifying sudden voltage drops or overconsumption (leakage).
Operations Dashboards
Cartographic visualisation of network nodes.
Governance and Security
Encryption and anonymisation of domestic consumption readings.
Proactive Alerting
Creation of rules triggering notifications (SMS/Webhook) to on-call teams.
Technical Architecture
Several sources, one master record to arbitrate between them, then analysis: Core HR & Single Employee Record and Local Government Open Data stem from this same chain.
- Network Supervision (Smart Grids)nnTime-Series FundamentalsOptimised storage for timestamped data, with retention policies (Downsampling).
- Massive ingestionQueuing of messages to handle peaks in readings without data loss.
- Anomaly DetectionAlgorithms identifying sudden voltage drops or overconsumption (leakage).
- Operations DashboardsCartographic visualisation of network nodes.
- Governance and SecurityEncryption and anonymisation of domestic consumption readings.
- Proactive AlertingCreation of rules triggering notifications (SMS/Webhook) to on-call teams.
Smooth and frictionless integration
We work with your existing data collection systems, concentrators and head-ends, whoever built them. The ingestion connects in parallel to your current data flows, without modifying the collection chains validated by your telecommunications teams. The dashboards integrate with existing portals or with your own network control tools. Customer and asset repositories remain in your information systems: we consume them in read-only mode.
No forced migration, no replacement of your dispatch tools.
Network Traffic Audit and Volume Analysis
Mapping of data sources, analysis of load peaks, identification of current bottlenecks. Deliverable: realistic sizing.
Architecture and targeted POC
Deployment on a limited perimeter (one region, one type of meter). Validation of ingestion and querying performance on real data.
Industrialisation and integration
Extension across the entire asset base, connection to existing business tools, implementation of alerting rules and detection models.
Transfer and autonomy
Training of operations and data teams, operational documentation, transition to support mode with progressive skills development.
Measurable results for your organisation
- Meter readings ingested continuously, with no overnight extraction
- Time series stored in a database built for them, with a retention policy
- Synchronised reading peaks absorbed by a message queue, with no data loss
- Alerts ranked by priority: the operator sees only what needs an action
- The timeline of a network incident reconstructable from the measurement base
- GDPR compliance and CRE requirements documented and auditable
Clarifying your decision-making
What is the infrastructure cost compared to our current systems?
On the monitoring projects Clarendis has delivered, infrastructure cost stays below that of a conventional relational database approach, mainly through compression and the absence of oversized servers. Return on investment is measured above all in engineering time saved on pipeline maintenance.
How do you manage the transition without interrupting our operations?
The transition to the new monitoring platform runs in parallel with the existing system over an overlap period agreed with you. Both systems coexist, which allows data consistency to be validated and teams to be trained. The switchover is progressive, by geographical area or by meter type.