Advanced Low-Voltage Monitoring

Advanced low-voltage monitoring enables Distribution System Operators (DSOs) to turn measurements, events and data from smart meters, sensors, concentrators and corporate systems into a reliable picture of what is actually happening on the network.

Its value lies not simply in deploying devices or displaying information. A mature advanced low-voltage monitoring approach connects the physical network, assets, systems and operational processes, helping DSOs detect problems, understand their causes, prioritise interventions and support maintenance and investment decisions.

Against a backdrop of increasing self-consumption, electric vehicles, heat pumps, industrial electrification, data centres and bidirectional power flows, the low-voltage network can no longer be managed as a passive part of the electricity system. It needs to become observable, understandable and operable.

Low-voltage networks were originally developed for a relatively straightforward electricity system:

• Power flowed from medium voltage towards the end customer.
• Demand followed relatively predictable patterns.
• Network topologies were predominantly radial.
• Protection relied heavily on fuses.
• Automation and monitoring were limited.
• Many network issues only became known through customer reports or field inspections.

That model is changing. Distributed solar generation can create reverse power flows and increase voltage at specific points on the network. Electric vehicles and heat pumps introduce new loads, increase demand coincidence and create localised peaks. The electrification of industrial processes and the growth of data centres are placing additional pressure on transformers, LV feeders and switchboards.

At the same time, DSOs need to:

• Maintain quality and continuity of supply.
• Integrate new generation and demand.
• Reduce technical and non-technical losses.
• Improve network resilience.
• Prevent failures and extend asset life.
• Prioritise investment using objective criteria.
• Make better use of the capacity of existing infrastructure.

The physical network has evolved faster than the visibility available to those operating it. The challenge, therefore, is not simply to deploy more equipment, but to turn the data already available into operational insight. This is the role of advanced low-voltage monitoring.

Advanced low-voltage monitoring, is the coordinated combination of devices, communications, processes, data and software applications that enables a DSO to understand and analyse the behaviour of its LV network.

An advanced low-voltage monitoring environment can integrate information from:

• RTUs and monitoring equipment installed at secondary substations.
• Voltage, current, power, energy and temperature sensors.
• Data concentrators and smart meters.
• AMI-HES smart metering systems.
• GIS and network asset inventories.
• SCADA, ADMS, OMS and other corporate systems.
• Data platforms and maintenance applications.
• Field campaigns and information collected on site.

However, simply integrating these sources is not enough. The real purpose of advanced low-voltage monitoring is to connect measurements, assets, topology, events and operating conditions in order to answer specific operational questions:

• What is happening on the network?
• Where is the problem located?
• Which customers, feeders or assets may be affected?
• Is this an isolated incident or part of a recurring pattern?
• Which action should be prioritised?
• Does the network need reinforcement, or could its configuration be optimised?
• Which investment would deliver the greatest operational or economic impact?

A mature advanced low-voltage monitoring capability therefore goes beyond simply collecting and visualising network data. It helps DSOs understand and manage the network.

LV is where most new distributed generation and loads are being connected. It is also one of the network levels that has historically had the least instrumentation and where maintaining a complete, accurate and up-to-date digital representation can be particularly challenging.

Many DSOs still face issues such as:

• Incomplete or outdated network topologies.
• Incorrect associations between customers, meters, phases and feeders.
• Discrepancies between the physical network and the GIS model.
• Gaps in measurements or data captured at different levels of granularity.
• Devices with communication or operational issues.
• Manual processes for locating and diagnosing incidents.
• Information distributed across multiple systems and departments.

Without reliable observability, network operation tends to remain reactive. A problem becomes visible only once it causes an outage, a customer complaint, an isolated alarm or the need for a field visit.

Advanced low-voltage monitoring enables DSOs to move towards a more preventive and evidence-based operating model. This does not mean that every problem can be anticipated. It means that teams have more context to identify issues earlier, reduce uncertainty and act with greater precision.

Having thousands of voltage, current or energy readings does not automatically make a network observable.

To interpret a measurement correctly, it is necessary to know:

• Which device generated it.
• Which asset or network point it relates to.
• Which transformer, feeder and phase it is associated with.
• Which customers depend on that part of the network.
• What the network configuration was at that particular time.
• Whether the data is complete and reliable.
• Which events occurred before and afterwards.
• Whether similar patterns can be found in other assets.

An effective advanced low-voltage monitoring strategy therefore needs to combine several layers.

• Reliable electrical measurements

Monitoring needs to provide reliable voltage, current, power, energy and load measurements, with the appropriate level of granularity for each use case.

• Events, alarms and incidents

An advanced low-voltage monitoring platform should help identify and contextualise different types of alarms and events, including overloads and saturation, phase imbalances and voltage deviations.

The value increases when alarms are no longer treated as isolated events, but are grouped, prioritised and linked to the relevant network assets and locations.

• Up-to-date topology

Topology is one of the foundations of any network analysis. Understanding the actual relationship between a meter, phase, feeder, transformer and network segment is essential for interpreting measurements correctly and enabling advanced use cases.

• Device and communications status

The reliability of analytics depends on the reliability of the infrastructure generating the data. Monitoring the health and communications status of devices is therefore an integral part of monitoring the network itself.

• Data quality and governance

Data quality is not a one-off cleansing exercise carried out before an application is deployed. It is a continuous operational process.

A reliable platform should help determine when data can be trusted and used, when it needs to be reviewed and what impact poor-quality data could have on subsequent analytics.

The journey from a largely passive LV network to one that can be actively managed can be summarised in three stages.

1. Measure what is actually happening on the network

The first step is to collect sufficient, consistent information. This includes electrical measurements, events, alarms, communications status, configurations and data generated by AMI infrastructure and field devices.

The DSO needs to know which devices are available, when data was last received and whether the information is of sufficient quality for the intended use case.

Without a reliable data chain, any subsequent analytics will also be less reliable.

2. Understand what is happening

Measuring is not the same as understanding.

The second stage involves placing the information in context by connecting it with network topology, assets, customers and historical network behaviour.

Analytics can help DSOs:

• Distinguish a genuine anomaly from a measurement error.
• Detect recurring patterns.
• Identify relationships between events.
• Detect imbalances or losses.
• Analyse the likely root cause of an incident.
• Determine whether an overload is temporary or structural.
• Identify issues that are not visible from the secondary substation alone.
• Compare operational or planning scenarios.

This combination of electrical engineering expertise and analytics is what turns data into grid intelligence.

3. Act with greater precision

The final step is to use that intelligence to improve network operation.

Advanced low-voltage monitoring can support decisions such as:

• Prioritising inspections and field work.
• Assigning incidents to the appropriate person or department.
• Identifying risks before they result in critical failures.
• Recommending phase changes to reduce imbalance.
• Identifying transformers or feeders at risk of overload.
• Locating areas with high losses.
• Reviewing network topology associations.
• Improving maintenance planning.
• Assessing whether an investment is necessary and where it will deliver the greatest value.
• Preparing the network for new loads and distributed generation.

A mature advanced low-voltage monitoring capability does not replace the expertise and judgement of technical teams. It gives them a more complete and reliable basis on which to make decisions.

Medium-voltage monitoring provides visibility into the behaviour of the different assets that determine supply to secondary substations. It can help identify overloads, voltage drops, operational constraints, configuration changes, outages and areas with limited capacity.

However, understanding the real impact of these conditions requires them to be connected with the secondary substations, LV feeders and customers that depend on that part of the network.

This is where integrating MV and LV information becomes particularly valuable.

One of the risks of grid digitalisation is deploying equipment without first defining which decisions that equipment needs to support.

An advanced low-voltage monitoring strategy should not begin by asking how many sensors need to be installed. It should begin by asking:

• What problem does the DSO need to solve?
• What data is already available?
• What is the quality of that data?
• Which systems need to be integrated?
• What information is genuinely missing?
• What level of accuracy does the use case require?
• What operational or economic impact is expected?

In some cases, data from smart meters may already be sufficient. In others, additional monitoring at secondary substations, further sensors or field validation campaigns may be required.

The aim is not to deploy more technology for its own sake. It is to deploy the technology that is actually needed while extracting maximum value from the existing installed base.

Ariadna supports DSOs throughout their digitalisation journey, from integrating and improving existing data through to applying it across network operation, maintenance and planning.

Through a progressive, modular approach tailored to the reality of each DSO, we connect systems, assets, measurements and electrical engineering expertise to turn fragmented information into a more reliable view of the network and into operational and investment decisions with tangible impact.

Advanced low-voltage monitoring can deliver value across different areas of a DSO. Its economic impact can translate into lower OPEX, fewer unnecessary field visits, energy recovery, longer asset life and more accurate CAPEX prioritisation.

Operations

• Greater visibility of the actual state of the LV network.
• Earlier detection of network issues.
• Better prioritisation and allocation of interventions.
• Less reliance on manual diagnostics.
• Better coordination between teams and systems.

• Identification of assets and devices showing abnormal behaviour.
• Reduced need for non-targeted inspections.
• Risk-based maintenance prioritisation.
• Earlier detection of overloads, imbalances and asset deterioration.

• More reliable energy balances.
• Identification of areas showing abnormal deviations.
• Better prioritisation of inspections.
• Improved correlation between consumption, topology and network measurements.

• Better understanding of actual network loading.
• Identification of available capacity and constraints.
• Assessment of the impact of new loads and generation.
• Comparison of reinforcement, reconfiguration and operational alternatives.
• Data-driven justification of investment decisions.

• Integration of heterogeneous data sources.
• Monitoring of device and communications status.
• Fewer manual processes.
• Greater traceability.
• Better use of existing AMI, GIS, SCADA and other systems.

Advanced low-voltage monitoring is not simply about installing equipment at secondary substations or adding new dashboards. It is a progressive operational capability that enables DSOs to observe, understand and manage their networks with greater precision.

The difference between a network that is merely monitored and one that can be effectively managed lies in the ability to connect data with topology, assets, incidents and the decisions the DSO needs to make.

As electrification, distributed generation and bidirectional power flows increase, so does the need for a reliable view of the low-voltage network and for this information to be connected with medium-voltage monitoring.

DSOs that develop a robust advanced low-voltage monitoring strategy, integrated within a coherent view of both network levels, will be better equipped to operate their existing infrastructure, identify the origin and impact of network issues and anticipate future requirements.

At Ariadna, we support DSOs throughout this process, from data integration, validation and contextualisation to advanced analytics, grid digital twins and decision support. Our approach connects medium- and low-voltage information to build a more complete, observable and operable view of the distribution network.