The reliable operation of energy infrastructure depends not only on system stability and cybersecurity, but also on effective physical protection. Substations, solar parks, and cable transition points are critical components of the energy system. Their failure can lead to supply disruptions, economic losses, and safety risks.
Securing these facilities is challenging due to their size, distribution, and level of automation. Many sites are remote, cover large areas, and operate without permanent on-site personnel. As a result, continuous and automated monitoring is required to detect unauthorized access or interference at an early stage.
Common technologies such as video surveillance, motion sensors, thermal cameras, and radar are widely used but have practical limitations. These include sensitivity to environmental conditions, frequent false alarms, and – in the case of camera-based systems – restrictions related to data protection.
Operating Principles of 3D LiDAR Sensors
LiDAR (Light Detection and Ranging) is an active sensing method that measures distances by emitting laser pulses and measuring their time of flight after reflection. This process creates a three-dimensional representation of the environment, known as a point cloud.
Each point contains spatial information, allowing objects to be detected and analyzed based on their position, size, and shape. Depending on the system, additional information such as reflectivity or movement can also be derived.
Because LiDAR inherently provides its own light source, it operates independently of ambient lighting. This enables consistent performance under day/night cycles and low-light conditions. The technology is also relatively robust under typical weather conditions such as rain or fog, although performance may decrease in extreme environments.
A key characteristic for security applications is the ability to distinguish relevant objects (e.g., humans or vehicles) from non relevant ones (e.g., small animals or vegetation) using geometric features, such as object size or number of detected points, rather than image based identification.
System Design and Deployment in Energy Facilities
In security applications, LiDAR sensors are typically installed to monitor defined areas around critical assets. These can include fenced perimeters of substations, access points, or the inter-row spaces in photovoltaic installations.
Using dedicated software, virtual detection zones can be configured within the sensor’s field of view. These zones can be adapted to the specific site and use case. Parameters such as object size, movement, or time spent in a predetermined zone can be used to define when an event should be triggered.
This allows different levels of response depending on the situation. For example, a system can generate an early warning when a person approaches a restricted area, and escalate to an alarm if a boundary is crossed. At the same time, smaller objects such as animals or vegetation can be filtered out to reduce false alarms.
Most LiDAR systems can be connected to existing monitoring and control infrastructure through standard interfaces. This enables integration into central control systems and supports coordinated responses.
Early Detection Capabilities: Insights from a Cable Infrastructure Incident
The relevance of early detection mechanisms in physical infrastructure security has been underscored by a recent incident in Berlin. In January 2026, a targeted arson attack on a cable bridge caused a large-scale power outage.
In this incident, unknown perpetrators deliberately set fire to multiple high-voltage cables located on a bridge over the Teltow Canal. The fire destroyed several critical transmission lines, forcing their shutdown and interrupting the power supply to approximately 45,000 households, 2,200 businesses and several hospitals and nursing homes. Repair and restoration of the damaged infrastructure required several days, resulting in the longest power outage in Berlin in recent years.
The consequences extended beyond the immediate loss of electricity. Heating systems, telecommunications infrastructure, and public services were affected, highlighting the systemic impact of localized physical attacks on energy infrastructure. The incident also demonstrated that such attacks can be carried out with comparatively low technical effort while causing disproportionately large disruptions.
Instead of focusing solely on the protected asset (e.g., cables or substations), LiDAR sensors continuously monitor the surrounding space and analyze object movements withing defined zones.
From a technical perspective, the attack illustrates a key limitation of many existing security approaches: detection often occurs only after damage has already been inflicted. In the Berlin case, the fire was identified only once the cables were already burning, at which point preventive measures were no longer possible.
LiDAR-based monitoring systems address this gap by enabling the detection of pre-incident activity. Instead of focusing solely on the protected asset (e.g., cables or substations), LiDAR sensors continuously monitor the surrounding space and analyze object movements within defined zones. This allows the identification of potentially critical situations before direct interaction with infrastructure occurs.
Applied to the Berlin scenario, such a system could have been configured to detect:
- unauthorized approach to the cable bridge,
- presence in restricted or typically unoccupied areas,
- unusual dwell times or repeated movement patterns near critical components,
- attempts to access or climb infrastructure elements.
Because LiDAR captures spatial and temporal information, it enables the analysis of trajectories and behavior rather than isolated events. For example, a person entering a restricted area and remaining there for an extended period could trigger a staged response, ranging from early warning to alarm escalation.
While such systems cannot prevent all forms of sabotage, they enable earlier detection and intervention. In scenarios like the Berlin attack, this could provide a time window for response measures before physical damage occurs, thereby reducing the likelihood or impact of disruptions to critical infrastructure.
System Performance and Implementation Considerations
As with any sensing technology, performance depends strongly on system design and configuration. Reliable detection requires that the sensor layout, alarm logic, and system interfaces are adapted to the specific site conditions. Key aspects include:
Sensor placement: Objects such as transformers, fences, steel structures, or vegetation can block the sensor’s field of view and create blind spots. For this reason, sensor locations must be selected carefully to ensure that all relevant areas are covered and that overlapping fields of view are available where necessary.
Environmental conditions: Heavy rain, snow, dense fog, or airborne dust can reduce measurement quality by scattering or attenuating the laser signal. Although LiDAR is generally more robust than camera based systems under poor weather conditions, local weather patterns and possible counter-measures should still be considered during system planning.
Parameter configuration: Detection thresholds must be adjusted to balance sensitivity and false alarm rates. For example, object size, movement speed, dwell time, or direction of travel can all be used to determine whether an event should trigger an alarm. Incorrect settings may either generate unnecessary alarms or reduce the probability of detecting relevant events.
Implications for Infrastructure Protection
The physical protection of energy infrastructure is becoming increasingly important as power systems become more decentralized, automated, and interconnected. Solar parks, substations, cable transition points, and remote switching stations are often distributed across large areas and operate with little or no on-site personnel. At the same time, these facilities play a critical role in maintaining grid stability and ensuring continuity of supply.
Against this background, physical security systems need to move beyond simple perimeter monitoring and support earlier identification of unusual activity around critical assets. For energy operators, this means not only detecting that an intrusion has occurred, but also identifying behaviors that may indicate preparation for sabotage or interference before damage takes place.
3D LiDAR-based monitoring can contribute to this by providing continuous coverage of critical areas, even in remote or unmanned locations. When combined with central control systems and existing operational processes, it can help improve response times and support a more preventive approach to infrastructure protection. As regulatory requirements for critical infrastructure continue to evolve, such capabilities are likely to become more relevant for operators of energy facilities.
When combined with central control systems and existing operational processes, 3D LiDAR can help improve response times and support a more preventive approach to infrastructure protection.
After obtaining his Master’s degree in phys ics and electrical engineering from the Tech nical University of Munich, Andreas Bollu joined the Blickfeld’s optics team, where he was involved in detector development and simulation. In 2021, he moved into the role of Team Lead Security Solutions, responsible for business development and product management. Since 2024, he has been serving as the Vice President of the Security Unit.
This article was originally published in the June 2026 issue of the Grid Modernization and Flexibility magazine.
View Magazine
