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Smart Grid Sensors: Improving Reliability for Distribution Grids During Energy Transition and Grid Modernization

Smart Grid Sensors: Improving Reliability for Distribution Grids During Energy Transition and Grid Modernization

SMART GRID


In recent years, distribution networks have seen many new challenges in operation and demand which leads to increasing criticality of medium and low voltage grid assets. This drives the need for visibility, efficiency, and automation, while Smart Grid sensor technologies are rapidly improving solutions for distribution system operators (DSOs) to meet this need.
Power utilities face increasing challenges associated with rapid transformation of power generation and consumption. Distributed Energy Resources (DER), including renewables, cause stresses on grid assets. Climate change and associated extreme weather events, as well as an aging grid infrastructure, add to growing concerns, while digitalization with Smart Grids contributes to solutions for improved reliability.
Over the past decades, electrical infrastructure investment has been prioritized for transmission grids due to criticality of high voltage assets. However, in recent years, distribution networks have seen many new challenges in operation and demand which leads to increasing criticality of medium and low voltage grid assets. This drives the need for visibility, efficiency, and automation, while Smart Grid sensor technologies are rapidly improving solutions for distribution system operators (DSOs) to meet this need.
Utility asset managers face pressure from governments and consumers to preserve and enhance grid performance while maintaining assets that are often operating beyond designed life. This is no easy task during grid modernization efforts along with ever-increasing electrical loads combined with complex power flows.

Sensors can be deployed to help minimize fault durations and even prevent faults.

Solutions are sought to improve key performance factors such as SAIDI (System Average Interruption Duration Index), and SAIFI (System Average Interruption Frequency Index). This is accomplished by achieving quicker reaction time to locate and repair faults (SAIDI) and reducing frequency of faults with the use of data for predictive maintenance techniques (SAIFI). These indices are measured and reported with rising scrutiny in recent years, as the pressure for improved reliability is ever-increasing. Not only do enhancements of these metrics result in dependable power delivery to consumers, but they also result in reduced operating costs. Sensors can be deployed to help minimize fault durations and now even prevent faults, thus improving SAIDI, SAIFI, and maintenance expenses.

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Lowest Hanging Fruit
Utility distribution staff must consider efficient maintenance and the most cost-effective strategies to improve SAIDI and SAIFI and are focussing efforts on reliability on overhead distribution lines. Outages caused by faults in overhead lines are often an inevitable part of operations as these critical assets are exposed to weather, mechanical stresses, contamination, aging equipment, vegetation, and wildlife.
Many utilities invest extensive efforts in replacing overhead lines with underground cables to significantly improve reliability. There is a correlation in relation of overhead lines in the network to SAIDI, and as such, regions with more underground cables typically enjoy better performance of their grid. However, it is not always practical to replace overhead lines with underground cables due to high costs, particularly for rural and remote areas with distribution lines covering large geographical distances. Undergrounding projects are expensive and time consuming, requiring a large labour force commitment that is not always readily available. Therefore, overhead lines remain as a necessary means of delivering power to customers by many utilities globally.

To improve the reliability found in overhead line networks, sensors such as Fault Passage Indicators (FPIs) or Fault Current Indicators (FCIs) are deployed to identify general fault locations as quickly as possible, which reduces time necessary for Fault Location, Isolation, and Service Restoration (FLISR).

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To improve the reliability found in overhead line networks, sensors such as Fault Passage Indicators (FPIs) or Fault Current Indicators (FCIs) are deployed to identify general fault locations as quickly as possible, which reduces time necessary for Fault Location, Isolation, and Service Restoration (FLISR). Traditionally, an FPI or FCI is a simple instrument that measures current amplitude. Once current reaches a particular threshold (or exceeds a step-change), a fault is identified and an indication (LED) on the device alerts field technicians to the general location of a fault. These are often installed in strategic locations, for example at junctions of line branches. This helps service restoration crews to know where to start looking for a fault. Then the search begins, In an ideal situation, the faulted powerline runs parallel to a road so that vehicles can be deployed to conduct the search. If this is not the case, all-terrain vehicles, drones, or even helicopters are dispatched, and sometimes even on-foot fault searching is necessary.
Several enhancements in FPI and FCI technology have helped to reduce search time. For example, some devices can now communicate and send an SMS message or alert control room operators via SCADA/DMS with fault localization data, leading to a better reaction time over searching for a flashing light in the field.
Challenges for FPIs and FCIs
Even after advancements in notification of fault location, additional challenges remain for distribution operators. For example, DER has transformed the grid from a radial, one-directional network to a complex grid with multi-directional power-flow dependant on factors such as power consumption or presence of sunlight or wind at a given time of day. For this reason, fault direction is not a simple parameter to detect. Additionally, comparing current amplitude versus a simple threshold is not sufficient for detecting or locating high impedance earth faults, especially on compensated (Peterson coil) networks. Another challenge for distribution operators is the need to provide high service quality and reduce the number of faults (SAIFI) by identifying weak elements in the grid in advance with the use of predictive maintenance. Simple FPIs cannot provide a solution for this need.
Modern Innovations of Grid Sensors
The solution for finding fault direction and identifying high impedance earth faults, transient earth faults, or intermittent earth faults can be found in the algorithms found in power system protection relays or intelligent electronic devices (IEDs). Zero sequence current, phase relationship, harmonics and other electrical quantities are essential for fault algorithms. Computing these values requires three-phase, time-synchronized measurement of amplitude, frequency, phase angle, and duration with sufficient sampling rate, resolution, and accuracy. For protection relays, this is no problem, hence the existence of three-phase protection devices.

DER has transformed the grid from a radial, one-directional network to a complex grid with multi-directional power-flow dependant on factors such as power consumption or presence of sunlight or wind at a given time of day. For this reason, fault direction is not a simple parameter to detect.

The solution for finding fault direction and identifying high impedance earth faults, transient earth faults, or intermittent earth faults can be found in the algorithms found in power system protection relays or intelligent electronic devices (IEDs).

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However, this exposes a challenge for simple FPIs and FCIs which measure only single-phase current amplitude.
Conventional FPIs and FCIs simply measure single-phase current amplitude, whereas modern grid sensors now include precise synchronization, communication, and advanced algorithms carried out via edge computing with hardened cybersecurity protocols. With these functionalities, grid sensors can be deployed as sets of three sensors at multiple locations across power lines, providing wireless communication of precise location and fault data, even for high impedance earth faults on compensated networks.

 

 

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Modern technology integrates GPS location information, meaning sensor locations and faults can be plotted directly onto a digital map, allowing technicians to navigate more easily and quickly to fault locations.
Since grid sensors now operate with better fault detection and localization capabilities due to enhanced measurement techniques and computing algorhims, why stop at fault location? With precise measurements and collection of data for trending, even transient earth faults and self-extinguished arcing events are identified. These types of faults are often not even detected by utilities or their customers but are often indicators for future outages or asset failure.

Grid sensors can be deployed as sets of three sensors at multiple locations across power lines, providing wireless communication of precise location and fault data, even for high impedance earth faults on compensated networks.

This means weak spots in the grid can be identified and analyzed over time, and this data can serve as an input into condition-based maintaintance strategies in order to prevent permanent faults and outages, thus reducing SAIFI.
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Additionally, vegetation growth and encroachment can be identified by modern line sensor data so that growth management can be conducted before catastrophic events such as wildfires occur. Power quality parameters, such as harmonics, can be measured in order to identify extra stresses on the grid, for example from renewable resources. This means that grid sensors are transitioning away from simple FPIs and are now able to additionally serve as local power quality monitors with data that can be used for predicitive grid analytics.

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Example of self-extinguishing earth fault now detectible for analysis thanks to modern grid sensing algorithms.

The Future
Thanks to digitalization, edge-computing, Artificial Intelligence (AI), cloud computing, and internet of things (IoT), grid sensors and their associated data can be expected to integrate more with advanced DMS systems. Ongoing efforts for interoperability allow data between sensors and other assets to be shared, for example with power system protection schemes, self-healing grids, energy management, metering, and more. Artificial intelligence and pattern recognition contribute to automated analytics for increased asset reliability and efficiencies.

 

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David Eckert
David Eckert is Sales Manager for Africa and Europe for Megger’s Grid Performance Solutions division. David has served in several technical and sales roles including Application Engineer for power system protection testing, Regional Sales Manager for substation testing instruments (transformer, circuit breaker, relay, battery), and Sales Manager of solutions for insulation condition monitoring and diagnostics. Prior to Megger, David served in roles as Plant Electrical Reliability Engineer and Business Analyst in the industrial production sector.
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