%20small.png)
Power is one of those things we only really notice when it’s NOT working. Most people don’t celebrate flicking on a light switch or running a load of laundry. Unless, that is, the power is out. In those cases, flickering lights and the AC booting up are reasons to rejoice.
However, most of the time, when electricity is flowing, we forget about it.
This is because most electric companies are primarily concerned with providing a continuous flow of uninterrupted power to their customers. One of the ways they do this is through early fault detection methods.
Early fault detection (or EFD) proactively identifies potential issues or faults within the power grid to fix problems before they cause outages or failures.
Detecting Potential Faults
EFD efforts require utilities to continuously monitor their electric distribution systems, paying special attention to things like voltage, current, equipment temperature, and more using sensors and data analytics.
When anomalies occur (a surge in current, or a drop in voltage for example), the early fault detection monitoring system detects and alerts teams that a deviation could indicate an impending outage. With this info, utilities can assess the severity of the issue and choose the best course of action. Sometimes this looks like dispatching field crews, adjusting power flow to relieve stress, or isolating fault sections. In extreme conditions, utilities may choose to initiate load shedding or controlled outages to protect the wider system from faults.
Early Fault Detection for Effective Distribution
When we consider the massive systems that are used to bring power from generators to our light switches, the need for early fault detection becomes abundantly clear.
Electric distribution systems are complex. From a 50,000-foot view, power is pulled from plants using transmission lines, which then connect to substations, and feed that power to homes and businesses using distribution. But as soon as we start to zoom in, electric systems start to get more and more complicated.
Each component of the grid can create complications. Faults within the transmission network can affect generation and distribution with wide-spread impacts. Issues within radial distribution networks are difficult to pinpoint because they’re so interconnected, and even customer equipment (such as capacitor banks or large motors) can impact the power distribution network.
Not to mention, failures in one area can cascade to cause widespread outages. Downtime isn’t just a financial cost; there’s equipment damage, service interruptions, and construction work that can have long-term ramifications on the health of the grid.
EFD helps utilities take action before minor issues like a small drop in voltage can create major impacts. With early fault detection, power companies can improve reliability and reduce maintenance costs. Plus, fewer outages and better-managed distribution take less of a toll on assets, prolonging their life.
Detecting Early isn’t Easy
While important and necessary, EFD isn’t easy. Advanced monitoring is expensive and time-consuming, and can be a huge burden on utility teams. Some major challenges to early fault detection include:
Managing massive volumes of data.
Power systems generate tons of data with everything from customer usage to peak demands to current flow to DER operations. With all that data, it can be difficult to transmit, store, and analyze in real time.
To detect faults early, power companies need tools that are incredibly fast at analyzing and sifting through all the data. Utilities need edge computing to process data locally, using high-bandwidth communication networks and cloud platforms for scalable storage and analytics.
Dynamic data and complex distribution.
Part of the reason there’s so much data is because there are so many variables to distribution design. Normal operating conditions can vary with different power generation sources, geographic factors, demographics, usage, and more.
Advanced pattern recognition can help model system behavior to clue teams in when deviations occur, and even adapt to changing conditions.
Updating old infrastructure.
Many distribution networks operate using aging assets, which weren’t designed for monitoring and can be hard to integrate with new, advanced technologies. Retrofitting sensors within the power grid is costly.
Strategic deployment of sensors at critical points helps monitor network performance without requiring mass overhaul and updates, but utilities need the right planning and deployment tools to pinpoint the best places.
Dual Purpose of Data
The data required for robust EFD programs shouldn’t end at fault detection. Power flow, voltages, equipment loading, and operational performance can be used to validate and calibrate network models for planning and design.
Insights from that data also help reveal stressed areas and overloaded lines that may need redesigning and help provide valuable info on what areas need priority. Accurate, data-driven models help engineers plan out and optimize network topologies, component sizing, and protection measures for a safer grid.
On the flip side, a robustly designed network also helps support early fault detection, because systems with proper load capacity and requirements, with properly sized equipment, provide critical measurement points for EFD and sensor deployment.
Early fault detection helps ensure consistent and reliable power and services, but like everything worth doing, it’s not easy to accomplish. The right data collection, mapping, engineering, and analysis tools will help utilities build robust electric distribution systems that include EFD technology for ongoing services.
Thanks for reading! Did you know our data collection, engineering, and workflow management toolsets function for distribution management and design? We’re constantly building and creating new functionality to ensure Katapult Pro is a living software that meets whatever needs you might have. Learn more by shooting us a message at contact@katapultengineering.com!
Comments