Transmission and Distribution
Utility companies face a common problem: Keeping up with the conditions of their transmission and distribution network. Especially their aging infrastructure.
Some wood power poles have been in the ground for more than 50 years, performing well but withstanding the worst of Mother Nature. They’re at significant risk for structural failure. And a downed pole – and powerlines – can lead to outages and wildfires.
How do utilities see what’s happening with their field assets? Especially in the most remote locations? Sending out a regular inspection crew is costly and inefficient. Traditional sensors need expensive power and communications infrastructure. Failures happen despite regular maintenance.
Utilities need “eyes.” Eyes to see the health of their assets, to see the conditions of the surrounding environment, to see failures and emergencies. And to demonstrate that a downed line didn’t cause a fire.
The eyes come in the form of high-quality data. And visuals.
Taking the first step: Hayden Data’s technology
Our sensors continuously monitor utility poles and other assets and provide the data in near-real-time. They measure a set of parameters and report all anomalies. Repair and replacement crews are dispatched before the asset fails. Healthy assets require no attention, other than being monitored on our awareness platform. This is conditional maintenance, instead of scheduled maintenance.
Introducing ‘smart assets’ into the transmission and distribution network
Power utilities can now use artificial intelligence, machine learning, and advanced analytics to perform predictive maintenance. Our awareness solution combines the data of an asset’s last-reported condition and compares it to the historical data from thousands of similar assets. Assets under similar environmental conditions and stress loads can be used to predict potential failures in the network. They become “intelligent.” Scheduled and conditional maintenance plans can become predictive.
The Hayden Data Awareness Platform can also recommend maintenance intervals based on replacement supply chains and area crew capacity.
- Asset knowledge in near-real-time
- Preventive maintenance for individual assets
- Impact awareness of environmental conditions
- Safer communities and insurability of power utilities
- Residual strength and structural health monitoring
Hayden Data smart sensors continuously monitor the health and residual strength of infrastructure assets. Users can execute preventive and condition-based maintenance on assets before they fail. They allocate their maintenance resources where needed, when needed, instead of through inefficient and costly scheduled inspections. Our AI and ML technologies let users optimize the cost and effectiveness of their maintenance and inspection programs.
- Vegetation management
Cameras installed on our sensor enclosures provide images of areas along lines and within clearance areas. Utilities can quickly spot vegetation encroachment and allocate management resources. Costly aerial and ground-based observations are eliminated. Photos can be taken at timed intervals or on command.
- Dynamic Line Rating (DLR)
Transmission line capacity, according to some estimates, can be increased by as much as 40% depending on environmental conditions. The key is having the ability to monitor those conditions along lines and entire networks.
Hayden Data sensors measure conditions such as temperature, humidity, wind, rain, UV radiation, and more. Users can make informed decisions about dynamically rating power lines.
- Fire/smoke/gas leak/flood/earthquake detection
Hayden Data smart sensors detect fire, smoke, gas leaks, monitor rainfall, and can detect seismic activity. Deployed along power lines, we can create a complete environmental hazard early warning and rapid response system.
High-quality data lets utility companies take a more active role in preventing infrastructure failure and negative public perceptions.
With Hayden Data technology, power utilities become part of a safety solution.
You must be logged in to post a comment.