Power Utility companies across the world are struggling with aging infrastructure throughout their network. In the case of wooden power poles, there have been some poles found to be part of the distribution network over 50 years since installation. The aged poles in rural and regional areas pose the most significant risk for starting bushfires should they suffer structural failure. They will also take longer to remedy due to their remote locations.
Distribution of pole failure within high population areas represents its own risk.
Power outages to a broader consumer base, impact on critical industries, and more extensive exposure to compensation claims due to the economic impact on consumers.
Thanks to low cost, ease of installation, and favorable performance characteristics, wood poles are expected to remain the primary structures supporting the distribution grid going forward. Investment in the utility’s wood poles equates to an investment in keeping the increasingly smart grid secure and resilient. Every utility should have a robust, well-designed life extension program in place for their pole plant that is continually monitored and developed as part of a Predictive Maintenance strategy.
One of the most significant inhibitors to achieving this vision is that Utilities are large infrastructure-heavy organizations that have lost the ability to embrace innovation. They struggle to onboard additional tools and solutions that could assist them in understanding the current state of their critical pole infrastructure.
If they were able to meet this challenge, they could begin to move from the Scheduled Based Maintenance strategies that they currently employ and through to a Predictive Based Maintenance that would enable them to recognize the cost savings that these transitions have had in other industries. Transitioning from Scheduled Based to Condition-Based maintenance, only replacing those items that need changing due to an adverse change in their structural stability, has seen savings of 20% to 25% across other industries.
The further move from Condition Based to Predictive Based maintenance, replacing those items that it is predicted will fail, has seen additional cost savings between 20%-30%.
This strategy combines to deliver cost savings of close to 40% through moving from the traditional Scheduled Based Maintenance through to Predictive Maintenance.
The sensors that Hayden Data can provide today in our Gen 1 technology can deliver Condition Based Maintenance. By continually monitoring Power Poles and reporting in near real-time when a set of measured parameters reaches an unacceptable threshold. Maintenance can then be scheduled to remediate the issue before the imminent failure of the asset, allowing maintenance crews to focus only on those assets that require support and replacement and bypassing assets that are still in sound working condition.
Hayden Data’s Generation Two technology is road mapped to combined with advanced analytics to deliver the opportunity for our customers to move from Condition Based Maintenance to Predictive Maintenance. Thus, realise the opportunity to transition from Schedule Based to Predictive Based maintenance where possible.
We anticipate that over time, our Predictive Maintenance solution will combine the data of an asset’s last reported condition and compare it to the historical data from thousands, if not hundreds of thousands, of similar poles subjected to comparable environmental conditions and stress loads to predict the date of failure. The software will determine the recommended period for maintenance based on known parameters around supply chain and crew capacity in a specific area.
When added to the data and information knowledge repository that Hayden Data will gain across their global deployments, the cost reductions that can be achieved through reduced maintenance costs can easily justify the investment in rolling out the Hayden Data solutions. These could be staged and modularised depending on the risk profile of an area and cost-benefit analysis.
The emerging analytics technologies pipeline supports the implementation of applications for known uses of data but also enables the discovery of new use cases via the exploration of available measurement data. The reasons for utilities’ sluggishness in traversing this pipeline were highlighted extensively in the 2017 “Learning from Data Grid of the Future” paper. Fundamentally, utilities are tied to legacy platforms, ill-equipped for analytics, and need the right tools to process and analyze time-series sensor data at scale.
Most Power Utilities are also struggling with antiquated cost models whilst experiencing a paradigm shift within their industry. The influx of Renewable Energy and large utility based battery storage, the largest being in South Australia at the 129MWh Hornsdale Power Reserve (https://hornsdalepowerreserve.com.au/), is driving the need to be more innovative and to adopt change across their networks.
Most importantly, this slow traversal of the analytics pipeline is putting the protection of the grid at an ever-increasing, yet unnecessary risk. Due to the increasing nature and volume of this risk, it has removed the ability to insure against, as the insurance companies have no way to gain an understanding of the threat or to validate a company’s compliance in monitoring the risk on an ongoing basis and then taking remediation action to mitigate risks when it occurs.