Wildfire (bushfire) analytics with PLP Enterprise
Catastrophic losses to life and property from bushfires / wildfires have emphasised the need for a more strategic approach to managing fire. A key requirement of such an approach is a means of quantifying overall bushfire risk and prioritising the work required to remediate the risk. Our PLP enterprise system allows for an integrated approach to wildfire risk analytics by leveraging a cloud-based, structural modeling big data platform, backed by advanced predictive algorithms.
Our solution is based on the principles discussed in ISO:31000:2018 Risk Management Guidelines, which provides a common approach for managing any type of risk. Our solution focuses primarily on the risk assessment and seeks to
Develop capabilities in predicting an asset’s overall wildfire related risk; and
Given an asset’s risk, prioritize work, repairs and/or replacements to minimize its risk potential
The Risk Management Process
In the context of ISO:31000:2018, the risk management process involves identifying, analysing, evaluating and treating wildfire related risk. Our PLP enterprise platform provides a systematic and consistent approach to risk assessment. It helps identify risk by generating a structural model of the network, performs risk analysis by testing various scenarios and helps prioritize risk by integrating asset characteristic data.
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PLP Enterprise risk assessment capabilities
At its core, PLP Enterprise solution combines data from LiDAR, GIS, asset management systems and other sources to produce an accurate physical model of a utility network. This structural model can be used to proactively address and mitigate the threat of wildfires caused by electrical infrastructure failures and associated ignitions. Specifically, it helps utilities maintain appropriate clearances between their lines' and vegetation, predicting an asset's overall wildfire-related risk and prioritizing corrective works to minimize potential wildfire ignitions.
This is done by
Integrating LiDAR and GIS data to produce a structural model of the network
Simulation of bush fire risk under various wind, temperature, and other conditions, taking into account the actual blow out of conductors and physical forces on the poles
Integration of asset characteristics data and operational data for accurate risk assessment
Utilization of analytics to prioritize mitigation efforts and best treatment options on the highest risk assets, especially in higher wild fire prone areas
Once the tasks have been classified and prioritized for work, PLP enterprise can then integrate with other systems such as vegetation management, field force mobility management or task management systems for implementation and tracking. PLP enterprise can also take input from these systems once all the tasks are complete to provide comprehensive monitoring and review capability.
A further extension of this platform would include streaming data from smart meters, SCADA systems, etc., and use machine learning to identify early warning signs of potential faults.
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