Vector Electricity Asset Management Plan— 2020 Update
3.2
Maintenance Models Duration from outages captures all the effort and tasks involved with identifying and reaching the fault location, making safe and isolating the faulted area, partially restoring service (i.e. switching to reduce the outage area) and the steps involved with repairing the fault. This undertaking is a key asset management process for managing both inherent and environmental causes. To support the SRMP, our operating models for each maintenance type have been reviewed and updated where required.
REACTIVE MAINTENANCE
In order to bring more focus to repair and restoration time we have put effort into breaking down the Reactive Maintenance time-line of activities.
The resulting six steps have then been assessed for options to improve the time taken for each step. Using data analysis completed both internally and by the FSPs, solutions were prioritised by understanding which would have the greater impact on reducing CAIDI. In both regions of our network, different solutions are needed based on each network’s topology and characteristics. Solution options include resourcing levels and structure, vehicle type and fit-out, and physical location of crews. Increased funding for reactive maintenance has been approved to implement the determined solutions. Additionally, a performance framework focused on the FSPs achieved duration time has been introduced. The intent of the performance framework is to drive focus onto CAIDI to achieve the fastest possible restoration for the customers. VEGETATION MAINTENANCE As discussed in the 2019 AMP, we have approached management of vegetation with a lens of reliability, resilience and safety. In RY20 Vector has implemented improved risk-based planning, technology enablement, increased resourcing for the delivery of cutting programmes, and processes to monitor and audit the performance of vegetation service providers (VSPs). The implementation of a Quantitative Tree Risk Assessment (QTRA) model assesses the likelihood of failure of any tree. Consequence of failure is expressed in terms of the impacted feeder span and predicted SAIDI determined using our SAIDI criticality model. Increased investment for RY20 and an expanded group of service providers, including a separate resource for initial survey and subsequent audit activity, have enabled an acceleration of activity to address the highest risk vegetation. The use of cloud-based management software allows all parties to have access to the same data, ensuring the most effective use of resources. The use of this new model will be formalised to include the information available from the LiDAR survey completed in RY20 and our ongoing strategy.
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