Abstract
An approach is demonstrated to visualise overhead line failure rates and estimated wind power output during extreme wind events on transmission networks. Reanalysis data is combined with network data and line failure models to illustrate spatially resolved line failure probability with data corrected for asset altitude and exposure. Wind output is estimated using a corrected power curve to account for high speed shutdown with wind speed corrected for altitude. Case studies demonstrate these methods' application on representations of real networks of different scales. The proposed methods allow users to determine at-risk regions of overhead line networks and to estimate the impact on wind power output. Such techniques could equally be applied to forecasted weather conditions to aid in resilience planning. The methods are shown to be particularly sensitive to the weather data used, especially when modelling risk on overhead lines, but are still shown to be useful as an indicative representation of system risk. The techniques also provide a more robust method of representing weather-related failure rates on lines considerate of the altitude, voltage level, and their varying exposure to weather conditions than current techniques typically provide, which can be used to usefully represent failure probability of lines during storms.
Highlights
Extreme weather and climate change manifest as risks for the power system in multiple ways which are difficult to quantify, and challenging to productively address
Examples of work completed in this area can be seen in [11, 30]. In the former, data analysis studied the availability of a wind farm as storm-fronts passed across it to determine the impact of High wind speed shutdown (HWSS), whereas in the latter, HWSS was evaluated by generating a power curve from wind speed data compared to aggregated wind power outputs in GB; due to the scarcity of wind speed/output data at high speeds, a Gaussian filter was fitted by inspection to the tail of the data to represent HWSS
There will, be risk of flooding in Southerly, low-lying regions with less wind, compounding risk associated with overhead lines (OHLs) at higher elevations, exacerbated by variability in wind power associated with HWSS
Summary
This paper concentrates on how to represent and model windrelated failure rates on OHL networks and the potential links between system risk and wind power availability during storm events These methods begin to take consideration of the impact of the different geographic conditions a network branch may cover and the diversity of weather it may be subjected to in a way comparable studies have not. The proposed methodology, demonstrates an approach for quantifying the threats associated with adverse weather conditions which can be adapted for different natural hazards to understand how threats to OHL may vary regionally Disaggregating such representations of network branches could allow operators and planners to determine not just which network branches are at risk, but where on the network they are most likely to fault, allowing for more optimised placement of repair assets and teams. This shows a clear advancement in the representation of line faults in security assessments, while demonstrating the varied strength of different levels of network and the need to distinguish between such networks in analysis
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