Spatially Enabled Asset Management (SEAM)
|Funding mechanism||Network Innovation Allowance (NIA)|
|Duration||Nov 2020 - Oct 2021|
|Research area||New technologies and commercial evolution|
In line with the overall objective of creating and testing a machine learning algorithm to identify and propose fixes for GIS data issues, the project objective are to;
- Generate of potential hypotheses to test and use cases for the tool to be applied to
- Understand the data available to support the machine learning proof of concept
- Outline of the model design including selection of machine learning algorithms.
- Create a final cleaned and prepared dataset that will be used to train and develop the model.
- Provide an interim report that sets out early findings from the modelling and direction for the remainder of the project.
- Develop the final version of the PoC model and front end.
- Carry out statistical evaluation of the model and accuracy through comparison of the model outputs with baseline and training datasets.
- Carry out data cleaning and loading of selected network area, including schematics if available in the format of a connectivity and impedance electrical model of EHV, HV and LV networks.
Provide a summary of key findings, assessment of outcomes against success criteria, recommendations and learnings to be shared.
Geographic Information Systems for utilities have been created by the digitisation of paper records. These have then undergone transformations as data has been moved between one system and another and manipulations such as the corrections to the Master Map background. It is expected that there will be inaccuracies in the current records and these can persist for many years partly due to the lack of visibility of buried assets and partly due to the length of time between sites requiring site visits that would be expected to update the GIS.
Inaccuracies in the GIS system have the potential to impact:
- Accuracy of network modelling
- Accuracy of regulatory reporting
- Field safety
- Network operational efficiency
- Network upgrade/maintenance efficiency
- Accuracy of New Connections information
- Accuracy of information provided to third parties
At the same time the process to correct identified GIS problems, for example a micro-disconnect issue may be highly manual and therefore costly and time consuming. Therefore, the use of an AI engine to police and correct this essential GIS data is a more effective and efficient method of risk reduction.
Similarly, when performing power systems analysis on the networks, data gaps (such as missing cables and asset types) can be a key issue and can greatly increase the analysis time due to time spent fitting data and cleansing the set prior to carrying out the analysis. With the data having potentially many users within and outside of WPD, every time data gaps are filled this not only duplicates effort but is likely to result in different assumptions being made and potentially different conclusions being drawn. A process that can reduce data gaps consistently and accurately would help resolve these issues.
The SEAM project aims to investigate how Machine Learning (ML) can be employed to carry out data cleansing and data gap closure. The ML model will be trained using an existing dataset and then the ability of the model to successfully identify and correct data issues will be evaluated with a separate dataset that has had errors introduced. This will be followed by application to an unaltered WPD dataset with the results being compared to the errors identified by WPD’s Integrated Network Model.
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