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Projects: Projects for Investigator
Reference Number NIA_WPD_054
Title Spatially Enabled Asset Management (SEAM)
Status Completed
Energy Categories Other Cross-Cutting Technologies or Research(Energy system analysis) 90%;
Other Power and Storage Technologies(Electricity transmission and distribution) 10%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 90%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 10%;
UKERC Cross Cutting Characterisation Systems Analysis related to energy R&D (Energy modelling) 100%
Principal Investigator Project Contact
No email address given
National Grid Electricity Transmission
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 November 2020
End Date 01 October 2021
Duration ENA months
Total Grant Value £426,298
Industrial Sectors Power
Region London
Programme Network Innovation Allowance
Investigators Principal Investigator Project Contact , National Grid Electricity Transmission (100.000%)
  Industrial Collaborator Project Contact , Western Power Distribution (0.000%)
Web Site https://smarter.energynetworks.org/projects/NIA_WPD_054
Objectives 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 WPDs Integrated Network Model. The project will include LV and 11kV networks which represent the bulk of GIS data. 33kV networks are included to provide a comparison between the issues identified by the ML algorithm and those flagged by the Integrated Network Model. To enable the use of INM data, the South West region will be used though the approach could be applied to any area. The project will investigate model inputs, outputs and different machine learning algorithms with the final model incorporated into a user interface. The design and algorithms used will be documented to enable knowledge transfer. 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.
Abstract Over time, various factors have adversely affected the quality of DNOs Geospatial Information System (GIS) data. If datasets are shared with patterns of error, then different users will fill these error gaps in different ways leading to inconsistent results from analysis and how the data is exploited by applications. A Machine Learning (ML) tool is proposed to carry out data cleansing and data gap closure of WPDs network GIS and relevant network data. The ML tool will be trained and populated with existing network asset data and run to identify data gaps. The model will initially be developed based on a set of business rules that will be updated based on the outcomes of the ML algorithms. Appropriate will be testing carried out to validate the results and assess the accuracy of the model.
Publications (none)
Final Report (none)
Added to Database 02/11/22