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Reference Number NIA2_NGESO018
Title Automated Identification of Sub-Synchronous Oscillations (SSO) Events
Status Started
Energy Categories RENEWABLE ENERGY SOURCES(Solar Energy) 20%;
RENEWABLE ENERGY SOURCES(Wind Energy) 20%;
OTHER POWER and STORAGE TECHNOLOGIES(Electricity transmission and distribution) 60%;
Research Types Applied Research and Development 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
National Grid ESO
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 September 2022
End Date 31 March 2024
Duration ENA months
Total Grant Value £450,000
Industrial Sectors Power
Region London
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , National Grid ESO (100.000%)
  Other Investigator Project Contact , National Grid ESO (100.000%)
  Industrial Collaborator Project Contact , National Grid plc (0.000%)
Web Site https://smarter.energynetworks.org/projects/NIA2_NGESO018
Objectives The project will be delivered in four work packages:WP1 - Review of methodsDevelop novel methodologies for frequency domain analysis of controller interactions, assess strengths and weaknesses against existing techniques including implementation challenges, filtering methodologies for further exploration.Explore local, screening and global sensitivity analysis techniques for priority ranking of critical uncertain parameters.Compare machine learning techniques to find a suitable classification algorithm for the automated identification of SSO events.Review existing (time-domain) modelling tools for operational stability margin, inputs and assumptions for controller interaction studies and the associated business processes (engagement with ESO/NGET subject matter experts)Review learning from complementary ESO and industry innovation projectsWP2 - Development and testing of methodsDefine appropriate case studies and test networks (and operational scenarios) to test the performance of the developed methods of frequency scanning, uncertainty sensitivities and classification of SSO scenarios.Develop and trial the most promising methods from WP1 on test networks in PowerFactory/PSCAD, explore sensitivity and automation techniques to reduce computational resource, explore probabilistic techniques to characterise uncertainties. Algorithms will be implemented in Python and linked to PowerFactory/PSCAD.Compare and verify the method results e.g., based on available measurements of events or published results from other research and networks eventsWP3 - DemonstrationDemonstrate the developed methods on the GB networkAdapt the tools developed in WP2 to run on the full network to ensure the practicality of the tool to run in larger networks with reasonable computational resourcesWP4 – Roadmap and implementationRecommendations on implementation of the SSO analysis framework for assessing operational stability margin in system design and connection studies.Recommendations on how this framework can be extended as a data driven online SSO identification and warning tool as more PMU data is available.Handover and training on the developed tools New transmission network connections are checked for Sub-synchronous Oscillations (SSO) based on a few future network conditions. Due to the inherent uncertainties in the network and forthcoming reinforcements, scenarios other than those currently considered could materialise. These uncertainties, and the possibility of more frequent controller interactions, are due to the changing nature of the future system with a more significant proportion of converter interfaced generation (of different technology types) coupled with declining short circuit level.As the SSO identification requires Electromagnetic Transient (EMT) analysis, which is very time-consuming, there is a need to develop an analytical framework that will allow screening of scenarios without running EMT simulations and reduce the total number of scenarios that need to be investigated further. This will ensure that EMT simulations are used only for scenarios of potential concern, and system engineers can focus on root cause analysis. Due to the volume of studies, the process complexity and the amount of data generated, it is crucial to automat this process as much as possible. The project aims to apply a few advanced techniques borrowed from mathematics, statistics, and machine learning to solve the complex problem of identifying and managing SSOs in future power systems. The project will aim to deliver the following objectives:A primary objective of this project is to represent the black box models by a grey box approach which will allow for the identification of state variables which participate and contribute to the poorly damped oscillations. This is crucial to facilitate root cause analysis of controller interaction events.Develop a methodology to filter from an extensive a pool of scenarios with the possibility of SSO events based on impedance scans techniques.Develop a tool combining automation and machine learning techniques to run EMT simulations unattended and to identify SSO events automatically.Provide study cases to evaluate the performance and accuracy of the tools by testing historical event data or synthetic data created by simulation.
Abstract As the number of large wind and solar connections increases, any potential interaction, due to the differences in their converter control system, will be an important consideration during planning and design studies. It will be increasingly important to understand the impact of any new connection in terms of unacceptable oscillatory behaviour considering the possible sources of uncertainty (e.g., forecast errors, parameter errors) and variability (e.g., wind speed) that can affect the network condition.This project will explore, develop, and test a combination of novel frequency domain methodologies and machine learning techniques to identify potential system operating conditions which can lead to Sub-Synchronous Oscillations (SSOs) and implement an automated control interaction studies framework.
Publications (none)
Final Report (none)
Added to Database 14/10/22