Thesis
Application of machine learning for power system frequency stability : security constrained optimisation and adaptive emergency control
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2025
- Thesis identifier
- T17495
- Person Identifier (Local)
- 202168023
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- This thesis develops a novel set of Machine Learning (ML)-based techniques for frequency stability management in modern power systems with high penetration of Converter-Interfaced Generation (CIG). Specifically, the techniques aim to enhance situationalawareness by capturing detailed frequency dynamics, which conventional approachesstruggle to do, and ensure frequency stability through preventive actions - by introducing ML-based constraints into optimisation models to account for the detailed dynamics - and corrective actions - by implementing agentic ML-based adaptive loadshedding, focusing on locational aspects and optimising where, when and how muchload to shed. Adapted for both online and offline applications, these techniques equipoperators with advanced decision-making tools.Traditionally, system operators use detailed analytical expressions to manage andensure secure operation. This approach captures and represents the frequency dynamics of the system by solving the Differential Algebraic Equations (DAEs) of the network.Furthermore, operators usually perform offline or time domain simulations (TDS) analyses on a selected set of scenarios, including expected worst-case scenarios. However,these approaches have significant computational requirements, making them typicallysuitable only for offline applications with a limited number of scenarios. Even whenevaluating such a limited set of scenarios, identifying the most critical ones a priorihas become increasingly difficult. As power systems become increasingly complex anduncertain, the challenge intensifies, with the growing number of candidate scenarioscreating a significant computational hindrance. Consequently, operators often need toover-secure the system, which comes at a cost and hinders widespread adoption of cleanenergy to achieve net-zero targets. Moreover, numerical approaches - simplified alternative models for fast screening - are increasingly facing accuracy issues due to theincreasing complexity of the system, potentially introducing significant errors, whichcan lead to unforeseen instability.In response to these challenges, this research proposes an ML-based approach toensure frequency stability in modern low-inertia networks. ML models are capable ofaccurately learning complex system relationships using simulation or observational dataused during training. As a result, they can predict the system response almost instantly,because solving the network’s DAEs is no longer required to make predictions. Such anapproach is especially suitable for real-time or near-real-time applications, providingquick scenario screenings of detailed dynamics, where TDS would be computationallyprohibitive. Ultimately, operators are provided with advanced decision-making tools,enabling them to more accurately and efficiently manage frequency stability while imposing minimal computational overhead.
- Advisor / supervisor
- Papadopoulos, Panagiotis
- Bukhsh, Waqquas
- Resource Type
- DOI
- Funder
Relations
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PDF of thesis T17495 | 2025-10-24 | Public | Download |