Transient Stability Analysis with Physics-Informed Neural Networks

Stiasny, Jochen; Misyris, George S.; Chatzivasileiadis, Spyros

doi:10.48550/arxiv.2106.13638

Cited by 6 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent literature highlights the diverse applications of physics-informed Neural Networks (PINNs) in power system transient stability, with a particular focus on the ordinary differential equation (ODE) swing equation. These applications vary from simple setups involving a single infinite bus [26] to more intricate configurations involving nine buses [29], all examining rotor angle behavior in synchronous generators. However, there is a noticeable lack of comprehensive analyses of PINNs across different scales of power systems.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Analysis of PINNs for Power System Transient Stability

de Cominges Guerra,

Li,

Wang

2024

Electronics

View full text Add to dashboard Cite

The integration of machine learning in power systems, particularly in stability and dynamics, addresses the challenges brought by the integration of renewable energies and distributed energy resources (DERs). Traditional methods for power system transient stability, involving solving differential equations with computational techniques, face limitations due to their time-consuming and computationally demanding nature. This paper introduces physics-informed Neural Networks (PINNs) as a promising solution for these challenges, especially in scenarios with limited data availability and the need for high computational speed. PINNs offer a novel approach for complex power systems by incorporating additional equations and adapting to various system scales, from a single bus to multi-bus networks. Our study presents the first comprehensive evaluation of physics-informed Neural Networks (PINNs) in the context of power system transient stability, addressing various grid complexities. Additionally, we introduce a novel approach for adjusting loss weights to improve the adaptability of PINNs to diverse systems. Our experimental findings reveal that PINNs can be efficiently scaled while maintaining high accuracy. Furthermore, these results suggest that PINNs significantly outperform the traditional ode45 method in terms of efficiency, especially as the system size increases, showcasing a progressive speed advantage over ode45.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comprehensive Analysis of PINNs for Power System Transient Stability

de Cominges Guerra,

Li,

Wang

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Such methods are promising for real-time operation [79], [80] as their prediction requires minimal computational time, but have challenges related to the generation of training data [81], the interpretability of the prediction [82], their risks and probabilities of success [83], and their usability to other operating conditions and topologies [84], etc. Recently promising methods use the known dynamical model, i.e., the ODEs, to inform directly the ML training which can reduce the demands for training data [85], [86]. Real-time DSA could soon become a reality and upgrade the security assessment module of Fig.…”

Section: E Real-time Dsamentioning

confidence: 99%

Perspectives on Future Power System Control Centers for Energy Transition

Marot

Kelly²,

Naglič

et al. 2022

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Today's power systems are seeing a paradigm shift under the energy transition, sparkled by the electrification of demand, digitalisation of systems, and an increasing share of decarbonated power generation. Most of these changes have a direct impact on their control centers, forcing them to handle weather-based energy resources, new interconnections with neighbouring transmission networks, more markets, active distribution networks, micro-grids, and greater amounts of available data. Unfortunately, these changes have translated during the past decade to small, incremental changes, mostly centered on hardware, software, and human factors. We assert that more transformative changes are needed, especially regarding humancentered design approaches, to enable control room operators to manage the future power system. This paper discusses the evolution of operators towards continuous operation planners, monitoring complex time horizons thanks to adequate real-time automation. Reviewing upcoming challenges as well as emerging technologies for power systems, we present our vision of a new evolutionary architecture for control centers, both at backend and frontend levels. We propose a unified hypervision scheme based on structured decision-making concepts, providing operators with proactive, collaborative, and effective decision support.

show abstract

“…PINNs were first applied to power systems in [34] to predict swing equation dynamics. They have since additionally been used for system identification [35], transient stability predictions [36], and for learning grid dynamics without simulation data [37]. Beyond ODE simulation and trajectory prediction, physics and sensitivity informed methods have also been utilized for regularizing models related to power distribution grid optimization [38], ACOPF [39], [40], DCOPF [41], parameter estimation [42], and risk-aware voltage optimization via "riskregularization" [43].…”

Section: Model Training and Physics-based Regularizationmentioning

confidence: 99%

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Stiasny¹,

Chevalier²,

Nellikkath³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes. With its ability to learn from complex datasets and provide predictive solutions on fast timescales, machine learning (ML) is well-posed to help overcome these challenges as power systems transform in the coming decades. In this work, we outline five key challenges (dataset generation, data pre-processing, model training, model assessment, and model embedding) associated with building trustworthy ML models which learn from physics-based simulation data. We then demonstrate how linking together individual modules, each of which overcomes a respective challenge, at sequential stages in the machine learning pipeline can help enhance the overall performance of the training process. In particular, we implement methods that connect different elements of the learning pipeline through feedback, thus "closing the loop" between model training, performance assessments, and re-training. We demonstrate the effectiveness of this framework, its constituent modules, and its feedback connections by learning the N-1 small-signal stability margin associated with a detailed model of a proposed North Sea Wind Power Hub system.

show abstract

Transient Stability Analysis with Physics-Informed Neural Networks

Cited by 6 publications

References 12 publications

A Comprehensive Analysis of PINNs for Power System Transient Stability

A Comprehensive Analysis of PINNs for Power System Transient Stability

Perspectives on Future Power System Control Centers for Energy Transition

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Contact Info

Product

Resources

About