A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

Chen, Yexiang; Lakshminarayana, Subhash; Maple, Carsten; Poor, H. Vincent

doi:10.1109/oajpe.2022.3140314

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…This process incurs substantial training overhead and demands a substantial amount of training data specific to the new system topology. Chen et al [56] employed meta-learning for DNN-based OPF predictor training, which can find a common initialization vector that enables fast training for any system topology.…”

Section: Direct Mapping Of Opf Variablesmentioning

confidence: 99%

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Jiang,

Wang,

et al. 2024

Energies

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Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

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Section: Direct Mapping Of Opf Variablesmentioning

confidence: 99%

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Jiang,

Wang,

et al. 2024

Energies

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“…The developed new model can identify CCPA location under the real-time system. We omit the detailed description of the algorithm due to the lack of space and refer the reader to our past work [20], in which we apply meta-learning for a similar regression task in power systems.…”

Section: Meta Learning For Attack Localization Under Mtd Topology Rec...mentioning

confidence: 99%

Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning

Chen¹,

Lakshminarayana²,

Teng³

2022

Preprint

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As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect. This work proposes a novel approach to detect such attacks and identify the location of the line outages (due to the physical attack). The proposed approach consists of three parts. Firstly, moving target defense (MTD) is applied to expose the physical attack by actively perturbing transmission line reactance via distributed flexible AC transmission system (D-FACTS) devices. MTD invalidates the attackers' knowledge required to mask their physical attack. Secondly, convolution neural networks (CNNs) are applied to localize line outage position from the compromised measurements. Finally, model agnostic meta-learning (MAML) is used to accelerate the training speed of CNN following the topology reconfigurations (due to MTD) and reduce the data/retraining time requirements. Simulations are carried out using IEEE test systems. The experimental results demonstrate that the proposed approach can effectively localize line outages in stealthy CCPAs.

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“…This survey [30] explored ML and DL methodologies in energy systems. There are several methods applied for ML and DL, such as DNN [12], convolution neural network (CNN) [27,31,32], reinforcement learning (RF) [33], Gaussian process (GP) [34], graph neural network (GNN) [31,35,36], Lagrangian-based approaches [37], compact learning and principal component analysis (PCA) [38], meta-learning [39], and the learning-aided OPF approach [40]. A hybrid or combined model reunites physics-based and statistical methods or two or more individual methods [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Combined MIMO Deep Learning Method for ACOPF with High Wind Power Integration

Unlu,

Peña

2024

Energies

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The higher penetration of renewable energy sources in current and future power grids requires effective optimization models to solve economic dispatch (ED) and optimal power flow (OPF) problems. Data-driven optimization models have shown promising results compared to classical algorithms because they can address complex and computationally demanding problems and obtain the most cost-effective solution for dispatching generators. This study compares the forecast performance of selected data-driven models using the modified IEEE 39 benchmark system with high penetration of wind power generation. The active and reactive power load data of each bus are generated using Monte Carlo simulations, and synthetic wind power data are generated by utilizing a physical wind turbine model and wind speed samples withdrawn from a Weibull distribution. The objective is to design and evaluate an enhanced deep learning approach for the nonlinear, nonconvex alternating current optimal power flow (ACOPF) problem. The study attempts to establish relationships between loads, generators, and bus outcomes, utilizing a multiple-input, multiple-output (MIMO) workflow. Specifically, the study compares the forecast error reduction of convolutional neural networks (CNNs), deep feed-forward neural networks (DFFNNs), combined/hybrid CNN-DFFNN models, and the transfer learning (TL) approach. The results indicate that the proposed combined model outperforms the CNN, hybrid CNN-DFFNN, and TL models by a small margin and the DFFNN by a large margin.

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A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

Cited by 13 publications

References 27 publications

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning

Combined MIMO Deep Learning Method for ACOPF with High Wind Power Integration

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