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Review of data-driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power systems

ieee ACCESS 2023年 11卷 130644-130673页

作者： De Caro, Fabrizio Collin, Adam John Giannuzzi, Giorgio Maria Pisani, Cosimo Vaccaro, Alfredo Univ Sannio UniSannio Dept Engn I-82018 Benevento Italy Terna Rete Italia SpA I-00138 Rome Italy

The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.

关键词： Power system stability Contingency management Power system dynamics Feature extraction Stability criteria Real-time systems Power system security data models Security management Transient analysis Voltage control data-driven methodology dynamic security assessment frequency stability machine learning online power system security rotor stability static security assessment transient stability voltage stability

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On the Applicability of Reinforcement learning in the Traffic Flow control Problem

On the Applicability of Reinforcement Learning in the Traffi...

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2025 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Gorodnichev, Mikhail Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

This article presents the development of a traffic flow regulation method based on an adaptive approach that accounts for changing characteristics of traffic conditions. The study explores key reinforcement learning techniques utilizing Q-learning to enhance traffic management. Simulations of traffic flow control were conducted in the SUMO simulation environment, focusing on cyclic switching methods. The article outlines the implementation of an environment management strategy and the application of reinforcement learning algorithms via the SUMO API. Two reinforcement learning techniques were selected for traffic flow regulation, and extensive training was conducted with various hyperparameters to identify optimal configurations. A comparative analysis of traditional cyclic switching methods and data-driven approaches is provided, highlighting the potential advantages of adaptive methods for improving traffic efficiency and safety in urban environments. The findings suggest that reinforcement learning can significantly enhance traffic management systems in real-time applications. © 2025 ieee.

关键词： Reinforcement learning

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Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task learning

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ieee TRANSACTIONS ON AUTOMATIC control 2023年第5期68卷 2758-2773页

作者： Zeng, Sihan Doan, Thinh T. Romberg, Justin Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24060 USA

In this article, we study a decentralized variant of stochastic approximation (SA), a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with its own operator and data observations, cooperatively find the fixed point of the aggregate operator over a decentralized communication graph. Our main contribution is to provide a finite-time analysis of this decentralized SA method when the data observed at each agent are sampled from a Markov process;this lack of independence makes the iterates biased and (potentially) unbounded. Under fairly standard assumptions, we show that the convergence rate of the proposed method is essentially the same as if the samples were independent, differing only by a log factor that accounts for the mixing time of the Markov processes. The key idea in our analysis is to introduce a novel Lyapunov-Razumikhin function, motivated by the one used in analyzing the stability of delayed ordinary differential equations. We also discuss applications of the proposed method on a number of interesting learning problems in multiagent systems.

关键词： Markov processes Convergence Robot sensing systems Approximation algorithms Q-learning Multitasking Stability analysis Distributed optimization reinforcement learning stochastic approximation (SA)

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A Deep Convolutional Neural Network-Based Approach to Detect False data Injection Attacks on PV-Integrated Distribution systems

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ieee ACCESS 2024年 12卷 2803-2816页

作者： Ahmadzadeh, Masoud Abazari, Ahmadreza Ghafouri, Mohsen Ameli, Amir Muyeen, S. M. Concordia Univ Secur Res Ctr Montreal H3G 1M8 PQ Canada Lakehead Univ Elect Engn Dept Thunder Bay P7B 5E1 ON Canada Qatar Univ Dept Elect Engn Doha Qatar

The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme, has made the PDS prone to various cyber threats, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven framework to detect FDI attacks against voltage regulation of PV-integrated PDS. Initially, an attack-free system is modeled along with its voltage regulation scheme, where the grid measurements are sent to a centralized controller and the control signals are transmitted back to PVs to be used by their local controllers. Then, a convolutional neural network (CNN) framework is proposed to detect FDI attacks. To train this framework-which should be able to distinguish between normal grid behaviors and attacks-a complete and realistic dataset is formed to cover all normal conditions and unpredictable changes of a PDS during a year. Since normal variations and fluctuations in power consumption lead to changes in the voltage profile, this dataset is enriched using features such as season, weekdays, weekends, load conditions, and PV generation power. The performance of the trained framework has been compared with other supervised Machine learning-based and deep-learning techniques for FDI attacks against modified ieee 33- and 141-bus PDSs. Simulation results demonstrate the superior performance of the proposed framework in detecting FDI attacks.

关键词： Distribution systems false data injection cyberattacks convolutional neural network photovoltaic voltage regulation

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A Novel Approach to Wind Turbine Blade Icing Detection With Limited Sensor data via Spatiotemporal Attention Siamese Network

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第6期20卷 8993-9005页

作者： Wang, Lei He, Yigang Zhou, Yazhong Li, Lie Wang, Jing Zhao, Yingying Du, Bolun Wuhan Univ Sch Elect Engn & Automat State Key Lab Power Grid Environm Protect Wuhan 430072 Peoples R China China Elect Power Res Inst Wuhan 430074 Peoples R China

This article focuses on data-driven approaches for icing detection (ID) on wind turbine blades. In light of the widespread application of sensor technologies in wind turbines, such data-driven ID methods have become increasingly prominent. However, current methods have deficiencies, particularly in acknowledging the structural properties of multivariate sensor data and in differentiating icing stages, both critical for the identification of failure patterns. To bridge these gaps, we propose a spatiotemporal attention Siamese network (STASN) for blade ID. This model employs a Siamese network architecture for efficient few-shot learning amidst class imbalance. It uniquely incorporates a graph attention network and gated recurrent unit for extracting spatiotemporal features from sensor data. This design not only acknowledges the spatial structure of the data but also distinctly identifies features pertinent to various icing stages. The efficacy of STASN was validated using actual sensor data from supervisory control and data acquisition systems. The results demonstrate STASN's capability in discerning distinct icing stage features and its potential in early icing prediction. This research underscores STASN's utility in providing advanced, flexible fault alarms for blade icing, representing a significant stride in wind turbine maintenance and safety.

关键词： Wind turbines Blades Feature extraction data models Correlation Task analysis Spatiotemporal phenomena Few-shot learning (FSL) graph attention network (GAT) icing detection (ID) Siamese network (STASN) wind turbine blade

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data-driven Dissipativity Analysis of Perturbed Linear Time-invariant systems 36

Data-driven Dissipativity Analysis of Perturbed Linear Time-...

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36th Chinese control and Decision conference (CCDC)

作者： Sun, Zongyuan Bai, Nan Duan, Zhisheng Peking Univ Coll Engn Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In this article, we consider the data-driven dissipativity analysis of perturbed linear time-invariant (LTI) systems. Both cases are considered when input-state trajectories and input-output trajectories are available, and the dissipative conditions are provided respectively. Compared with existing studies under the assumption of additive noise, we consider the case where system matrices contain some uncertainty, and we also derive a detailed discussion of the constructed input-output-equivalent system. A numerical simulation is provided to verify the effectiveness of theoretical result.

关键词： data-driven Dissipativity LTI systems Uncertain systems

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data-driven Permissible Safe control with Barrier Certificates 63

Data-Driven Permissible Safe Control with Barrier Certificat...

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63rd ieee conference on Decision and control, CDC 2024

作者： Mazouz, Rayan Skovbekk, John Mathiesen, Frederik Baymler Frew, Eric Laurenti, Luca Lahijanian, Morteza University of Colorado Boulder United States Delft University of Technology Netherlands

ISBN: (纸本)9798350316339

This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian Process (GP) regression and obtaining probabilistic errors for this estimate. Then, we develop an algorithm for constructing piecewise stochastic barrier functions to find a maximal permissible strategy set using the learned GP model, which is based on sequentially pruning the worst controls until a maximal set is identified. The permissible strategies are guaranteed to maintain probabilistic safety for the true system. This is especially important for learned systems, because a rich strategy space enables additional data collection and complex behaviors while remaining safe. Case studies on linear and nonlinear systems demonstrate that increasing the size of the dataset for learning grows the permissible strategy set. © 2024 ieee.

关键词： Stochastic systems

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Empowering Autonomous Underwater Vehicles Using learning-Based Model Predictive control With Dynamic Forgetting Gaussian Processes

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ieee TRANSACTIONS ON control systems TECHNOLOGY 2025年

作者： Amer, Abdelhakim Mehndiratta, Mohit Brodskiy, Yury Kayacan, Erdal Aarhus Univ Dept Elect & Comp Engn Artificial Intelligence Robot Lab AiR Lab DK-8000 Aarhus Denmark GIM Robot Espoo 02650 Finland EIVA AS DK-8660 Skanderborg Denmark Paderborn Univ Dept Elect Engn & Informat Technol Automat Control Grp RAT D-33098 Paderborn Germany

Autonomous underwater vehicles (AUVs) present several challenges due to the complex and simultaneous interplay of various factors, including but not limited to unmodeled dynamics, highly nonlinear behavior, intercouplings, communication delays, and environmental disturbances. In particular, environmental disturbances degrade trajectory tracking performance for model-based controllers, e.g., model predictive control (MPC) algorithms. data-driven methods such as the Gaussian process (GP) are effective at learning disturbances in real time;however, the underlying offline hyperparameter tuning process limits their overall effectiveness. To overcome this limitation, we propose a novel dynamic forgetting GP (DF-GP) methodology that compensates for operational disturbances, thus circumventing the need for hyperparameter retuning. In essence, the proposed method optimally combines the predictions of individual GPs-designed with handcrafted forgetting factors, rendering precise disturbance estimation of varying timescales. What is more, the predicted disturbances update the model parameters in MPC, facilitating a learning-based control framework that ensures accurate tracking performance in different underwater scenarios. Rigorous simulation and real-world experiments demonstrate the efficiency and efficacy of the proposed framework. The results show a 25% improvement in disturbance estimation and tracking performance, demonstrating that the proposed framework outperforms its direct competitors.

关键词： Mathematical models Estimation Vehicle dynamics Vectors Real-time systems Predictive models Predictive control Adaptation models Gaussian processes Electronic mail Autonomous underwater vehicle (AUV) model predictive control (MPC) learning-based control model learning Gaussian processes (GPs) trajectory tracking

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Approximating Reachable Sets for Neural Network-Based Models in Real Time via Optimal control

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ieee TRANSACTIONS ON control systems TECHNOLOGY 2023年第4期31卷 1901-1908页

作者： Thapliyal, Omanshu Hwang, Inseok Purdue Univ Sch Aeronaut & Astronaut Engn W Lafayette IN 47906 USA

In this brief, we present a data-driven framework for real-time estimation of reachable sets for control systems where the plant is modeled using neural networks (NNs). We utilize a running example of a quadrotor model that is learned using trajectory data via NNs. The NN learned offline can be excited online to obtain linear approximations for reachability analysis. We use a dynamic mode decomposition (DMD)-based approach to obtain linear lifting of the NN model. The linear models thus obtained can utilize optimal control theory to obtain polytopic approximations to the reachable sets in real time. The polytopic approximations can be tuned to arbitrary degrees of accuracy. The proposed framework can be extended to other nonlinear models that utilize NNs to estimate plant dynamics. We demonstrate the effectiveness of the proposed framework using an illustrative simulation of quadrotor dynamics.

关键词： Computational modeling Artificial neural networks Trajectory Numerical models Real-time systems Optimal control Mathematical models Approximation methods machine learning reachability analysis

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Distributed Optimization for Quadratic Cost Functions With Quantized Communication and Finite-Time Convergence

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ieee TRANSACTIONS ON control OF NETWORK systems 2025年第1期12卷 930-942页

作者： Rikos, Apostolos I. Grammenos, Andreas Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Johansson, Karl H. KTH Royal Inst Technol Div Decis & Control Syst S-11428 Stockholm Sweden Digital Futures S-11428 Stockholm Sweden Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England Alan Turing Inst London NW1 2DB England Univ Cyprus Sch Engn Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Aalto Univ Sch Elect Engn Dept Elect Engn & Automat Espoo 02150 Finland

In this article, we propose two distributed iterative algorithms that can be used to solve the distributed optimization problem for quadratic local cost functions over large-scale networks in finite time. The first algorithm exhibits synchronous operation while the second one exhibits asynchronous operation. Both algorithms operate exclusively with quantized values. This means that the information stored, processed, and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to, first, optimal task scheduling for data centers, and second, global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. In addition, it is shown that our proposed algorithms compare favorably to algorithms in the current literature.

关键词： Convergence Quantization (signal) Stochastic processes Task analysis Machine learning algorithms Cost function control systems Distributed algorithms federated learning finite-time optimization quantization resource allocation

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