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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. This paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. The WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. The proposed model is also evaluated using real-world data on IEEE 118-bus power system. The results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

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A New Mutimodel approach for modelling of fractional order interval transfer functions 20

A New Mutimodel approach for modelling of fractional order i...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Talmoudi, Samia Ksouri, Moufida Lahmari Applied College Department of Computer Science University of Tabuk Tabuk Saudi Arabia University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis1002 Tunisia

ISBN: (纸本)9798350332568

This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is proposed. This approach is based, essentially, on multimodel approach. In fact, firstly, two new determination methods of models' library, inspired from the Kharitonov approach, are exposed. Then, we propose to compute the validity degree of each model of the obtained library, by optimizing a constrained least squares problem. The global model is, finally deduced by fusion of outputs of the different library' models. To prove its efficiency and its precision, the proposed approach is then compared to the approximation approach of Oustaloup through two simulation examples. © 2023 IEEE.

关键词： Transfer functions

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Distributed Optimization via Gradient Descent with Event-Triggered Zooming Over Quantized Communication

Distributed Optimization via Gradient Descent with Event-Tri...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston MA US Department of Electrical and Computer Engineering School of Engineering University of Cyprus Aalto University Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems KTH Royal Institute of Technologyy Stockholm Sweden

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples.

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Identifiability and Solvability in Inverse Linear Quadratic Optimal control Problems

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Journal of systems Science & Complexity 2021年第5期34卷 1840-1857页

作者： LI Yibei WAHLBERG Bo HU Xiaoming Optimization and Systems Theory Department of MathematicsKTH Royal Institute of TechnologySE-10044StockholmSweden Division of Decision and Control School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologySE-10044StockholmSweden

In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is *** the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.

关键词： Inverse optimal control linear quadratic regulators model identifiability

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control of Isolated AC Microgrids with Constant Power Loads: A Set Invariance Approach

Control of Isolated AC Microgrids with Constant Power Loads:...

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Mediterranean Conference on control and Automation (MED)

作者： Grigoris Michos George C. Konstantopoulos Paul A. Trodden Visakan Kadirkamanathan Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street Sheffield UK Department of Electrical and Computer Engineering University of Patras Rion Greece

This paper proposes a robust control scheme for isolated AC Microgrids, where each node is connected locally to a constant power load (CPL). Contrary to many approaches in the literature, we consider the explicit model of the inverter dynamics and separate the overall system into two parts; a nominal subsystem parametrized by a nominal load and an error subsystem describing the difference between the true and the nominal voltage, resulting from perturbations of the load demand. In the presented analysis, we investigate the non-linear structure of the CPL in order to analytically describe its geometric effect on the network dynamics. We exploit this information to propose mild conditions on the tuning parameters such that a positive invariant set for the error dynamics exists and the distance between the true and the nominal voltage trajectories is bounded at all times. We demonstrate the properties of the proposed control scheme in a simulated scenario.

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Output Feedback Position Tracking control of Marine Vessels Subject to Periodic Disturbances

Output Feedback Position Tracking Control of Marine Vessels ...

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American control Conference (ACC)

作者： Deniz Kurtoglu Enver Tatlicioglu Erkan Zergeroglu Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems (LACIS) University of South Florida Tampa FL United States of America Department of Electrical Electronics Engineering Ege University Izmir Turkey Department of Computer Engineering Gebze Technical University Gebze Kocaeli Turkey

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This work deals with the design and implementation of a novel output feedback position tracking controller for marine vessels subject to uncertainties in their dynamical parameters and periodic external disturbance. Specifically, an adaptive controller that does not make use of velocity measurements has been presented which can compensate for uncertainties in the system's dynamical parameters and periodic external disturbance. A filtered based velocity surrogate formulation in conjunction with a periodic noise estimator and a desired model compensation based adaptive parameter estimator have been utilized to tackle the problem. Boundedness of the closed loop system and convergence of the position tracking error to the origin are proven via Lyapunov-type arguments. Comparative numerical simulations are presented to illustrate the effectiveness of the proposed controller.

关键词： Uncertain systems Uncertainty Noise Estimation Numerical simulation Filtering theory Numerical models

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Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

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IEEE Transactions on Power systems 2025年

作者： Zhou, Yuqi Zhu, Hao National Renewable Energy Laboratory Power Systems Engineering Center GoldenCO80401 United States The University of Texas Chandra Family Department of Electrical & Computer Engineering AustinTX78712 United States

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations. © 1969-2012 IEEE.

关键词： Decentralized control

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Insulation condition forewarning of form-wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network

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High Voltage 2021年第2期6卷 302-313页

作者： Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIllinoisUSA Department of Instrument Science and Engineering Shanghai Jiao Tong UniversityShanghaiChina

Form-wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment,such as high temperature and low *** study proposes a forewarning method for insulation condition monitoring of form-wound windings based on partial discharge(PD)and deep learning neural *** PD features are extracted from the PD profile,which provides information about physics-of-failure and reflects the degree of insulation *** algorithm fusion extracted from auto-encoder and long short-term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi-time-step prediction through historical data to provide *** electrical and thermal accelerated ageing test is performed on the form-wound windings at 0.2 atm to simulate working environment of electric *** proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time-series prediction methods,indicating the advantages of the proposed ***,an on-line hardware setup using a deep learning processor is rec-ommended to implement the forewarning *** proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring.

关键词： network processor neural

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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