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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Khabusi, Simon Peter Huang, Yo-Ping Vu, Van Phong National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan National Penghu University of Science and Technology Department of Electrical Engineering Penghu88046 Taiwan Ho Chi Minh City University of Technology and Education Department of Automatic Control Ho Chi Minh City71300 Viet Nam

ISBN: (纸本)9781665410205

Feature selection holds significant importance in knowledge mining as it plays a pivotal role in selecting and preserving the most informative features within a dataset while discarding irrelevant, redundant, or noisy attributes. This process contributes to enhancing model performance, reducing computational complexity, and refining interpretability, thus facilitating more accurate and efficient data analysis. In high-dimensional datasets, the necessity for feature selection becomes more pronounced due to the heightened risk of encountering the curse of dimensionality. Therefore, this study proposes a weighted fuzzy rough quickreduct (FRQR) feature selection approach employing feature weights to handle the equal situation problem inherent in FRQR. The proposed method is evaluated on ten publicly available datasets with feature sizes ranging from 2000 to 15154. The selected features are used to train and test random forest candidate models whose estimates are then combined according to the posterior probabilities by Bayesian Model Averaging (BMA). The performance of the model on the selected features is evaluated on four performance metrics. The essentiality of the selected features is further determined by comparing the model classification performance achieved on the non-selected features and all the dataset features. The results indicate competitiveness in the performance metric values achieved on selected features over the other two feature categories affirming the efficacy of the proposed method. © 2024 IEEE.

关键词： Feature Selection

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Volt-Var control for Utility-scale Solar PV Plants to Replace SVCs 4

Volt-Var Control for Utility-scale Solar PV Plants to Replac...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Shimomukai, Ken Maeda, Haruka Fujita, Goro Shibaura Institute of Technology Functional Control Systems Graduate School of Engineering and Science Tokyo Japan Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage violation is where voltage exceeds the acceptable range. Often, grid operators request photovoltaic plant owners to regulate voltage sufficiently with expensive and space-consuming static Var compensators. Unfortunately, this sometimes makes the project less feasible. This paper argues that there are better ways to regulate voltage. We ran a simulation with a 70 MWp photovoltaic plant as an addition to the grid. Without voltage regulation, voltage violations were found to be significant. This paper found that oversizing the inverter sufficiently would remove all voltage violations without deploying a static Var compensator. This is often a cheaper and space-saving solution for voltage management. This paper argues that economics and spatial efficiency of reactive power compensation devices is key to increasing photovoltaic penetration. © 2023 IEEE.

关键词： Reactive power

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Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

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IEEE/CAA Journal of automatica Sinica 2022年第6期9卷 1026-1036页

作者： Jingyang Mao Xiangyu Meng Derui Ding Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803 USA IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership ***,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy ***,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and *** the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation ***-time recursive algorithms are given to find the minimal ellipsoid containing the true ***,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

关键词： Affine model membership functions set-membership filtering stability Takagi-Sugeno fuzzy modeling

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On tuning of ADRC with competing design indices:a quantitative study

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control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing With Revenue-Adequacy and FFR Constraints

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 379-391页

作者： Goudarzi, Hamed Hesamzadeh, Mohammad Reza Bunn, Derek Fotuhi-Firuzabad, Mahmud Shahidehpour, Mohammad Sharif University of Technology Center of Excellence in Power System Management and Control Tehran11365-11155 Iran Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden London Business School Department of Management Science and Operations LondonNW1 4SA United Kingdom Illinois Institute of Technology Electrical and Computer Engineering Department ChicagoIL60616 United States

This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm. © 2023 IEEE.

关键词： Power markets

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties 38

An adaptive observer-based SOC estimation method for lithium...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

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A Comparative Evaluation of GA and PSO Tuned PID controllers for the Quadruple Tank System

A Comparative Evaluation of GA and PSO Tuned PID Controllers...

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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Hailu, Gashaye L. Yetayew, Tefera T. Bahir Dar University Electrical and Computer Engineering Faculty Bahir Dar Institute of Technology Bahir Dar Ethiopia Adama Science and Technology University Department of Power and Control Engineering Adama Ethiopia

ISBN: (纸本)9798331527792

In this research, nature inspired metaheuristic optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Techniques are formulated to tune optimal combinations of PID controller parameters for a quadruple tank level control application. The degree of relation of the best paring for both interacted control loops are justified by using Relative Gain Array (RGA) calculation. The overall system has been modeled and implemented in MATLAB/Simulink and performance evaluations has been done for time domain performance specifications of settling time, rise time, percentage overshoot and steady state error as comparison criterions of controllers. Accordingly, for the minimum phase case, GA tuned PID has settling time of 0.36sec and 0.31second for the tank 1 and tank 2 respectively. And PSO tuned PID controller has settling time of 0.96sec and 0.35sec for tank 1and tank 2 respectively. The quantitative and qualitative analysis of results reveals that there is fast dynamic response using GA PID relative to PSO PID even though the difference is not significant that may be due to stochastic feature of the metahueristic optimization algorithms. In summary, the optimal combination of PID controller algorithms' parameters can give robust performance compared to PID controller designed using the classical approaches or manual tuning techniques. © 2024 IEEE.

关键词： Genetic algorithms

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear Systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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Lyapunov-Inspired Deep Reinforcement Learning for Robot Navigation in Obstacle Environments

Lyapunov-Inspired Deep Reinforcement Learning for Robot Navi...

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Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Halil Ibrahim Ugurlu Adrian Redder Erdal Kayacan Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Department of Automatic Control Paderborn University Paderborn Germany

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

The inherent black-box nature of deep reinforcement learning (DRL) poses challenges in ensuring safety constraints. This paper, therefore, introduces a DRL reward design inspired by Lyapunov stability theory for safe robot navigation in the presence of obstacles. The navigation problem is formulated as a state-space control problem with close obstacle locations integrated into the state representation. To ensure safe obstacle avoidance, we introduce a novel reward-shaping strategy utilizing a Lyapunov function that discourages fast movement toward obstacles. Our numerical experiments demonstrate the effectiveness of the reward design strategy compared to baselines in achieving consistent superior learning with higher mission completion rates while maintaining speeds closer to a desired target speed. In addition, we show that our reward design enables a generally smaller choice for the discount factor for value-function-based DRL algorithms, which can lead to faster convergence. This is possible since the reward design merely penalizes the one-step decay of the Lyapunov function. Furthermore, policy training simulations employ an early episode termination method to constrain exploration and add more valuable samples to the DRL training replay memory. Finally, real-world experiments with a quadrotor validate the ability of our method to safely navigate around varying densities of obstacles. The proposed method consistently takes cautious maneuvers near obstacles by slowing down, achieving greater obstacle clearance compared to baseline, although with an increase in mission completion time.

关键词： Training Navigation Deep reinforcement learning Safety Planning Collision avoidance Robots Lyapunov methods Quadrotors Convergence

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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