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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Kamal Shahid Muhammad Majid Gulzar Muhammad Ali Raza Salman Habib Hasnain Ahmad Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainability Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings.

关键词： Meters Load monitoring Reactive power Artificial neural networks Smart homes Machine learning User interfaces

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Constructive barrier feedback for collision avoidance in leader-follower formation control

arXiv

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arXiv 2023年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal Division of Decision and Control Systems KTH Royal Institute of Technology Sweden I3S-CNRS Université Côte d'Azur Institut Universitaire de France France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper proposes a novel constructive barrier feedback for reactive collision avoidance between two agents. It incorporates this feature in a formation tracking control strategy for a group of 2nd-order dynamic robots defined in three-dimensional space. Using only relative measurements between neighboring agents, we propose an elegant decentralized controller as the sum of a nominal tracking controller and the constructive barrier feedback for leader-follower formations under a directed single-spanning tree graph topology. The key ingredient is the use of divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring robots without compromising the nominal controller's performance. Compared to traditional barrier function-based optimization controllers, the proposed constructive barrier feedback avoids feasibility issues and results in more computationally efficient control algorithms with systematic equilibrium analysis. Simulations are provided to validate the effectiveness of the proposed control method. © 2023, CC BY-NC-ND.

关键词： Collision avoidance

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Optimal Sliding Mode control of Single Arm PAM-Actuated Manipulator

Optimal Sliding Mode Control of Single Arm PAM-Actuated Mani...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Sadiq, Musaab Esam Humaidi, Amjad Jaleel Kadhim, Saleem Khalefa Mhdawi, Ammar Al Alkhayyat, Ahmed Ibraheem, Ibraheem Kasim General Company for Grain Processing Ministry of Trade Baghdad Iraq University of Technology Control and Systems Eng. Dept. Baghdad Iraq Edge Hill University Department of Computer Science United Kingdom College of Technical Engineering Islamic University Najaf Iraq College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781665437660

The manipulators actuated by Pneumatic Artificial Muscles (PAMs) are characterized by high nonlinearity and time varying of their coefficients. Therefore, nonlinear and robust controllers are required to cope with these challenging control problems. This work presents the development of control design for trajectory tracking of PAM-Actuated manipulator based on Sliding Mode control (SMC). The PSO technique is used to tune the design parameters of proposed controller for further enhancement of controlled PAM system performance. A comparison study has been conducted between the optimal and non-optimal SMC controllers via computer simulation using MATLAB programming software. © 2021 IEEE.

关键词： Trajectory tracking System performance Programming Muscles Manipulators systems engineering and theory Numerical simulation

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A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

A fast and effective maximum power point tracking method und...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods Power electronics Solar panels

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Study of the Shock Loads Effect on the Operation of Multi-Motor Electric Drives of a Rolling Mill with a Second-order Elastic Coupling

Study of the Shock Loads Effect on the Operation of Multi-Mo...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Valerii Tytiuk Alexey Kalinin Valeriy Druzhinin Vitalii Kuznetsov Oleksii Chornyi Victor Busher Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Department of Informative Computer Systems Abylkas Saginov Karaganda Technical University NPJSC Karaganda The Republic of Kazakhstan Department of Power Engineering Karaganda Industrial University NPJSC Temirtau The Republic of Kazakhstan Department of the Electrical Engineering Ukrainian State University of Science and Technologies Dnipro Ukraine Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine

This paper presents a study for optimizing a hot rolling mill. The paper is devoted to the study of ways to reduce the effect of shock loads of synchronous electric drives of the roughing mill group on the relative lengthening of the metal strip in the inter-stand gap of the rolling stands of the finishing mill group. The elasticity of the metal strip in the inter-stand gap of the rolling mill has a significant impact on the movement of intercoupled multi-motor electric drives of the finishing mill group. Another crucial route of exposure that influences the motion of the electric drives within the finishing mill group involves the electromagnetic connection between the drives in the roughing and finishing mill groups. The impact of the electromagnetically connected circuits is evident through the effect of shock loads from synchronous electric drives on the angular velocity of the direct current electric drives within the finishing mill group. The characteristics of the electromagnetic communication circuit are established by the power grid arrangement and equipment specifications. To assess the quality of work of electric drives of the finishing mill group, it is proposed to use the relative lengthening of the metal strip in the inter-stand gap, which is an important indirect indicator of the quality of rolling. The mathematical model of a hot rolling mill that takes into account both the elasticity of the metal strip that connects the DC electric drives of the finishing group, and the electromagnetic coupling circuit that takes into account the impact loads of synchronous electric drives of the roughing group on the example of the mill 1700 of ArcelorMittal Temirtau JSC was proposed in this paper. Using the mathematical model developed in Simulink software using the methods of planning the experiment, regression models were obtained to establish the dependence of the parameters of overexcitation of a synchronous motor on the magnitude of the voltage sag in the no

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Distributed Optimization with Gradient Descent and Quantized Communication

arXiv

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arXiv 2022年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden and Digital Futures StockholmSE-100 44 Sweden Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node’s estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples. © 2022, CC BY.

关键词： Directed graphs

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Toward Transactive control of Coupled Electric Power and District Heating Networks

arXiv

引用

arXiv 2022年

作者： Maurer, Jona Tschuch, Nicolai Krebs, Stefan Bhattacharya, Kankar Cañizares, Claudio Hohmann, Sören Karlsruhe Institute of Technology Institute of Control Systems Wilhelm-Jordan-Weg 1 Karlsruhe76131 Germany Department of Electrical and Computer Engineering University of Waterloo ONN2L3G1 Canada

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits. © 2022, CC BY-NC-ND.

关键词： Renewable energy resources

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COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTIMATION 9

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTI...

引用

9th International Conference on Learning Representations, ICLR 2021

作者： Silva, Arlei Kocayusufoglu, Furkan Jafarpour, Saber Bullo, Francesco Swami, Ananthram Singh, Ambuj Computer Science Department UC Santa BarbaraCA93106-5110 United States Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States U.S. Army Research Lab AdelphiMD20783 United States

The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep learning

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Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysis

Empirical Risk Minimization with Relative Entropy Regulariza...

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IEEE International Symposium on Information Theory

作者： Samir M. Perlaza Gaetan Bisson Iñ aki Esnaola Alain Jean-Marie Stefano Rini INRIA Sophia Antipolis France Laboratoire de Math&#x00E9 matiques GAATI Universit&#x00E9 de la Polyn&#x00E9 sie Fran&#x00E7 aise Faaa French Polynesia Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Electrical and Computer Engineering National Chao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

The optimality and sensitivity of the empirical risk minimization problem with relative entropy regularization (ERM-RER) are investigated for the case in which the reference is a σ-finite measure instead of a probability measure. This generalization allows for a larger degree of flexibility in the incorporation of prior knowledge over the set of models. In this setting, the interplay of the regularization parameter, the reference measure, the risk function, and the empirical risk induced by the solution of the ERM-RER problem is characterized. This characterization yields necessary and sufficient conditions for the existence of regularization parameters that achieve arbitrarily small empirical risk with arbitrarily high probability. Additionally, the sensitivity of the expected empirical risk to deviations from the solution of the ERM-RER problem is studied. Dataset-dependent and dataset-independent upper bounds on the absolute value of the sensitivity are presented. In a special case, it is shown that the expectation (with respect to the datasets) of the absolute value of the sensitivity is upper bounded, up to a constant factor, by the square root of the lautum information between the models and the datasets.

关键词： Sufficient conditions Upper bound Sensitivity analysis Entropy Risk management Information theory

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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

Short - Term Prediction of Coke Pushing Current Peak Based o...

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Industrial Cyber-Physical systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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