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Iranian Conference on Electrical engineering (ICEE)

作者： Mohammad Bagher Moradi Mohammad Hasan Nazari Seyed Hossein Hosseinian Hamed Nafisi Electrical Engineering Department Amirkabir University of Technology Tehran Iran Smart Control System Department Niroo Research Institude (NRI) Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

Prosumers within residential networks exhibit varying objectives when devising their plans for renewable energy sources. Some of them leverage their resources to minimize energy procurement costs, while others strive to maximize energy sales. Microgrids offer a suitable platform for residential producers to achieve their objectives. In a microgrid equipped with peer-to-peer features, prosumers determine their energy sales according to their pre-established goals. Additionally, they engage in energy sharing with consumers. Following a thorough examination of energy transactions in the prosumers self-scheduling step, the objective is to optimize societal welfare. This study introduces a hierarchical approach for addressing energy transactions and meeting the objectives of prosumers. At the outset, the investigation delves into prosumers' self-scheduling aligning with their specified objectives. Following a thorough examination of energy distribution, the objective is to optimize societal welfare. Furthermore, sensitivity analysis is employed to assess the impact of uncertainties in load on the achieved outcomes. The suggested method is evaluated in a test network comprising 20 prosumers and 20 consumers. The simulation is carried out using Mixed-Integer Linear Programming (MILP) in GAMS, utilizing the CPLEX solver.

关键词： Procurement Renewable energy sources Uncertainty Costs Sensitivity analysis Microgrids Robustness

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A Study of Two Types of Introductory Exercise Problems for Network Programming Learning with Python 23

A Study of Two Types of Introductory Exercise Problems for N...

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11th International Conference on Computer and Communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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A Battery Model Parameter Identification Method Considering Topology Reconfiguration 20

A Battery Model Parameter Identification Method Considering ...

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20th International Conference on AC and DC Power Transmission 2024, ACDC 2024

作者： Chen, Aizuo Quan, Yue Zhou, Yanglin Lei, Ertao Zhang, Junkun Ci, Song Department of Electrical Engineering Tsinghua University Beijing100084 China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong 510080 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781837242672

In the field of battery circuit model parameter estimation, the combination of bilinear transformation with the least squares method has garnered widespread attention due to its excellent performance under continuous current conditions. However, in dynamic reconfigurable battery network (DRBN), the network's topological structure is constantly changing, and the battery current consists of non-periodic pulse signal sequences, making it challenging for traditional methods to accurately estimate the battery model parameters within such networks. To address this issue, this paper proposes a lithium-ion battery circuit model parameter estimation method that takes into account network topology reconfiguration. This method, based on the operational modes and sampling methods of DRBN, derives a new discrete transformation formula for the transfer function and constructs a least squares model parameter identification model using this formula. By establishing an experimental platform for DRBN, the effectiveness of the proposed method is validated in this paper. © The Institution of engineering & Technology 2024.

关键词： Network topology

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Convergent Wheeled Robot Navigation Based on an Interpolated Potential Function and Gradient

SSRN

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SSRN 2023年

作者： Seder, Marija Klančar, Gregor Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of System Control and Cybernetics Faculty of Electrical Engineering University of Ljubljana Slovenia

The article presents a novel idea to construct a smooth navigation function based on the grid-based search that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predicted control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Smallest Eigenvalues Based Logarithmic Derivative Method for Computing Dominant Oscillation Modes in Large-Scale Power systems

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Journal of Modern Power systems and Clean Energy 2024年第3期13卷 747-756页

作者： Linguang Wang Xiaorong Xie Wenkai Dong Yong Mei Aoyu Lei Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing China Power Dispatching and Control Center of China Southern Power Grid Guangzhou China

With the rapid integration of renewable energy, wide-band oscillations caused by interactions between power electronic equipment and grids have emerged as one of the most critical stability issues. Existing methods are usually studied for local power systems with around one hundred nodes. However, for a large-scale power system with tens of thousands of nodes, the dimension of transfer function matrix or the order of characteristic equation is much higher. In this case, the existing methods such as eigenvalue analysis method and impedance-based method have difficulty in computation and are thus hard to utilize in practice. To fill this gap, this paper proposes a novel method named the smallest eigenvalues based logarithmic derivative (SELD) method. It obtains the dominant oscillation modes by the logarithmic derivative of the $k$ -smallest eigenvalue curves of the sparse extended nodal admittance matrix (NAM). An oscillatory stability analysis tool is further developed based on this method. The effectiveness of the method and the tool is validated through a local power system as well as a large-scale power system.

关键词： Power system stability Eigenvalues and eigenfunctions Stability analysis Oscillators Mathematical models Impedance Computational efficiency Transmission line matrix methods Thermal stability Sparse matrices

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LiFePO4 Battery SOC Estimation under OCV-SOC Curve Error Based on Adaptive Multi-Model Kalman Filter

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IEEE Transactions on Transportation Electrification 2025年

作者： Paizulamu, Daniyaer Cheng, Lin Xu, Helin Zhuang, Yingrui Qi, Ning Ci, Song Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Columbia University Department of Earth and Environmental Engineering New YorkNY10027 United States

LiFePO4 batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the open circuit voltage-state of charge curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose an SOC estimation method that accounts for error in OSC. First, we establish battery equivalent circuit model and introduce a parameters identification algorithm based on adaptive recursive least squares. Next, we derive the relationship between the innovation's cross-correlation matrix/auto-correlation matrix of the Kalman filter and the OSC error. We then develop an adaptive multi-model Kalman filter, which dynamically adjusts the measurement model parameters of each filter based on the sign of the OSC error. By assigning a probability to each filter according to its predicted voltage distribution function, the optimal filter is selected. The proposed method is tested under various OSC error types and operating conditions. Results demonstrate that the proposed method achieves high accuracy and robustness, with root mean square error of less than 0.03, also offers significant advantages in computational efficiency, interpretability, and generalizability. © 2015 IEEE.

关键词： Distribution functions

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Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

Data-Driven Robust Optimization for Energy Chemical Processe...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

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A Dual-Stage Optimal controller Design for controlling AVR system

A Dual-Stage Optimal Controller Design for Controlling AVR S...

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International Computer Science and engineering Conference (ICSEC)

作者： Layla H. Abood Mohamed J. Mohamed May H. Abood Department of Control and System Engineering University of Technology Baghdad Iraq Department of Computer Engineering Al-Iraqia University Baghdad Iraq

In this study a cascade controller is proposed to maintain the Automatic Voltage Regulator (AVR) system, its comprises from two stage the first one is a conventional Proportional and Derivative (PD) controller and the second one is a Fractional Order PID (FOPID) controller is combined together for regulating the terminal voltage in AVR system, the gains of the proposed controller is tuned used a smart optimization algorithm called Squirrel Search algorithm (SSA) and adopt an Integral Time Absolute Error (ITAE) for monitoring for monitoring desired response to obtain a stable and robust response, the system response is tested using Matlab 2019 and the transient analysis is used to calculate overshoot, settling time, rise time, and peak time then compared these values selected classical controllers(PID,FOPID) to verify the robust response of the proposed controller. A robustness analysis verified the system response when there is an external disturbances, positive and negative disturbances signals are added, the controller try to face these signals, it takes 0.15 sec in negative disturbance and 0.1 sec in positive disturbance then in both states force the response to return to its desired response.

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Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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