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Your search keyword '"Ghias, Amer M. Y. M."' showing total 97 results
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97 results on '"Ghias, Amer M. Y. M."'

1. On Physics-Informed Neural Network Control for Power Electronics

Author

Hui, Peifeng, Cui, Chenggang, Lin, Pengfeng, Ghias, Amer M. Y. M., Niu, Xitong, and Zhang, Chuanlin

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Considering the growing necessity for precise modeling of power electronics amidst operational and environmental uncertainties, this paper introduces an innovative methodology that ingeniously combines model-driven and data-driven approaches to enhance the stability of power electronics interacting with grid-forming microgrids. By employing the physics-informed neural network (PINN) as a foundation, this strategy merges robust data-fitting capabilities with fundamental physical principles, thereby constructing an accurate system model. By this means, it significantly enhances the ability to understand and replicate the dynamics of power electronics systems under complex working conditions. Moreover, by incorporating advanced learning-based control methods, the proposed method is enabled to make precise predictions and implement the satisfactory control laws even under serious uncertain conditions. Experimental validation demonstrates the effectiveness and robustness of the proposed approach, highlighting its substantial potential in addressing prevalent uncertainties in controlling modern power electronics systems.

Published
2024

2. Large Language Models based Multi-Agent Framework for Objective Oriented Control Design in Power Electronics

Author

Cui, Chenggang, Liu, Jiaming, Feng, Junkang, Hui, Peifeng, Ghias, Amer M. Y. M., and Zhang, Chuanlin

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Power electronics, a critical component in modern power systems, face several challenges in control design, including model uncertainties, and lengthy and costly design cycles. This paper is aiming to propose a Large Language Models (LLMs) based multi-agent framework for objective-oriented control design in power electronics. The framework leverages the reasoning capabilities of LLMs and a multi-agent workflow to develop an efficient and autonomous controller design process. The LLM agent is able to understand and respond to high-level instructions in natural language, adapting its behavior based on the task's specific requirements and constraints from a practical implementation point of view. This novel and efficient approach promises a more flexible and adaptable controller design process in power electronics that will largely facilitate the practitioners., Comment: 6 pages, 6 figures

Published
2024

3. Blockchain for Future Smart Grid: A Comprehensive Survey

Author

Mollah, Muhammad Baqer, Zhao, Jun, Niyato, Dusit, Lam, Kwok-Yan, Zhang, Xin, Ghias, Amer M. Y. M., Koh, Leong Hai, and Yang, Lei

Subjects
Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture, Computer Science - Social and Information Networks, Electrical Engineering and Systems Science - Systems and Control
Abstract

The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues., Comment: 26 pages, 13 figures, 5 tables

Published
2019
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7. Adaptive Horizon Seeking for Generalized Predictive Control via Deep Reinforcement Learning With Application to DC/DC Converters

Author

Cui, Chenggang, Dong, Yinfeng, Dong, Xin, Zhang, Chuanlin, and Ghias, Amer M. Y. M.

Abstract

For enhancing the capacity to optimize the control performance of the generalized predictive control (GPC) method, this paper proposes an adaptive horizon mechanism design methodology based on deep reinforcement learning (DRL). To handle the systems with the presence of mismatched disturbances, a baseline and secure environment for DRL training is established by constructing an offset-free GPC design framework. Furthermore, the proposed method incorporates a multi-objective reward function design into the twin-delayed deep deterministic policy gradient (TD3) algorithm, which explicitly considers multiple comprehensive performance indexes. This enables the ability to attain the desired level of control precision and meet diverse control requirements across various operating conditions. Besides, it attains the capability to adapt to various system parameters effectively. The proposed method is validated through a numerical simulation of a three-order numerical system and an application to a DC/DC boost converter feeding constant power loads (CPLs). Simulation and experimental comparison results with other controllers demonstrate the efficacy and performance improvements.

Published
2024
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16. A Multi-Agent Deep Reinforcement Learning based Voltage Control on Power Distribution Networks

Author

Zhang, Bin, Ghias, Amer M. Y. M., and Chen, Zhe

Subjects
Asia, Distribution networks, Reinforcement learning, Training, Deep learning, Real-time systems, Renewable energy sources
Abstract

Due to the high penetration of renewable energy in the distribution network, the exponential increase in the amount of data collected and variables makes it difficult for centralized control methods to achieve real-time voltage regulation. Besides, hardware conditions (e.g., communication equipment) limit its application in practice. Therefore, a model-free multi-agent deep reinforcement learning (MADRL) voltage control strategy is developed in this paper. The proposed MADRL control strategy is carried out with a framework of centralized training and distributed execution. We apply deep deterministic policy gradient algorithm to help each agent control its corresponding PV inverter in a distributed manner. However, during the training process, the agent could observe other agents' information to improve training. The simulation on a 33-bus distribution network is carried out to illustrate the effectiveness of the proposed method, and its superiority is also validated by comparing with traditional methods.

Published
2022

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