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13th IEEE Power and Energy Society: Innovative Smart Grid Technologies - Asia, ISGT Asia 2024

作者： Naina, P.M. Swarup, K. Shanti Iit Department of Electrical Engineering Madras India

ISBN: (纸本)9798350373929

With the increasing penetration of distributed generators, distributed algorithms have become essential for efficient power system operations. However, these algorithms are often vulnerable to noise and communication delays due to their dependence on communication infrastructures. A consensus-based decentralized method is presented in this paper to solve economic dispatch in microgrids, accounting for practical communication challenges such as dynamically switching topologies, communication delays, and noise. A proportional-integral (PI) consensus protocol is proposed to address these issues, which includes an integral term to mitigate the harmful effects of noise and delays. The multi-agent system (MAS) model is used to describe the microgrid. A distributed PI consensus approach with two primary consensus variables - the global power imbalance (P ) and the incremental cost (λ) - is employed to solve the economic dispatch problem. A key benefit of this method is that it allows generating units to maintain the confidentiality of their cost characteristics. The effectiveness of the presented approach is demonstrated by simulation results on a microgrid with five buses. © 2024 IEEE.

关键词： consensus algorithm

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Privacy Preserving Second-order consensus by Adding Edge-based Perturbations

Privacy Preserving Second-order Consensus by Adding Edge-bas...

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2024 China Automation Congress, CAC 2024

作者： Ma, Zhuo Chu, Hongjun Cheng, Zuxia College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications NanJing China Institute of Advanced Technology for Carbon Neutrality Nanjing University of Posts and Telecommunications NanJing China

ISBN: (纸本)9798350368604

Many works on privacy-preserving consensus of multi-agents are limited to first-order dynamics, and developing privacy-preserving consensus algorithms based on second-order dynamical network systems has a stronger need for engineering practicality and presents a greater challenge. In this paper, we propose a privacy-preserving second-order consensus algorithm based on strongly connected directed graphs. The algorithm avoids direct interaction of real states among individuals by adding well-designed perturbation signals based on communication edges. Theoretical results indicate that the proposed algorithm is able to achieve accurate second-order dynamic consensus. Moreover, through rigid theoretical analysis, we give necessary and sufficient conditions for an agent to maintain its position and velocity privacy in the face of an internal honest-but-curious agent. Simulation examples verify the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： consensus algorithm

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Fast consensus Topology Design via Minimizing Laplacian Energy 63

Fast Consensus Topology Design via Minimizing Laplacian Ener...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Lu, Susie Liu, Ji Stanford Online High School United States Stony Brook University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

This paper characterizes the graphical properties of an optimal topology with minimal Laplacian energy under the constraint of fixed numbers of vertices and edges, and devises an algorithm to construct such connected optimal graphs. These constructed graphs possess maximum vertex and edge connectivity, and more importantly, generically exhibit large algebraic connectivity of an optimal order provided they are not sparse. These properties guarantee fast and resilient consensus processes over these graphs. © 2024 IEEE.

关键词： consensus algorithm

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Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34409-34419页

作者： Li, Xiang Yuan, Weijie Zhang, Kecheng Wu, Nan Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Robot & Comp Vis Shenzhen 518055 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using EL methods, several approaches have been proposed combining message passing (MP) algorithms. In this article, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low-complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.

关键词： Estimation Robot sensing systems Location awareness Accuracy Signal processing algorithms Message passing Internet of Things consensus algorithm distributed estimation edge learning (EL) factor graph Gaussian mixture model (GMM) message passing (MP)

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The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning With Efficient Communication

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第1期23卷 507-528页

作者： Xu, Xing Li, Rongpeng Zhao, Zhifeng Zhang, Honggang Zhejiang Univ Hangzhou 310027 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

The paper considers independent reinforcement learning (IRL) for multi-agent collaborative decision-making in the paradigm of federated learning (FL). However, FL generates excessive communication overheads between agents and a remote central server, especially when it involves a large number of agents or iterations. Besides, due to the heterogeneity of independent learning environments, multiple agents may undergo asynchronous Markov decision processes (MDPs), which will affect the training samples and the model's convergence performance. On top of the variation-aware periodic averaging (VPA) method and the policy-based deep reinforcement learning (DRL) algorithm (i.e., proximal policy optimization (PPO)), this paper proposes two advanced optimization schemes orienting to stochastic gradient descent (SGD): 1) A decay-based scheme gradually decays the weights of a model's local gradients with the progress of successive local updates, and 2) By representing the agents as a graph, a consensus-based scheme studies the impact of exchanging a model's local gradients among nearby agents from an algebraic connectivity perspective. This paper also provides novel convergence guarantees for both developed schemes, and demonstrates their superior effectiveness and efficiency in improving the system's utility value through theoretical analyses and simulation results.

关键词： Independent reinforcement learning federated learning consensus algorithm communication overheads

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Secure Trust-Based Delegated consensus for Blockchain Frameworks Using Deep Reinforcement Learning

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IEEE ACCESS 2022年 10卷 118498-118511页

作者： Goh, Yunyeong Yun, Jusik Jung, Dongjun Chung, Jong-Moon Yonsei Univ Coll Engn Sch Elect & Elect Engn Seoul 03722 South Korea Samsung Elect Suwon 16677 South Korea

Internet of Things (IoT) networks generate massive amounts of data while supporting various applications, where the security and protection of IoT data are very important. In particular, blockchain technology supporting IoT networks is considered as the most secure, expandable, and scalable database storage solution. However, existing blockchain systems have scalability problems due to low throughput and high resource consumption, and security problems due to malicious attacks. Several studies have proposed blockchain technologies that can improve the scalability or the security level, but there have been few studies that improve both at the same time. In addition, most existing studies do not consider malicious attack scenarios in the consensus process, which deteriorates the blockchain security level. In order to solve the scalability and security problems simultaneously, this paper proposes a Dueling Double Deep-Q-network with Prioritized experience replay (D3P) based secure trust-based delegated consensus blockchain (TDCB-D3P) scheme that optimizes the blockchain performance by applying deep reinforcement learning (DRL) technology. The TDCB-D3P scheme uses a trust system with a delegated consensus algorithm to ensure the security level and reduce computing costs. In addition, DRL is used to compute the optimum blockchain parameters under the dynamic network state and maximize the transactions per second (TPS) performance and security level. The simulation results show that the TDCB-D3P scheme can provide a superior TPS and resource consumption performance. Furthermore, in blockchain networks with malicious nodes, the simulation results show that the proposed scheme significantly improves the security level when compared to existing blockchain schemes by effectively reducing the influence of malicious nodes.

关键词： Blockchain consensus algorithm deep reinforcement learning (DRL) Internet of Things (IoT) trust

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On the Design of Event-Triggered consensus-Based Secondary Control of DC Microgrids

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第5期37卷 3834-3846页

作者： Shafiee, Pouya Khayat, Yousef Batmani, Yazdan Shafiee, Qobad Guerrero, Josep M. Regent Power Co LLC St Louis MO 63110 USA Univ Kurdistan Smart Micro Grids Res Ctr SMGRC Dept Elect Engn Sanandaj *** Iran Aalborg Univ Dept Energy DK-9220 Aalborg Denmark

Voltage regulation and proper load-sharing are two main control objectives of DC microgrids which the consensus-based secondary control can be regarded as a feasible approach to achieve them. Using this approach, in addition to the stable voltage regulation, the load is shared according to the nominal potential of the distributed generation units. Nevertheless, the conventional consensus-based secondary control uses the time-triggered communication paradigm that leads to a high communication burden due to transmitting and receiving unnecessary data. In this paper, to prevent excessive data-exchanging, the mentioned approach is upgraded with a novel event-triggered scheme. The system stability analysis under the suggested event-triggered secondary controller is assessed using the Lyapunov theorem. Furthermore, the Zeno-freeness feature of the proposed solution is mathematically proved after assessing uniformly ultimately bounded stability and extracting the current per-unit and voltage derivatives dynamic as the consensus algorithm's external inputs. The proposed approach is applied to a DC microgrid, and its performance is validated under different scenarios using Simulink/MATLAB environment. Moreover, the experimental tests prove the merits of the suggested method.

关键词： Voltage control Power system dynamics Power system stability Microgrids Laplace equations Bandwidth Reliability consensus algorithm DC microgrids event-triggered control secondary control voltage regulation

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An Economic Dispatch Method for Power System Based on Distributed Mutual-Fed-consensus

An Economic Dispatch Method for Power System Based on Distri...

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2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Li, Xiangjun Zhang, Panpan Li, Bo China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Energy Storage and Electrical Engineering Department Beijing China Northeast Electric Power University School of Automation Engineering China

ISBN: (纸本)9798350366204

This paper presents a distributed algorithm based on mutual feedback mechanism for economic dispatch problem of power system. In such a mutual feedback mechanism, the average of the load demand and power generated are estimated, respectively. Simulation results on standard test cases show the convergence and effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： consensus algorithm

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New Results on Finite Convergence Time Mode consensus: a Summary 63

New Results on Finite Convergence Time Mode Consensus: a Sum...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Huang, Chao Shim, Hyungbo Yu, Siliang Anderson, Brian D. O. Tongji University College of Electronic and Information Engineering Shanghai200092 China Seoul National University Asri Department of Electrical and Computer Engineering Seoul Korea Republic of Australian National University School of Engineering ActonACT2601 Australia

ISBN: (纸本)9798350316339

This paper studies the distributed mode consensus problem in a multi-agent system. Three algorithms are proposed to find the most frequent attribute (the mode) owned by the agents via distributed computation. The first algorithm computes the frequency of each attribute using consensus protocols rooted in blended dynamics, then identifies the most frequent attribute as the mode. The second algorithm, under the assumption that each agent possesses a priori knowledge of a minimum frequency for the mode, can decrease the frequency computations required at each agent for large lower bounds. In contrast, the third algorithm eliminates the necessity for such information by implementing an adaptive updating mechanism. These algorithms successfully determine the mode within a finite time frame, and predictive estimates for convergence time are included. Moreover, the first and second algorithms demonstrate plug-and-play property with a dwell time. © 2024 IEEE.

关键词： consensus algorithm

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A multi-objective social-based algorithm for community detection

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Multimedia Tools and Applications 2025年 1-30页

作者： Farrokhpour Dizaj, Yousef Lotfi, Shahriar Hajipour, Javad Department of Computer Science Faculty of Mathematics Statistics and Computer Science University of Tabriz Tabriz Iran

The detection of community structures in complex networks has garnered significant attention in recent years. Given its NP-hardness, numerous evolutionary optimization-based approaches have been proposed. However, there is still a need for improvement, since no previous method has been able to find the true community structure on all benchmark networks. In this paper, we propose a multi-objective approach based on the social-based algorithm (SBA). SBA is a hybrid of the imperialist competitive algorithm and the standard evolutionary algorithm. SBA has demonstrated remarkable performance in finding optimal solutions and can effectively identify correct community structures. In our method, instead of a single solution, a set of non-dominated solutions is produced, each representing a different compromise between the objective functions. To evaluate the performance of the proposed algorithm, various tests were conducted on synthetic and real-world datasets. The results of experiments on several social network benchmarks show up to a 57% improvement on benchmark networks compared to previous works. This indicates that the proposed algorithm performs well in community detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： consensus algorithm

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