4.6 Article

Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An attack-tolerant trustable distributed intelligence paradigm

Journal

ENERGY REPORTS
Volume 7, Issue -, Pages 8900-8911

Publisher

ELSEVIER
DOI: 10.1016/j.egyr.2021.10.113

Keywords

Multi-regional large-scale power system; Multi-agents system; Blockchain enabled intelligence; Attack-tolerant capability; Distributed deep reinforcement learning

Categories

Funding

  1. National Key R&D Program of China [2018AAA0101504]

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This article introduces a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) for cooperative control in multi-regional large-scale power systems using a multi-agents system. The system is capable of defending against malicious attacks and has been validated for effectiveness and efficiency through mathematical modeling and comparative experiments.
In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of centralized dispatching and hierarchical management, this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment. (C) 2021 Published by Elsevier Ltd.

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