Comprehensive Diagnosis Method of Large Power Grid Based on Multi-agent Perception of Local Computer-Visualized Power Flow

Abstract

For the large grid, a comprehensive fault diagnosis method based on multi-agent perception of the local computer-visualized power flow (CVPF) is proposed. This method first splits the entire network into a number of small sub-networks, and then transforms them into a local CVPFs. Local CVPF is then used to train the convolutional neural networks separately, and finally a multi-agent cluster is formed for comprehensive consultation. First, the retrieval and generation of the radial network was accomplished by defining nodes and branches at all levels. Then, using the fluctuation of the power flow on the branch as an indicator, the multi-agent diagnosis startup strategy was designed. The case study highlighted the problem of false starts of agents in a small observation range, and verified the feasibility of using multi-agent clusters to perceive local CVPF to achieve a comprehensive diagnosis within the jurisdiction and across the jurisdiction. In this process, the precision was used to evaluate the agent's online cross-jurisdiction diagnosis.