Abstract
Abstract As the scale of the power grid continues to expand, power supply companies centralize the management of grid monitoring signals at substations to adapt to the actual development of the power grid and improve the performance of grid operation. However, this leads to problems such as too many invalid signals, unidentified signal classification, excessive signal volume, and too many interfering signals at the monitoring site on a daily basis, which leads to difficulties in monitoring and certain security risks. Addressing this problem, this paper introduces a method of spatialization of text data using unique thermal coding and intelligent analysis of monitoring signals using AP clustering. The affinity propagation clustering algorithm can classify the monitoring signals faster and more accurately, count the number of signals, and deeply excavate the intelligent information implied therein. This greatly enhances the stability and safety of power grid operation. The proposed method is validated by the actual monitoring signal data.
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