A Framework for Information Organization and Interaction Based on Knowledge Forest and Entropy Reduction: A Case of the Converte

This supplementary note provides a detailed overview of the data and methodologies supporting the study \ A Framework for Information Organization and Interaction Based on Knowledge Forest and Entropy Reduction: A Case of the Converter Valve Hall.\ The aim is to assess the impact of load and ambient temperature changes on the entropy and information gain of transformer groups. The experiment involved nine ABB TR-1500 transformers (1500 kVA, 10 kV\/0.4 kV, 50 Hz, \u0394\/Y configuration), arranged in three groups of three transformers connected in parallel. Initial conditions were set with a 150 MW load and 25\u00b0C ambient temperature. Each group's initial entropy was recorded (Group 1: 1.5; Group 2: 1.4; Group 3: 1.6). The conditions were varied by increasing the load (Group 1: 200 MW), ambient temperature (Group 2: 35\u00b0C), or both (Group 3: 200 MW and 35\u00b0C). Post-change data were collected to calculate conditional entropy and information gain, revealing significant entropy reduction and quantifiable information gain across all groups. The data sources include operational data from the Shanghai Power Grid (January 2020 to December 2023) and simulation data generated using PSS\u00aeE and OpenDSS software. These datasets contain real-time electrical measurements, equipment status, environmental conditions, and simulated operational scenarios. Data processing involved cleaning, normalization, aggregation, and integration to ensure consistency and comprehensiveness. Key results, such as the correlation between environmental conditions and equipment performance, system stability under different scenarios, and efficiency and resilience metrics, are presented. This detailed dataset and methodology enable the replication and validation of the study's findings, providing a robust foundation for further research in transparent power grids based on entropy reduction theory.