"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."

本补充说明详细概述了支撑题为《基于知识森林（Knowledge Forest）与熵减（Entropy Reduction）的信息组织与交互框架——以换流阀厅（Converter Valve Hall）为例》的研究的数据与方法论。本研究旨在评估负荷与环境温度变化对变压器组熵值与信息增益的影响。本次实验采用9台ABB TR-1500型变压器（额定容量1500 kVA，变比10 kV/0.4 kV，频率50 Hz，Δ/Y接线），将其分为3组，每组3台并联运行。实验初始工况设置为总负荷150 MW、环境温度25℃，并记录各组初始熵值：第1组为1.5，第2组为1.4，第3组为1.6。通过调整工况实现变量扰动：第1组仅提升负荷至200 MW，第2组仅升高环境温度至35℃，第3组同时提升负荷至200 MW并升高环境温度至35℃。采集工况调整后的实验数据，用于计算条件熵与信息增益，结果显示所有组别均出现显著熵减与可量化的信息增益。本数据集的数据源包括2020年1月至2023年12月的上海电网运行数据，以及采用PSS®E与OpenDSS软件生成的仿真数据。上述数据集涵盖实时电气量测数据、设备状态数据、环境工况数据以及仿真运行场景数据。数据处理环节包含清洗、归一化、聚合与集成操作，以保障数据的一致性与完整性。本补充说明还展示了核心研究结果，包括环境工况与设备性能的相关性、不同场景下的系统稳定性，以及效率与韧性指标等内容。本补充说明提供的详细数据集与方法论可支撑研究结论的复现与验证，为基于熵减理论的透明电网领域后续研究提供了坚实的研究基础。