Accurate knowledge of power grid topology is a prerequisite for effective state estimation and grid stability. While data-driven methods for topology reconstruction exist, the minimum requirements for measurement quality, specifically regarding quantization, precision, and sampling frequency, remain under-explored. This study investigates the data fidelity required to reconstruct distribution grid topologies using voltage magnitude measurements. Adopting an information-theoretic approach, we utilize the Chow-Liu algorithm to generate maximum spanning trees based on mutual information. Rather than proposing a new reconstruction algorithm, our primary contribution is a comprehensive sensitivity analysis of the measurement data itself. We systematically evaluate the impact of data bit-depth, significant digit truncation, time-window length, and different mutual information estimators on reconstruction accuracy. We validate this approach using IEEE test cases (via MATPOWER) and time-series data from GridLAB-D. Our results demonstrate that grid topology can be successfully recovered even with highly quantized 8-bit data or millivolt-level precision. However, performance degrades significantly when downsampling intervals exceed 20 minutes or when data availability is limited to short durations. These findings establish an optimistic theoretical lower bound, suggesting that costly high-precision instrumentation may not be strictly necessary for structural inference under ideal conditions. This rigorous baseline provides a foundation for future evaluations of noisy real world smart meter data and hybrid approaches that incorporate existing engineering priors.

翻译：准确的电网拓扑知识是实现有效状态估计和电网稳定性的前提。尽管存在基于数据的拓扑重构方法，但测量质量的最低要求——特别是在量化、精度和采样频率方面——仍有待深入探索。本研究探讨了利用电压幅值测量值重构配电网拓扑所需的数据保真度。采用信息论方法，我们利用Chow-Liu算法基于互信息生成最大生成树。本研究的主要贡献并非提出新的重构算法，而是对测量数据本身进行全面的敏感性分析。我们系统评估了数据位深度、有效数字截断、时间窗口长度以及不同互信息估计器对重构精度的影响。通过IEEE测试案例（基于MATPOWER）和GridLAB-D的时间序列数据验证了该方法。结果表明，即使使用高度量化的8位数据或毫伏级精度，电网拓扑仍可成功恢复。然而，当降采样间隔超过20分钟或数据可用时长有限时，性能显著下降。这些发现确立了乐观的理论下界，表明在理想条件下，成本高昂的高精度仪器可能并非结构推理的必要条件。这一严格的基准为未来评估含噪的真实世界智能电表数据及融合现有工程先验知识的混合方法奠定了基础。