基于量测的动态负荷建模技术与方法评价
Evaluating Measurement-Based Dynamic Load Modeling Techniques and Metrics
摘要：
大型电力系统中的广域数据和算法为基于测量的动态负荷建模技术的实施创造了新的机会。这些技术提高了动态负荷模型的准确性，这是暂态稳定分析的一个组成部分。基于测量的负荷建模技术通常假设响应误差与系统或模型精度相关。响应误差是仿真输出和相量测量单元（PMU）样本之间的差异。本文研究了相似性措施，输出类型，仿真时间跨度，和干扰类型用于产生响应误差和响应误差的相关性，系统精度。本文旨在解决两个假设：1）响应误差可以决定总的系统精度？以及2）响应误差是否能够指示在总线处使用的动态负载模型是否足够准确？研究结果表明，只有特定的指标组合产生统计上显著的相关性，并且缺乏提供显著相关性的指标组合模式。在这项研究中，不到20%的模拟测试产生了统计学上显著的相关性。这些结果突出了常见的基于测量的负荷建模技术的关注，提高了人们对仔细选择和验证相似性度量和响应输出度量的重要性的认识。天真或未经测试的指标选择可能会产生不准确和误导性的结果。
英文摘要：
Wide-area data and algorithms in large power systems are creating new opportunities for implementation of measurement-based dynamic load modeling techniques. These techniques improve the accuracy of dynamic load models, which are an integral part of transient stability analysis. Measurement-based load modeling techniques commonly assume response error is correlated to system or model accuracy. Response error is the difference between simulation output and phasor measurement units (PMUs) samples. This paper investigates similarity measures, output types, simulation time spans, and disturbance types used to generate response error and the correlation of the response error to system accuracy. This paper aims to address two hypotheses: 1) can response error determine the total system accuracy? and 2) can response error indicate if a dynamic load model being used at a bus is sufficiently accurate? The results of the study show only specific combinations of metrics yield statistically significant correlations, and there is a lack of pattern of combinations of metrics that deliver significant correlations. Less than 20% of all simulated tests in this study resulted in statistically significant correlations. These outcomes highlight concerns with common measurement-based load modeling techniques, raising awareness to the importance of careful selection and validation of similarity measures and response output metrics. Naive or untested selection of metrics can deliver inaccurate and misleading results.
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