配电系统状态估计中非高斯测量不确定度的精确建模
Exact Modeling of Non-Gaussian Measurement Uncertainty in Distribution System State Estimation
摘要：
状态估计允许监测电力网络，利用现场测量来导出最可能的电网状态。在文献中，测量误差通常被假设为零均值高斯分布，然而，它已被证明，这一假设往往不成立。一个这样的示例是当考虑伪测量时。在配电网络中，由于实时测量的稀缺性，可能需要大量的伪测量。本文提出了一种状态估计器，它允许模型的测量不确定性与任何连续分布，没有近似。通过将状态估计写成一般的基于最大似然估计的约束优化问题，这成为可能。为了真实地描述配电网，三相不平衡潮流方程。结果表明，不同的不确定性建模方法之间的精度和计算工作量的差异，IEEE欧洲低压测试馈线。
英文摘要：
State estimation allows to monitor power networks, exploiting field measurements to derive the most likely grid state. In the literature, measurement errors are usually assumed to follow zero-mean Gaussian distributions; however, it has been shown that this assumption often does not hold. One such example is when considering pseudo-measurements. In distribution networks, a significant amount of pseudo-measurements might be necessary, due to the scarcity of real-time measurements. In this paper, a state estimator is presented which allows to model measurement uncertainty with any continuous distribution, without approximations. This becomes possible by writing state estimation as a general maximum-likelihood estimation-based constrained optimization problem. To realistically describe distribution networks, three-phase unbalanced power flow equations are used. Results are presented that illustrate the differences in accuracy and computational effort between different uncertainty modeling methods, for the IEEE European Low Voltage Test Feeder.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
