论文标题
基于噪声传感器测量的天然气网络中拓扑验证的基于统计学习的算法
A Statistical Learning-Based Algorithm for Topology Verification in Natural Gas Networks Based on Noisy Sensor Measurements
论文作者
论文摘要
天然气网络拓扑的准确了解对于天然气网络的正确运行至关重要。故障，身体攻击和网络攻击可能会导致实际的天然气网络拓扑与操作员认为存在的情况不同。错误的拓扑信息误导了操作员以应用不适当的控制，从而导致损坏和缺乏气体供应。在文献中提出了用于验证拓扑结构的几种方法，用于电源发电网络，但我们不知道天然气网络有任何出版物。在本文中，我们为天然天然气网络开发了一种有用的拓扑验证算法，基于修改一般统计的一般方法，以消除对本应用的严重限制，同时保持良好的性能。我们证明，新算法等同于原始的基于统计的方法，用于大量传感器观察。我们为渐近性能提供了新的闭合形式表达式，这些表达式对于实现可靠性能所需的典型传感器观测值是准确的。
Accurate knowledge of natural gas network topology is critical for the proper operation of natural gas networks. Failures, physical attacks, and cyber attacks can cause the actual natural gas network topology to differ from what the operator believes to be present. Incorrect topology information misleads the operator to apply inappropriate control causing damage and lack of gas supply. Several methods for verifying the topology have been suggested in the literature for electrical power distribution networks, but we are not aware of any publications for natural gas networks. In this paper, we develop a useful topology verification algorithm for natural gas networks based on modifying a general known statistics-based approach to eliminate serious limitations for this application while maintaining good performance. We prove that the new algorithm is equivalent to the original statistics-based approach for a sufficiently large number of sensor observations. We provide new closed-form expressions for the asymptotic performance that are shown to be accurate for the typical number of sensor observations required to achieve reliable performance.
