The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork. However, a reliable estimate of the admittance matrix may either be missing or quicklybecome obsolete for temporally varying grids. In this work, we propose a data-driven identificationmethod utilising voltage and current measurements collected from micro-PMUs. More precisely,we first present a maximum likelihood approach and then move towards a Bayesian framework,leveraging the principles of maximum a posteriori estimation. In contrast with most existing con-tributions, our approach not only factors in measurement noise on both voltage and current data,but is also capable of exploiting available a priori information such as sparsity patterns and knownline parameters. Simulations conducted on benchmark cases demonstrate that, compared to otheralgorithms, our method can achieve significantly greater accuracy.

翻译：间歇性可再生发电的日益一体化,特别是在分配层面,需要先进的规划和优化方法,这取决于电网的知识,特别是收集电网的地形和线参数的入门矩阵,然而,对入门矩阵的可靠估计可能缺失,或很快过时,用于时间差异的电网。在这项工作中,我们建议采用数据驱动的识别方法,使用从微型多电联收集的电压和当前测量。更确切地说,我们首先提出一种最大可能性的方法,然后转向巴伊西亚框架,利用事后估计最高值的原则。与大多数现有的共同因素相反,我们的方法不仅在测量电压和当前数据的噪音方面存在着因素,而且还能够利用现有的先期信息,如电压模式和已知线参数。根据基准案例进行的模拟表明,与其他数值相比,我们的方法可以达到更高的准确性。