Enriching Load Data Using Micro-PMUs and Smart Meters

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which record high-resolution data; however, in practice, micro-PMUs are installed at limited locations networks due to budgetary constraints. contrast, smart meters widely deployed but only measure relatively low-resolution energy consumption, cannot sufficiently reflect the actual instantaneous volatility within each sampling interval. this paper, we have proposed a novel approach for enriching data service transformers that meters. The key our is statistically recover data, masked using trained probabilistic models of both high- and sources, i.e., overall framework consists two steps: first, with Gaussian Process leveraged capture relationship between maximum/minimum average interval meters; Markov chain model employed characterize transition probability known load. Next, used as xmlns:xlink="http://www.w3.org/1999/xlink">teachers decompose into targeted data. enriched significantly enhance system observability situational awareness. We verified real