AMI Data Quality and Collection Method Considerations for Improving the Accuracy of Distribution Models

Spectral clustering is applied to the problem of phase identification of electric customers to investigate the data needs (resolution and accuracy) of advanced metering infrastructure (AMI). More accurate models are required to accurately interconnect high penetrations of PV/DER and for optimal electric grid operations. This paper demonstrates the effects of different data collection implementations and common errors in AMI datasets on the phase identification task. This includes measurement intervals, data resolution, collection periods, time synchronization issues, noisy measurements, biased meters, and mislabeled phases. High quality AMI data is a critical consideration to model correction and accurate hosting capacity analyses.

I. Introduction

The availability of advanced metering infrastructure (AMI) data provides an opportunity to analyze the distribution system at a level that was previously impossible and to accelerate the installation of high penetrations of residential PV systems. One of the many challenges facing the installation of highpenetration of residential PV systems and other distributed energy resources (DER) is the necessity for rapid and accurate hosting capacity analysis simulations [1]. AMI data has the potential to aid in determining the placement of solar installations, as well as correcting errors present in existing utility models, [2]–[6]. Accurate models are necessary for accurate hosting capacity analyses which are critical to interconnect high penetrations of DER, and research using AMI data is showing promising results in correcting common types of errors. However, there are many open questions regarding what type of AMI data should be collected to best facilitate the new data science techniques. Each utility implements their own version of AMI data collection with different collection intervals, meter precision, etc. See [7] for an overview of AMI and smart meter deployment.