Load Decomposition at Smart Meters Level Using Eigenloads Approach

The deployment of the advanced metering infrastructure (AMI) in distribution systems provides an excellent opportunity for load monitoring applications. Load decomposition can be done at the smart meters level, providing a better understanding of the load behavior at near-real-time. In this paper, loads' current and voltage waveforms are processed offline to form a comprehensive library. This library consists of a set of measurements projected onto the eigenloads space. Eigenloads are basically the eigenvectors describing the load signatures space. Similar to human faces, every load has a distinct signature. Each load measurement is transformed into a photo and an efficient face recognition algorithm is applied to the set of photos. A list of all the online devices is always stored and can be accessed at any time. The proposed method can be implemented at the smart meters level. The distributed computation that can be achieved by performing simple calculations at each smart meter, without the need for sending intensive data to a central processor, is beneficial. From a system operator perspective, load composition in near-real-time provides the loads' voltage dependence that are needed, for example, in volt-VAR optimization (VVO) in distribution systems. Further applications of load composition data are also discussed.