Load Decomposition at Smart Meters Level Using Eigenloads Approach


The deployment of the advanced metering infrastructure (AMI) in distribution systems provides an wonderful chance 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. During this paper, masses' current and voltage waveforms are processed offline to create a comprehensive library. This library consists of a set of measurements projected onto the eigenloads space. Eigenloads are essentially the eigenvectors describing the load signatures area. A dead ringer for human faces, every load has a distinct signature. Every load measurement is remodeled into a photograph and an economical face recognition algorithm is applied to the set of photos. A list of all the web devices is often stored and can be accessed at any time. The proposed method can be implemented at the sensible meters level. The distributed computation which will be achieved by performing straightforward calculations at every sensible meter, while not the necessity for sending intensive information to a central processor, is helpful. From a system operator perspective, load composition in close to-real-time provides the loads' voltage dependence that are needed, for instance, in volt-VAR optimization (VVO) in distribution systems. Further applications of load composition information are also mentioned.

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