Decentralized Load Estimation for Distribution Systems Using Artificial Neural Networks.

In this paper, we present a decentralized load estimation approach to support real-time volt/var optimization in distribution networks with high penetration of distributed generation. The decentralized load estimation scheme relies on local information and on a limited amount of information from neighbor areas. We defined a procedure for measurement selection and feeder partitioning so as to limit communication needs and allow prioritizing the estimation accuracy for selected loads. The measurement selection and feeder partitioning methods are based on the mutual information and minimum-redundancy-maximum-relevance concepts. We tested the proposed approach with a modified radial IEEE 34-node test feeder and a weakly meshed IEEE 123-node test feeder. The effectiveness of the method is validated using a hardware-in-the-loop simulation platform. [ABSTRACT FROM AUTHOR]