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Accuracy of Probabilistic Harmonic Estimation in Sparsely Monitored Transmission Networks

Accurate estimation of harmonics in uncertain, power electronics interfaced large transmission networks without installation of excessive number of power quality monitors can significantly improve and facilitate the probabilistic harmonic propagation studies. Traditional harmonic state estimation methods have been widely studied but are still very challenging in practical application due to the requirement of a large number of synchronized monitoring devices and real-time operational structure of the network. Based on a preliminary study that demonstrates the effectiveness of sequential artificial neural networks (ANNs) in the probabilistic harmonic estimation in uncertain transmission networks, this paper presents further comprehensive accuracy assessment in terms of different types/numbers of harmonic measurements, different stop errors to optimise training time and limited numbers of installed power quality monitors due to realistic reasons. It has been demonstrated that the sequential ANNs is sufficiently accurate and applicable in estimating harmonics in uncertain transmission network, thus contributing to facilitate the identification of potential harmonic issues, benchmarking, standard compliance and the deployment of appropriate harmonic propagation and mitigation solutions.