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Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [ABSTRACT FROM AUTHOR]

Abstract: Maturity index (MI) is a key determinant of pea softness and ultimately retail value. Pea seed development goes through the optimal market stage for human consumption about a week before harvest. MI increases rapidly during the last 3–4 days prior to the optimal harvest which is when there is a need for better forecasting capability. Extensive field sampling is currently used to track MI in each of the individual paddocks, though it has limited ability to predict MI more than a day ahead. We developed an Artificial Neural Network (ANN) model that complements field sampling by forecasting the MI trend several days ahead. It was built using historical harvest information along with weather and climate forecasts. We implement and evaluate the ANN in a large pea growing region in Tasmania, Australia, and this paper highlights key results. The ANN produced an average error of 31.8MI units when forecasting MI at harvest with a 7-day lead time versus the current manual method which produced an average error of 36.6MI units for a lead time of 2 days. This means the model provides the ability to not only harvest peas closer to their ideal MI but also plan harvesting and transport logistics with a much greater lead time. [Copyright &y& Elsevier]