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- W2904259436 abstract "Traditional feeder voltage estimation techniques in low voltage networks lack the capability to analyse 2-way power flow and are rooted in probabilistic techniques developed in the 1940's. With the increasing penetration of distributed generation, there is a greater expectation on the network operator to reduce over-voltage complaints and have knowledge of accurate voltages along feeders. Smart meters installed in the developed world record power usage, but often the voltages at only those time intervals where they fall outside the prescribed limits. This paper presents a modern approach for estimating feeder voltages, derived from a regression model based on power system first principles. The parameters of the model are determined using standard machine learning techniques, and account for demand diversity, phase unbalance along the feeder and the transformer tap position. The model is trained using half-hourly household consumption data from the utility and voltage results from the load flow simulation of a real-world network created for a given topology. The model is then shown to predict voltage variations in the network with new consumers added in. These predictions, when assessed against traditional voltage estimation practices or simulation results, provide a close match at each time instance. The proposed methodology hence provides a new way of computing accurate voltage drop and rise in a feeder with new prosumers. It allows the network operator to gain a better understanding of dynamic distribution network voltages, save on capital expenditure due to avoided over-investment, and reduce risk of voltage excursions when adding new prosumers." @default.
- W2904259436 created "2018-12-22" @default.
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- W2904259436 date "2018-10-01" @default.
- W2904259436 modified "2023-09-26" @default.
- W2904259436 title "Predicting Voltage Variations in Low Voltage Networks with Prosumers" @default.
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- W2904259436 doi "https://doi.org/10.1109/appeec.2018.8566480" @default.
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