Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning
| Autor: | Maizura Mokhtar, Ciaran Higgins, David Flynn, Jim Whyte, Valentin Robu, Fiona Fulton, Caroline Loughran |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Artificial neural network Computer science business.industry Smart meter 020209 energy Deep learning 020208 electrical & electronic engineering 02 engineering and technology Reliability engineering Power (physics) Machine Learning (cs.LG) Network management 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Artificial intelligence Observability Electrical Engineering and Systems Science - Signal Processing business Low voltage Voltage |
| Zdroj: | ISGT Europe |
| Popis: | The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive `fit-and-forget' approach to LV network management will be inefficient to ensure its effective operations. A more adaptive approach is required that includes the prediction of risk and capacity of the circuits. Many of the proposed methods require full observability of the networks, motivating the installations of smart meters and advance metering infrastructure in many countries. However, the expectation of `perfect data' is unrealistic in operational reality. Smart meter (SM) roll-out can have its issues, which may resulted in low-likelihood of full SM coverage for all LV networks. This, together with privacy requirements that limit the availability of high granularity demand power data have resulted in the low uptake of many of the presented methods. To address this issue, Deep Learning Neural Network is proposed to predict the voltage distribution with partial SM coverage. The results show that SM measurements from key locations are sufficient for effective prediction of voltage distribution. 9th IEEE International Conference on Innovative Smart Grid Technologies (IEEE ISGT Europe 2019) |
| Databáze: | OpenAIRE |
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