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Prediction of voltage distribution using deep learning and identified key smart meter locations

Energy and AI 2021年第4期6卷 31-40页

作者： Maizura Mokhtar Valentin Robu David Flynn ciaran higgins Jim Whyte Caroline Loughran Fiona Fulton Smart System Research Group Heriot-Watt UniversityRiccarton CampusEH144ASEdinburghUK CWI National Research Institute for Mathematics and Computer Science1098XG AmsterdamThe Netherlands Algorithmics Group EEMCSDelft University of Technology2628XE DelftThe Netherlands Derryherk Ltd. Renfrewshire PA113BEUK Smart Meter Systems SP Energy NetworksGlasgow G25ADUK

The energy landscape for the Low-Voltage(LV)networks is undergoing rapid *** changes are driven by the increased penetration of distributed Low Carbon Technologies,both on the generation side(*** of micro-renewables)and demand side(*** vehicle charging).The previously passive‘fit-and-forget’approach to LV network management is becoming increasing inefficient to ensure its effective operation.A more agile approach to operation and planning is needed,that includes pro-active prediction and mitigation of risks to local sub-networks(such as risk of voltage deviations out of legal limits).The mass rollout of smart meters(SMs)and advances in metering infrastructure holds the promise for smarter network ***,many of the proposed methods require full observability,yet the expectation of being able to collect complete,error free data from every smart meter is unrealistic in operational ***,the smart meter(SM)roll-out has encountered significant issues,with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV *** with a comprehensive SM roll-out privacy restrictions,constrain data availability from *** address these issues,this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV *** results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution,even without the use of the high granularity personal power demand data from individual customers.

关键词： Voltage prediction Smart meters Deep neural learning Distribution network operation Big Data Analytics Analytic methods in power networks Privacy-preserving data analysis

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