Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter

The ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for...

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Main Authors: SM Shafiul Alam, Anthony R. Florita, Bri-Mathias Hodge
Format: Article
Language:English
Published: Wiley 2020-03-01
Series:IET Smart Grid
Subjects:
Online Access:https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0246
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spelling doaj-ce0234f4a4b9458eb7f913c826fcd3652021-04-02T13:26:56ZengWileyIET Smart Grid2515-29472020-03-0110.1049/iet-stg.2018.0246IET-STG.2018.0246Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filterSM Shafiul Alam0Anthony R. Florita1Anthony R. Florita2Bri-Mathias Hodge3Power Systems Engineering Center, National Renewable Energy LaboratoryPower Systems Engineering Center, National Renewable Energy LaboratoryPower Systems Engineering Center, National Renewable Energy LaboratoryPower Systems Engineering Center, National Renewable Energy LaboratoryThe ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for full system observability. Furthermore, the direct impact of variable cloud formation and uncertain propagation necessitates the development and validation of a spatiotemporal model. Accordingly, this study presents the modelling and validation of the spatiotemporal variability of solar power indices at 1 minute resolution for the scale of a residential neighbourhood. The spatiotemporal model is then applied to a Multi-Rate and Event-DRIven Kalman Kriging (MREDRIKK) filter to dynamically estimate behind-the-meter PV generation. The Kriging step exploits spatial correlations to estimate PV power output at locations from where measurements are unobserved. The multi-rate feature of the MREDRIKK filter enables the sampling of measurements at a rate much lower than the temporal dynamics of the associated states. A comprehensive study is undertaken to investigate the effect of multi-rate and event-driven measurement updates on the performance of the MREDRIKK filter. In addition, the superior performance of MREDRIKK filter is represented as compared to the persistence method irrespective of the observation size.https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0246statistical analysissolar powerkalman filtersphotovoltaic power systemsdistributed power generationtime 1.0 minevent-driven kalman kriging filterpv panelsgeographical proximitypv system monitoringrobust methoddistribution gridcost-effective photovoltaic panelsevent-driven kalman kriging filterdistributed pv generation estimationevent-driven measurement updatesmredrikk filtermultirate featurepv power outputkriging stepbehind-the-meter pv generationsolar power indicesspatiotemporal variabilitymodelling validationspatiotemporal modeluncertain propagationvariable cloud formation
collection DOAJ
language English
format Article
sources DOAJ
author SM Shafiul Alam
Anthony R. Florita
Anthony R. Florita
Bri-Mathias Hodge
spellingShingle SM Shafiul Alam
Anthony R. Florita
Anthony R. Florita
Bri-Mathias Hodge
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
IET Smart Grid
statistical analysis
solar power
kalman filters
photovoltaic power systems
distributed power generation
time 1.0 min
event-driven kalman kriging filter
pv panels
geographical proximity
pv system monitoring
robust method
distribution grid
cost-effective photovoltaic panels
event-driven kalman kriging filter
distributed pv generation estimation
event-driven measurement updates
mredrikk filter
multirate feature
pv power output
kriging step
behind-the-meter pv generation
solar power indices
spatiotemporal variability
modelling validation
spatiotemporal model
uncertain propagation
variable cloud formation
author_facet SM Shafiul Alam
Anthony R. Florita
Anthony R. Florita
Bri-Mathias Hodge
author_sort SM Shafiul Alam
title Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
title_short Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
title_full Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
title_fullStr Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
title_full_unstemmed Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
title_sort distributed pv generation estimation using multi-rate and event-driven kalman kriging filter
publisher Wiley
series IET Smart Grid
issn 2515-2947
publishDate 2020-03-01
description The ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for full system observability. Furthermore, the direct impact of variable cloud formation and uncertain propagation necessitates the development and validation of a spatiotemporal model. Accordingly, this study presents the modelling and validation of the spatiotemporal variability of solar power indices at 1 minute resolution for the scale of a residential neighbourhood. The spatiotemporal model is then applied to a Multi-Rate and Event-DRIven Kalman Kriging (MREDRIKK) filter to dynamically estimate behind-the-meter PV generation. The Kriging step exploits spatial correlations to estimate PV power output at locations from where measurements are unobserved. The multi-rate feature of the MREDRIKK filter enables the sampling of measurements at a rate much lower than the temporal dynamics of the associated states. A comprehensive study is undertaken to investigate the effect of multi-rate and event-driven measurement updates on the performance of the MREDRIKK filter. In addition, the superior performance of MREDRIKK filter is represented as compared to the persistence method irrespective of the observation size.
topic statistical analysis
solar power
kalman filters
photovoltaic power systems
distributed power generation
time 1.0 min
event-driven kalman kriging filter
pv panels
geographical proximity
pv system monitoring
robust method
distribution grid
cost-effective photovoltaic panels
event-driven kalman kriging filter
distributed pv generation estimation
event-driven measurement updates
mredrikk filter
multirate feature
pv power output
kriging step
behind-the-meter pv generation
solar power indices
spatiotemporal variability
modelling validation
spatiotemporal model
uncertain propagation
variable cloud formation
url https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0246
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AT brimathiashodge distributedpvgenerationestimationusingmultirateandeventdrivenkalmankrigingfilter
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