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Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter
by
Alam, SM Shafiul
, Florita, Anthony R.
, Hodge, Bri‐Mathias
in
auto-regressive model
/ B0240Z Other topics in statistics
/ B8120K Distributed power generation
/ B8250 Solar power stations and photovoltaic power systems
/ behind-the-meter pv generation
/ cost-effective photovoltaic panels
/ Distributed generation
/ distributed power generation
/ distributed PV generation estimation
/ distribution grid
/ event-driven kalman kriging filter
/ event-driven measurement updates
/ Expected values
/ Geographical distribution
/ geographical proximity
/ k-means clustering
/ Kalman filter
/ Kalman filters
/ Kriging
/ Kriging step
/ modelling validation
/ MREDRIKK filter
/ multirate feature
/ Panels
/ Photovoltaic cells
/ photovoltaic power systems
/ PV panels
/ PV power output
/ PV system monitoring
/ Rain
/ Residential areas
/ Residential development
/ Residential energy
/ robust method
/ Sensors
/ Signal processing
/ SOLAR ENERGY
/ solar power
/ solar power indices
/ Spacetime
/ spatiotemporal model
/ spatiotemporal variability
/ statistical analysis
/ time 1.0 min
/ time 1.0 min
/ Time series
/ uncertain propagation
/ variable cloud formation
2020
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Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter
by
Alam, SM Shafiul
, Florita, Anthony R.
, Hodge, Bri‐Mathias
in
auto-regressive model
/ B0240Z Other topics in statistics
/ B8120K Distributed power generation
/ B8250 Solar power stations and photovoltaic power systems
/ behind-the-meter pv generation
/ cost-effective photovoltaic panels
/ Distributed generation
/ distributed power generation
/ distributed PV generation estimation
/ distribution grid
/ event-driven kalman kriging filter
/ event-driven measurement updates
/ Expected values
/ Geographical distribution
/ geographical proximity
/ k-means clustering
/ Kalman filter
/ Kalman filters
/ Kriging
/ Kriging step
/ modelling validation
/ MREDRIKK filter
/ multirate feature
/ Panels
/ Photovoltaic cells
/ photovoltaic power systems
/ PV panels
/ PV power output
/ PV system monitoring
/ Rain
/ Residential areas
/ Residential development
/ Residential energy
/ robust method
/ Sensors
/ Signal processing
/ SOLAR ENERGY
/ solar power
/ solar power indices
/ Spacetime
/ spatiotemporal model
/ spatiotemporal variability
/ statistical analysis
/ time 1.0 min
/ time 1.0 min
/ Time series
/ uncertain propagation
/ variable cloud formation
2020
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Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter
by
Alam, SM Shafiul
, Florita, Anthony R.
, Hodge, Bri‐Mathias
in
auto-regressive model
/ B0240Z Other topics in statistics
/ B8120K Distributed power generation
/ B8250 Solar power stations and photovoltaic power systems
/ behind-the-meter pv generation
/ cost-effective photovoltaic panels
/ Distributed generation
/ distributed power generation
/ distributed PV generation estimation
/ distribution grid
/ event-driven kalman kriging filter
/ event-driven measurement updates
/ Expected values
/ Geographical distribution
/ geographical proximity
/ k-means clustering
/ Kalman filter
/ Kalman filters
/ Kriging
/ Kriging step
/ modelling validation
/ MREDRIKK filter
/ multirate feature
/ Panels
/ Photovoltaic cells
/ photovoltaic power systems
/ PV panels
/ PV power output
/ PV system monitoring
/ Rain
/ Residential areas
/ Residential development
/ Residential energy
/ robust method
/ Sensors
/ Signal processing
/ SOLAR ENERGY
/ solar power
/ solar power indices
/ Spacetime
/ spatiotemporal model
/ spatiotemporal variability
/ statistical analysis
/ time 1.0 min
/ time 1.0 min
/ Time series
/ uncertain propagation
/ variable cloud formation
2020
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Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter
Journal Article
Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter
2020
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Overview
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.
Publisher
The Institution of Engineering and Technology,John Wiley & Sons, Inc,Institution of Engineering and Technology (IET),Wiley
Subject
/ B0240Z Other topics in statistics
/ B8120K Distributed power generation
/ B8250 Solar power stations and photovoltaic power systems
/ behind-the-meter pv generation
/ cost-effective photovoltaic panels
/ distributed power generation
/ distributed PV generation estimation
/ event-driven kalman kriging filter
/ event-driven measurement updates
/ Kriging
/ Panels
/ Rain
/ Sensors
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