Identification of periods of clear sky irradiance in time series of GHI measurements

Reno, Matthew J.; Hansen, Clifford W.

doi:10.1016/j.renene.2015.12.031

Cited by 127 publications

(84 citation statements)

References 33 publications

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“…In this case, site irradiance and system power should result in ~ R = 1, providing no way to distinguish if a tracker is stuck or functioning. Using the method of Reno and Hansen, the site global horizontal irradiance (GHI) as downloaded from NSRDB is compared to the clear‐sky GHI, as calculated by the method of Ineichen, and clear‐sky times are identified. The empirically determined threshold inputs to Reno and Hansen are given in Table .…”

Section: Methods For Determining Daily Tracker Functionalitymentioning

confidence: 99%

“…Using the method of Reno and Hansen, the site global horizontal irradiance (GHI) as downloaded from NSRDB is compared to the clear‐sky GHI, as calculated by the method of Ineichen, and clear‐sky times are identified. The empirically determined threshold inputs to Reno and Hansen are given in Table . After identifying clear‐sky times, clear‐sky days are identified by selecting those days for which at least 90% of the NSRDB datapoints between 07:00 and 18:00 are determined to be clear.…”

Section: Methods For Determining Daily Tracker Functionalitymentioning

confidence: 99%

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A methodology to analyze photovoltaic tracker uptime

Ruth

Muller

2018

Progress in Photovoltaics

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A metric is developed to analyze the daily performance of single‐axis photovoltaic (PV) trackers. The metric relies on comparing correlations between the daily time series of the PV power output and an array of simulated plane‐of‐array irradiances for the given day. Mathematical thresholds and a logic sequence are presented, so the daily tracking metric can be applied in an automated fashion on large‐scale PV systems. The results of applying the metric are visually examined against the time series of the power output data for a large number of days and for various systems. The visual inspection results suggest that overall, the algorithm is accurate in identifying stuck or functioning trackers on clear‐sky days. Visual inspection also shows that there are days that are not classified by the metric where the power output data may be sufficient to identify a stuck tracker. Based on the daily tracking metric, uptime results are calculated for 83 different inverters at 34 PV sites. The mean tracker uptime is calculated at 99% based on 2 different calculation methods. The daily tracking metric clearly has limitations, but as there is no existing metrics in the literature, it provides a valuable tool for flagging stuck trackers.

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Section: Methods For Determining Daily Tracker Functionalitymentioning

confidence: 99%

Section: Methods For Determining Daily Tracker Functionalitymentioning

confidence: 99%

A methodology to analyze photovoltaic tracker uptime

Ruth

Muller

2018

Progress in Photovoltaics

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show abstract

“…The satisfaction of CS test 1 is in general not sufficient to classify power data within J as having been generated under a clear-sky condition. Specifically, if the sky is partially cloudy during the time interval J , the measured power may heavily oscillate, but could remain quite close to the clear-sky power at the maximum [30], thus satisfying (31). To overcome this issue, a further condition on the normalized increment of the power time series is derived.…”

Section: Clear-sky Data Detectionmentioning

confidence: 99%

“…In these approaches, however, further information on the cloud cover index at the plant site is assumed to be available. In [26,27], a heuristic method for the estimation of the parameters of well-known PVUSA model [28] based on theoretical clear-sky irradiance is presented, while in [29], a recursive procedure based on the clear-sky criteria proposed in [30] is devised. However, the former approach does not allow for capturing possible parameter variations or seasonal drifts, and moreover both approaches require trial-and-error in order to manually tune a number of algorithm parameters whose values may vary significantly according to the climate zone.…”

Section: Introductionmentioning

confidence: 99%

Estimation of photovoltaic generation forecasting models using limited information

2020

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This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. An extensive experimental validation of the proposed method on real data is also presented.

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“…In [29], a period is considered to be clear if the measured PV power is higher than the 80% percentile of the set of measurements taken at the same time of day, during the previous 15 days. In [23] this method is combined with the clear-sky detection routine described in [30], which uses GHI observation as input and a set of 5 extraction parameters. In this paper, we first developed a selection based on the smoothed power signals: the power output of each PV plant is filtered using a second order low-pass Butterworth filter [31].…”

Section: The Presence Of Shadows Affects the Relationships Between Thmentioning

confidence: 99%

An unsupervised method for estimating the global horizontal irradiance from photovoltaic power measurements

Nespoli

Medici

2017

Solar Energy

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The precise calculation of solar irradiance is pivotal for forecasting the electric power generated by PV plants. However, on-ground measurements are expensive and are generally not performed for small and medium-sized PV plants. Satellite-based services represent a valid alternative to on-site measurements, but their space-time resolution is limited. In this paper we present a method for estimating the global horizontal irradiance (GHI) from the power measurements of one or more photovoltaic (PV) systems located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. It can estimate the nominal power and orientation of multiple PV fields, using only the aggregated power signal from their PV power plant. Moreover, if more than one PV power plant is available, the different signals are reconciled using outliers detection and assessing shading patterns for each PV plant. Results from two case studies located in Switzerland are presented here. The performance of the proposed method at estimating GHI is compared with that of free and commercial satellite services. Our results show that the method presented here is generally better than satellite-based services, especially at high temporal resolutions.

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Identification of periods of clear sky irradiance in time series of GHI measurements

Cited by 127 publications

References 33 publications

A methodology to analyze photovoltaic tracker uptime

A methodology to analyze photovoltaic tracker uptime

Estimation of photovoltaic generation forecasting models using limited information

An unsupervised method for estimating the global horizontal irradiance from photovoltaic power measurements

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