QCPV: A quality control algorithm for distributed photovoltaic array power output

Killinger, Sven; Engerer, Nicholas A.; Müller, Björn

doi:10.1016/j.solener.2016.12.053

Cited by 55 publications

(34 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The primary difference of the two methods is the equation for deriving the GTI from the P ac . From Killinger et al (2016) and Killinger et al (2017), the specific power may be represented by Eq. (13):…”

Section: Comparison With a Power-projection Methodsmentioning

confidence: 99%

Photovoltaic system derived data for determining the solar resource and for modeling the performance of other photovoltaic systems

Marion

Smith²

2017

Solar Energy

View full text Add to dashboard Cite

Using performance data from some of the millions of installed photovoltaic (PV) modules with micro-inverters may afford the opportunity to provide ground-based solar resource data critical for developing PV projects. A method was developed to backsolve for the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the measured ac power of southfacing PV module/micro-inverter systems. The method was validated using one year of irradiance and PV performance measurements for five PV systems, each with a different tilt/azimuth orientation, and located in Golden, Colorado. Compared to using a measured global horizontal irradiance for PV performance model input, using the back-solved values of DNI and DHI only increased the range of mean bias deviations from measured values by 0.6% for the modeled annual averages of the global tilt irradiance and ac power for the five PV systems. Correcting for angle-of-incidence effects is an important feature of the method to prevent underestimating the solar resource and for modeling the performance of PV systems with more dissimilar PV module orientations. The results for the method were also shown more favorable than the results when using an existing power projection method for estimating the ac power.

show abstract

Section: Comparison With a Power-projection Methodsmentioning

confidence: 99%

Photovoltaic system derived data for determining the solar resource and for modeling the performance of other photovoltaic systems

Marion

Smith²

2017

Solar Energy

View full text Add to dashboard Cite

show abstract

“…Owing to factors such as measurement uncertainty or irradiance modeling error, experimental data often contain spurious values. There is no universally-accepted QC procedure (Gueymard and Ruiz-Arias, 2016), but recommended QC methods for surface radiation measurements (Long and Shi, 2008;García et al, 2014) and PV power output (Killinger et al, 2017) do exist. Recently, owing to the advances in remote-sensing technology, satellite-derived irradiance products have been shown to help perform QC on irradiance measurements (Urraca et al, 2017;Perez et al, 2017).…”

Section: Some Seemingly Trivial Implementation Issuesmentioning

confidence: 99%

Verification of deterministic solar forecasts

et al. 2020

Self Cite

View full text Add to dashboard Cite

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

show abstract

“…Gathering all these metadata is difficult and time consuming. Moreover, when available, the data contained in databases could be imprecise or flawed [23]. This could lead to erroneous estimations.…”

Section: Outline and Objectivementioning

confidence: 99%

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

Nespoli

Medici

2017

Solar Energy

View full text Add to dashboard Cite

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.

show abstract

QCPV: A quality control algorithm for distributed photovoltaic array power output

Cited by 55 publications

References 28 publications

Photovoltaic system derived data for determining the solar resource and for modeling the performance of other photovoltaic systems

Photovoltaic system derived data for determining the solar resource and for modeling the performance of other photovoltaic systems

Verification of deterministic solar forecasts

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

Contact Info

Product

Resources

About