Correcting Output Data from Distributed PV Systems for Performance Analysis

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1 Correcting Output Data from Distributed PV Systems for Performance Analysis Navid Haghdadi, Anna Bruce, Iain MacGill Photo: Tyree Energy Technology Building, University of New South Wales! Why performance analyses of PV systems? understanding the yield of PV systems and the cost, quality and reliability of PV electricity.! More than 1 million rooftop PV systems already installed in Australia.! Publically available Data from significant numbers of small PV inverters ( such as PVOutput.org,..) But!!! limited usefulness for performance analysis due to poor quality of the data and missing information.

2 After a few years of ramping up Renewables we have faced with a large number of data which can be used for performance evaluation Big data may mean more information, but it also means more false information*. That s why we need more people working on this ever increasing amount of data! * Nassim Taleb Photo: Evaluating and correcting current datasets to make them useful for other studies - Identifying and correcting wrongly reported parameters - Detecting and filling missing data Figure: Average daily value of 50 schools in Australia

3 PVOutput.org : free service for sharing, comparing and monitoring live solar photovoltaic (PV) and energy consumption data - More than 7000 systems in Australia reporting daily output - More than 2000 systems in Australia reporting intra-hourly output Distribution of Tilt and Orientation 24% of systems have not reported Tilt angle 17% have reported zero which is not likely to be correct So, all tilt and orientation angles should be checked.. Need to be evaluated! Tilt angle distribution Orientation angle distribution

4 Detection of wrong user-reported information Example of evaluating intra daily PV output data Wrong reported data Missing Data Wrong time Noise Missing Periods Wrong daylight saving Figure: 4 years 15 min interval PV output data of a Solar School in NSW

5 Filling Missing Data based on Output data of neighbor systems A B C A Missing Interval A,D A,B,D A,B,C,D A,B,C A,D A A,B D I II III I: Cannot be retrieved III: Retrieved by C II: Retrieved by B and C (contribution of B and C is set based on number of days and accuracy of regression)! Correlation between output of different systems located close to each other Well correlated Weakly correlated

6 10-fold Cross validation result Real vs. estimated values for missing data (data has been deliberately removed for randomly selected times in each fold and then is estimated with the proposed method) Finding Similar Systems for using as predictors using clustering yearly profile Figure: 8 clusters of daily generated energy of PV systems Method: Grouping yearly data of 4000 PV systems in Australia into 8 Clusters and look for best predictors of each system within the cluster to which this system belongs

7 10-fold Cross validation result for two systems of cluster number 4 using 8 predictors of the same cluster Correcting wrongly reported parameters of PV systems Figure: Natural logarithm of ratio between output energy of morning (before solar noon) and afternoon for different tilt and orientation angles

8 Correcting wrongly reported parameters of PV systems Result of Parameter Estimation of Simulated PV system based on real weather station data (Broome Airport Weather Station, SA, Jan Dec 2003 ) * Measured global and diffuse irradiation as well as temperature have been used to model the output power of PV module using SAM model for different tilt and orientation angles and then have been tested with the proposed method. Detection of Shading Probable Reason!

9 Detection of Module Orientation N School 34 Parameter Estimation of PV systems based on historical data

10 Parameter Estimation of PV systems based on historical data

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