A tuning routine to correct systematic influences in reference PV systems' power outputs. (15th November 2017)

• Record Type: Journal Article
• Authors: Killinger, Sven; Bright, Jamie M.; Lingfors, David; Engerer, Nicholas A.
• Is Part Of: Solar energy. Volume 157(2017)
• Page Start: 1082
• Page End: 1094
• Publication Date: 2017-11-15
• Subjects: PV power measurements -- Quality control -- PV tuning -- Upscaling; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2017.09.001
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: The goal of this paper is to produce and select representative PV power measurements. The three-step method quality controls, tunes and reduces variances of PV power data. PV upscaling approaches are demonstrably improved in an evaluation of 78 PV systems. Systematic PV power generation influences are balanced with this proposed method. Uncertainty and representation of PV reference system datasets are improved. Abstract: Power output measurements from PV systems are subject to a wide variety of systematic external and internal influences, such as shading, soiling, degradation, module and inverter quality issues and other system-level losses. All of these influences upon PV power measurements make the use of PV power output datasets for higher-level analysis problematic, particularly in their use as reference PV systems for estimating the power of a regional portfolio. To address these issues, we present a three-step method. Firstly, a parameterisation and quality control of power measurements is performed, which also corrects for consistent inefficiencies by a loss factor LF . Secondly, the detection of systematic de-ratings affecting PV system power output differently for each time step of the day (predominantly due to shading) together with the implementation of a subsequent "re-rating" of the power output measurements in a process referred to as tuning. The pivotal element of this approach is a 30-day running 90th percentile of the clear-sky index for photovoltaics k pv and the computation of a daily de-rating profile. Lastly, high k pv related variance in the early morning and evening is detected and filtered. Whilst these three methods are independent of each other, we suggest applying them in combination following the same order as in our paper. Cross-validations of these methods demonstrate significant improvements to the PV power measurement profiles, particularly in their use as reference PV systems for upscaling approaches. The RMSE falls from 0.174 to 0.09 W / W p, rRMSE from 46.5 % to 21.9 %, MAPE from 47.9 % to 20.8 % and the correlation r increases from 0.767 to 0.919 . Hence, we report overall improvements to RMSE, rRMSE, MAPE and r by 48 %, 53 %, 57 % and 20 %, respectively.