Ransome_poster_2022

MLFM FITTING OUTDOOR MATRICES pr_dcG, t_mod Fit data non-weighted or “weighted by occurrence” [Gantner 78 c-Si] normalised efficiency pr_dc DRAFT FOR CFV Do not distribute MLFM COEFFICIENTS ARE INDEPENDENT FOR UNIQUE MATRIX FITS Alter each of the mlfm4 coefficients c_c, c_t, c_lg, c_g separately Show sensitivity shape and magnitude of apparent performance change red arrows Changes are independent meaning there’s a unique best fit 1 c_c vs. 10 rise Constant change Fits “meas/nameplate” 4 c_g vs. 50 rise Changes most at high g Fits r_series loss 3 c_lg vs. 20 rise Changes most at low g Fits v_oc, r_shunt drop 2 c_t vs. 20 rise Change ∝ t_mod-25C Fits gamma, beta etc. STC DEFINITIONS 18 PVPMC 23-24 Aug 2022 Salt Lake City, USA IMPROVING ANALYSIS METHODS FOR IEC 61853 MATRIX MEASUREMENTS Steve Ransome1 SRCL and Juergen Sutterlueti Gantner Instruments 1 stevesteveransome.com www.steveransome.com MLFM FITTING v_oc, v_mp, pr_dc INDOOR vs. OUTDOOR Indoor CFV IEC61853 Outdoor Gantner Tempe AZ, 1year Module 5 Canadian Solar rmse Module 78 Solar World wrmse INTRODUCTION IEC 61853 “Matrix method” defines 28 dc measurements at up to 7 irradiances g0.1 – 1.1 kW/m2 4 temperatures t_mod15, 25, 50, 75C 4 independent coefficients are needed to uniquely fit a performance matrix 1 c_c measured/nameplate performance at STC 2 c_t temperature coefficient 1/X dX/dt_mod [1/K] Xp_mp, v_oc . 3 c_lg low light drop caused by v_oc drop or r_shunt loss increasing at low g 4 c_g high light drop caused by r_series as loss I2.r_series g2.r_series Matrices of pr_dc, v_oc, v_mp etc. can be fitted easily with a mechanistic model “MLFM4” with 50 of the fit errors of SAPM or PVGIS as neither of them model r_series correctly, it needs a c_g term [PVSC-49] SUMMARY References www.steveransome.com email stevesteveransome.com [PVSC 49] http//www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf PVPMC/PVLIB https//pvpmc.sandia.gov/ https//github.com/pvlib/pvlib-python Acknowledgements Gantner Instruments and CFV for measurement data https//pvpmc.sandia.gov/download/7701/ Dots Measured Smooth lines Fits MLFM is better than SAPM or PVGIS fitting matrices for all parameters with only 50 of their rmse they don’t model r_series [see PVSC49] MLFM has optimised fits to indoor measurements and fits good outdoor measurements well Weighting outdoor measurements by occurrence mean infrequent extreme or transient data don’t affect the fits The MLFM matrix fit c_t parameter is an accurate temperature coefficient without needing extra measurements at 1000W/m2 GLOSSARY nomenclature and definitions [unit] g measured poa irradiance 0.1 – 1.1 [kW/m2] t_mod measured module temperature 15,25,50,75 [C] g_stc 1 [kW/m2] t_stc 25 [C] dt t_mod – 25 [C] t_k t_mod 273.15 [K] t_stc_k 298.15 [K] normalise data for easier fitting and understanding NAMING PREFIXES measured normalised fitted, stc, lic, noct etc. norm_i_sc meas_i_sc / stc_i_sc / g [] norm_v_oc meas_v_oc / stc_v_oc [] norm_pr_dc meas_p_mp / stc_p_mp / g [] norm_i_mp meas_i_mp / stc_i_mp / g [] norm_v_mp meas_v_mp / stc_v_mp [] MLFM4 4 meaningful, normalised coefficients 1 const 2 temp coeff 3 low light improvement 4 high light norm_param c_c c_t*t_mod–25 c_lg * log10g*t_k/t_stc_k c_g*g Occurrence of external data at Tempe, AZ, 1m each h for 1 yr Most frequent region g 0.8-1.0kW/m2, t_mod 40-65C Can ignore least frequent and any ‘outliers’ 0.1 A BETTER METHOD TO FIND TEMPERATURE COEFFICIENTS Temperature coefficients can be more simply and accurately derived using c_t from mlfm matrix fits without needing extra measurements and trend fits as used in IEC 61853 Fitting good indoor vs good outdoor data Weight outdoor data by occurrence Outdoor weighted v_mp and v_oc fits can be as good as indoor Higher pr_dc variability outdoors soiling, aoi, beam fraction and spectrum affect i_sc MLFM fits matrices well v_oc c_t -0.32/K wrmse 0.17 v_oc c_t -0.30/K rmse 0.35 v_mp c_t -0.39/K rmse 0.30 pr_dc c_t -0.40/K rmse 0.18 Non-weighted pr_dc fit Weighted pr_dc fit STC LIC STC MLFM fits both weighted and unweighted well 4 independent terms are needed to model matrix behaviour IV curve terms IEC 61853 values and linear trend fits Not needed IEC 61853 TREND values and residual from MLFM fits c_t -0.46/K gamma rmse 0.99 wrmse 0.61 c_t -0.45/K gamma rmse 1.05 wrmse 0.55 STC v_mp c_t -0.40/K wrmse 0.20 pr_dc c_t -0.45/K wrmse 0.55 Tmod C Gi W/m2 Weighted rmse wrmse is 50 the rmse for good outdoor data as it tends to fit well behaved, bright, hot conditions and not cool, dull, infrequent and/or outliers