General lighting is undergoing a revolutionary change towards LED-based technologies. To provide firm scientific basis for the related colorimetric and photometric measurements, this paper presents the development of new white-LED-based illuminants for colorimetry, and their evaluation to recommend a new reference spectrum for calibration of photometers. Spectra of 1516 LED products were measured and used to calculate eight representative spectral power distributions for LED sources of different correlated colour temperature categories. The suitability of the calculated representative spectra for photometer calibration was studied by comparing average spectral mismatch errors with CIE Standard Illuminant A, which has been used for decades as the reference spectrum for incandescent standard lamps in calibration of photometers. It was found that in general, when compared with Standard Illuminant A, all the potential LED calibration spectra reduced spectral mismatch errors when measuring LED products. Out of the potential LED calibration spectra tested, the white LED spectrum with correlated colour temperature of 4103 K was found to be the most suitable candidate to complement Standard Illuminant A in luminous responsivity calibrations of photometers. When compared with Standard Illuminant A, employing the 4103 K reference spectrum reduced the spectral mismatch errors, on average, by approximately a factor of two in measurements of LED products and lighting. Furthermore, the new LED reference spectrum was found to reduce the spectral mismatch errors in measurements of daylight, and many types of fluorescent and discharge lamps, indicating that the proposed reference spectrum is a viable alternative to Standard Illuminant A for calibration of photometers.
This paper presents a fisheye camera method for determining spatial non-uniformity corrections in luminous flux measurements with integrating spheres. Using a fisheye camera installed into a port of an integrating sphere, the relative angular intensity distribution of the lamp under test is determined. This angular distribution is used for calculating the spatial non-uniformity correction for the lamp when combined with the spatial responsivity data of the sphere. The method was validated by comparing it to a traditional goniophotometric approach when determining spatial correction factors for 13 LED lamps with different angular spreads. The deviations between the spatial correction factors obtained using the two methods ranged from −0.15% to 0.15%. The mean magnitude of the deviations was 0.06%. For a typical LED lamp, the expanded uncertainty (k = 2) for the spatial non-uniformity correction factor was evaluated to be 0.28%. The fisheye camera method removes the need for goniophotometric measurements in determining spatial non-uniformity corrections, thus resulting in considerable system simplification. Generally, no permanent modifications to existing integrating spheres are required.
The IEC 61853 standard series aims to provide a standardized measure for PV module energy rating, namely the Climate Specific Energy Rating (CSER). For this purpose, it defines procedures for the experimental determination of input data and algorithms for calculating the CSER. However, some steps leave room for interpretation regarding the specific implementation. To analyze the impact of these ambiguities, the comparability of results and the clarity of the algorithm for calculating the CSER in part 3 of the standard, an intercomparison is performed among research organizations with 10 different implementations of the algorithm. We share the same input data, obtained by measurement of a commercial crystalline silicon PV module, among the participating organizations. Each participant then uses their individual implementations of the algorithm to calculate the resulting CSER values. The initial blind comparison reveals differences of 0.133 (14.7%) in CSER. After several comparison phases, a best practice approach is defined, which reduces the difference by a factor of 210 to below 0.001 (0.1%) in CSER for two independent PV modules. The best practice presented in this paper establishes clear guidelines for the numerical treatment of the spectral correction and power matrix extrapolation, where the methods in the standard are not clearly defined.Additionally, we provide input data and results for the PV community to test their implementations of the standard's algorithm. To identify the source of the deviations, we introduce a climate data diagnostic set. Based on our experiences, we give recommendations for the future development of the standard.
<p>The IEC 61853 standard series aims to provide a standardized measure for PV module energy rating, namely the Climate Specific Energy Rating (CSER). For this purpose, it defines procedures for the experimental determination of input data and algorithms for calculating the CSER. However, some steps leave room for interpretation regarding the specific implementation. To analyze the impact of these ambiguities, the comparability of results and the clarity of the algorithm for calculating the CSER in part 3 of the standard, an intercomparison is performed among research organizations with 10 different implementations of the algorithm. We share the same input data, obtained by measurement of a commercial crystalline silicon PV module, among the participating organizations. Each participant then uses their individual implementations of the algorithm to calculate the resulting CSER values. The initial blind comparison reveals differences of 0.133 (14.7%) in CSER. After several comparison phases, a best practice approach is defined, which reduces the difference by a factor of 210 to below 0.001 (0.1%) in CSER for two independent PV modules. The best practice presented in this paper establishes clear guidelines for the numerical treatment of the spectral correction and power matrix extrapolation, where the methods in the standard are not clearly defined. Additionally, we provide input data and results for the PV community to test their implementations of the standard’s algorithm. To identify the source of the deviations, we introduce a climate data diagnostic set. Based on our experiences, we give recommendations for the future development of the standard.</p>
In this paper, the fisheye camera method is validated for spatial non-uniformity corrections in luminous flux measurements with integrating spheres. The method was tested in eight integrating spheres with six LED lamps, and the determined angular intensity distributions and spatial non-uniformity correction factors were compared with the results of five goniophotometers. The average closeness score, describing the similarity between any two distributions, was 94.6 out of 100 for the distributions obtained using the fisheye camera method when compared with the goniophotometric results. The average closeness score for the five goniophotometers, when each goniophotometer was compared with the other four, was . On average, the relative deviation between the two methods was 0.05% when calculating the spatial corrections. The most significant sources of uncertainty for the fisheye camera method were large, view-obstructing sphere elements residing close to the camera port.
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