Understanding polymer degradation under voltage stress in PV applications
AC Partial Discharge (PD) specification has been a key design parameter for PV backsheets, as called for in IEC 61730-1. In previous work, we have studied how the structure of polymeric materials can impact their behavior in this test. In this paper, we shift the focus to consideration of potential root causes which can lead to electrical degradation in actual usage (e.g. a PV module producing DC power), and the efficacy of the AC partial discharge test in air and oil to identify these. The suitability of utilizing AC voltage stresses in PV is also challenged in this paper. New experimental evidence is presented as to why testing partial discharge with AC with specimens immersed in the medium is not suitable for testing of solar backsheet materials. PD measurements can be very different for tests performed in air versus oil, and can lead to different pass/fail conclusions for the same material. When tested in oil, materials with voids had a much lower Ve than materials with no voids; in air, there was no difference in Ve for a material prepared with and without voids. With regard to AC versus DC testing the time to breakdown versus voltage was measured for both; these are not linearly related: the AC and DC breakdown voltages for one material study were 25 kV(AC) and 85 kV (DC), more than 3× difference; the voltages which led to breakdown after 10000 seconds were 5 kV(AC) and 50 kV (DC), a 10× difference. The issues raised in this study are active areas of discussion for the TC 82 WG2 Standards Committee.
Study of Partial Discharge effects of PV backsheet component films. Structure property relationships, and measurement consistency
Partial Discharge is one of a number of tests defined in IEC61730–2, which specifies properties of photovoltaic modules and components for safe electrical and mechanical operation during their expected lifetime. The test is of practical importance in the development and manufacturing of backsheet barrier films because of the limitations it places on film thickness. The objective of this study was to understand the relationship of this test to film properties. Although simple to describe, in practice, the test is difficult to perform with consistency. We have found that this test performed in air usually provides consistent relative behavior, but that absolute values can vary on different days, or in different laboratories. We have tested several materials with varied thickness, and found a linear relationship between Ve and square root of film thickness when measured in air, in line with a surface discharge phenomenon. This paper will describe efforts to define and understand sources of variability in both inter and intra laboratory measurements, and on the measurements of some typical component materials of PV backsheets.
Digital detection, grouping and classification of partial discharge signals at DC voltage
In this paper, a digital dc PD pulse detection system with bandwidth of 10 kHz - 40 MHz is introduced, which was developed using some artificial intelligence methodologies. Focus is made on digital detection, grouping and classification of random pulse signals generated by PD phenomena at dc voltage. Digital detection is developed only resorting to a band-pass filter, a high-speed digitizer (100 MS/s) and a PC with data processing software. Grouping is realized with feature extraction of pulse waveshapes using equivalent time-frequency method (ETFM), making the 2D parameters plane or 3D parameters space, then using the unsupervised clustering Fuzzy C-Means (FCM) method to achieve fast separation for pulse sequence. And classification resorts to least square support vector machine (LS-SVM) based on a fingerprint, which is derivate form 2D histograms of basic parameters, the discharge magnitude q and the time between discharges Deltat of each sub-group. Field application is made for typical defects of oil-paper insulation under dc voltage. At last, several methods to improve separability of the grouping technique are also given for some special cases, including threshold value grouping, marginal coordinates grouping based on 2D parameters plane and grouping using ETFM preprocessed by wavelet denosing. Experimental results show that the dc PD detection system developed with artificial intelligence methodologies is practical and effective.