advertisement

energies Case Report Performance Analysis of a Grid-Connected Upgraded Metallurgical Grade Silicon Photovoltaic System Chao Huang 1, *, Michael Edesess 2 , Alain Bensoussan 1,3 and Kwok L. Tsui 1 1 2 3 * Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong, China; [email protected] (A.B.); [email protected] (K.L.T.) Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, Hong Kong, China; [email protected] School of Management, University of Texas at Dallas, Richardson, TX 75080-3021, USA Correspondence: [email protected]; Tel.: +852-3442-9321 Academic Editor: Tapas Mallick Received: 2 March 2016; Accepted: 27 April 2016; Published: 5 May 2016 Abstract: Because of their low cost, photovoltaic (PV) cells made from upgraded metallurgical grade silicon (UMG-Si) are a promising alternative to conventional solar grade silicon-based PV cells. This study investigates the outdoor performance of a 1.26 kW grid-connected UMG-Si PV system over five years, reporting the energy yields and performance ratio and estimating the long-term performance degradation rate. To make this investigation more meaningful, the performance of a mono-Si PV system installed at the same place and studied during the same period of time is presented for reference. Furthermore, this study systematizes and rationalizes the necessity of a data selection and filtering process to improve the accuracy of degradation rate estimation. The impact of plane-of-array irradiation threshold for data filtering on performance ratio and degradation rate is also studied. The UMG-Si PV system’s monthly performance ratio after data filtering ranged from 84% to 93% over the observation period. The annual degradation rate was 0.44% derived from time series of monthly performance ratio using the classical decomposition method. A comparison of performance ratio and degradation rate to conventional crystalline silicon-based PV systems suggests that performance of the UMG-Si PV system is comparable to that of conventional systems. Keywords: photovoltaic; upgraded metallurgical grade silicon; data filtering; performance ratio; degradation rate 1. Introduction During the last decade, the global photovoltaic (PV) market has grown at an average rate of 50% annually [1]. However, the high cost of PV cells made from conventional solar grade silicon is a hindrance to the rapid growth of the PV industry. One promising alternative is to use PV cells made from upgraded metallurgical grade silicon (UMG-Si) purified via the metallurgical process route [2–4]. The production of UMG-Si can be five times more energy efficiency than the conventional Siemens process to produce solar grade silicon; hence, the cost of UMG-Si is much lower [5,6]. However, UMG-Si contains more impurities, resulting in lower efficiency of crystalline silicon solar cells made from such materials than from the conventional Siemens process. Thanks to the improvements in the UMG purification process, Zheng et al., in 2016 [7] reported solar cells fabricated with 100% UMG-Si with peak efficiency of 20.9%, and 21.9% for a control device made from electronic grade silicon. Nevertheless, the potential advantage of low cost can be impaired if outdoor efficiency is reduced by degradation of quality due to high concentration of metallic and non-metallic impurities [8]. Manufacturers have started producing PV cells made of UMG-Si, and demonstration projects have been launched. PV modules made from UMG-Si manufactured by Canadian Solar and Silicor Materials Energies 2016, 9, 342; doi:10.3390/en9050342 www.mdpi.com/journal/energies Energies 2016, 9, 342 2 of 15 are being tested at the National Renewable Energy Laboratory (NREL) [9]. In China, a UMG-Si PV plant with a capacity of 330 kW has been built [10]. Measurement and reporting of the outdoor performance of UMG-Si PV systems will help facilitate further improvements and better applications. The outdoor performance of a PV system strongly depends on effective irradiation and module temperature. The power output as well as the module efficiency of crystalline silicon modules decreases with increasing cell temperature. In the field, however, there is often a difference between the observed incident irradiation on the surface of PV modules and the effective irradiation that is useful for PV production. This difference is explained by the influence of the solar spectrum and solar angle-of-incidence (AOI). PV modules respond more strongly to a certain range of wavelength of light contained in the solar spectrum [11]; the latter describes the intensity of irradiation at each wavelength of the incoming light, and varies as the day progresses. The influence of AOI on PV performance is due to two causes. One is the so called “cosine effect” which has already been removed from the pyranometer’s irradiation measurement; optical loss, however, related to AOI still remains [12]. The performance of PV modules tends to degrade over time. Degradation rate, Rd , which characterizes the changes of PV power delivery capacity over time, is of great importance in evaluating the long-term reliability of a PV system. Jordan et al. [13] surveyed nearly 2000 reported degradation rates, finding a median of 0.5% with a range from ´0.2% to 4.2%. Rd is usually derived from time series of PV performance metrics such as performance ratio (PR). To obtain an accurate degradation rate, it is desirable for the impacts of non-degradation factors to be isolated from PV performance metrics. To improve the accuracy of degradation rate estimation, the research presented in a number of previous articles [14–16] has filtered the observed data, discarding data points with low levels of irradiation, eliminating data points on cloudy days with large fluctuations in irradiation, and removing outliers. Analytical techniques also greatly influence the accuracy of degradation rate estimation. In the literature, linear regression is the most widely used analytical technique to estimate Rd from time series of performance metrics [17]. Other analytical techniques, classical decomposition and autoregressive integrated moving average (ARIMA), were used in [18] to estimate Rd with stronger robustness against data shift, outliers, and shorter observation time than linear regression. The aim of this study is to investigate the long-term outdoor performance of a 1.26 kW grid-connected UMG-Si PV system installed at the NREL in Golden, Colorado, USA. This investigation consists of two parts: evaluation of system performance in converting solar energy to electricity; and estimation of the long-term performance degradation rate. To make the investigation more meaningful, the performance of another 1 kW PV system consisting of PV cells made from monocrystalline silicon surrounded by ultra-thin amorphous silicon layers installed at the same place and studied during the same period of time is presented for reference. In the following sections, these two PV systems are identified as UMG-Si and mono-Si PV systems, respectively. Furthermore, this study strives to systematize and rationalize the necessity of using a data selection and filtering procedure to obtain more reliable long-term performance metrics for degradation rate estimation. The impact of a plane-of-array (POA) irradiation threshold for data filtering on PR and estimated degradation rate is also studied. The remainder of the paper is organized as follows. Section 2 briefly introduces the PV systems and database used in this study. In Section 3, the methods used to evaluate PV performance and to estimate the degradation rate are developed. Results and discussion are in Section 4. A comparison of PR and Rd for the UMG-Si PV system to those for conventional crystalline silicon-based PV modules is made in Section 5. This is followed by conclusions in Section 6. 2. PV System and Database The UMG-Si PV system and the mono-Si PV system are located in Golden, CO, USA (latitude 39.74˝ N, longitude 105.18˝ W). The 1.26 kW UMG-Si PV array consists of 6 polycrystalline silicon based CS6P-210PE modules manufactured by Canadian Solar, and the 1 kW mono-Si PV array consists of 5 HIP-200BA3 modules manufactured by Sanyo (San Diego, CA, USA). The PV arrays are installed at a Energies 2016, 9, 342 3 of 15 fixed direction with a tilt angle of 40˝ to receive maximal average solar irradiation. The PV arrays are connected to inverters manufactured by PV Powered for the UMG-Si PV system and by SMA for the mono-Si PV system. The inverter performs maximal power point tracking, which controls voltage at the optimum level to maximize power delivery. Thus, in this study the performance of PV is measured at the maximal power point. Electrical properties of the two kinds of modules are presented in Table 1. Table 1. Electrical properties of photovoltaic (PV) modules. Property UMG-Si mono-Si Maximal power at STC (Pmax ) Maximal power voltage (V mp ) Maximal power current (Imp ) Open circuit voltage (V oc ) Short circuit current (Isc ) Temperature coefficient (Pmax ) 210 W 29.0 V 7.25 A 36.4 V 7.89 A ´0.42%/˝ C 200 W 55.8 V 3.59 A 68.7 V 3.83 A ´0.29%/˝ C The continuously observed data for the PV systems is measured using a Campbell Scientific data logger by NREL [9]. DC performance is measured over a 50 mV current shunt and voltage divider. Meteorological and electrical data are sampled every 5 s and averaged every minute. POA irradiation is measured with a Kipp and Zonen CMP-11 broadband pyranometer. Module temperature is measured with three T-type thermocouples. The mono-Si PV system was installed almost three years earlier than the UMG-Si PV system; however, only the performance of both systems from 1 January 2011 to 31 December 2015 will be reported and used to analyze the annual performance degradation rate. 3. Methods 3.1. Evaluation of PV Performance IEC standard 61724 [19] defines normalized parameters like PV array yield (Ya ) and reference yield (Yr ) to quantify the energy production and solar resources over a period, and defines PR to describe the overall energy transformation capacity of a PV system. These normalized quantities can be updated daily, monthly, or yearly, and are often used to compare the performance of different PV systems. In this study, DC output from the UMG-Si PV array is used to minimize the effect of the inverter so as to focus on the performance of the PV array itself. Ya is defined as energy E (integration of power over time) generated by the array during the given period divided by the array nominal power P0 (the theoretical power output given the reference irradiation and temperature): E phq P0 Ya “ (1) Yr is defined as the ratio of the measured POA irradiation G over the given period to the reference irradiation G0 : Yr “ G phq G0 (2) PR is defined as Ya divided by Yr : PR “ 100 Ya p%q Yr (3) In addition to the daily or monthly PR, we analyze its instantaneous counterpart, PRt , which is given by: PRt “ 100 Pt {P0 p%q Gt {G0 (4) Energies 2016, 9, 342 4 of 15 where Pt and Gt are instantaneous array power output and POA irradiation at time t, respectively. PR over a period (daily or monthly) computed from instantaneous values is given by: ř Pt {P0 PR “ 100 ř p%q (5) Gt {G0 Hence, PR is the ratio of the array’s output as a percentage of its nominal output, to actual irradiation as a percentage of nominal irradiation, indicating the response of the PV array to irradiation. PR normalizes power output for the most important driver of its variability, the first-order irradiation; thus, it removes this source of variability from power output variability making it a suitable performance metric to investigate long-term changes in PV performance. This study will report the yields and PR of the UMG-Si PV system during the observation period in different time scales: minutely, daily, and monthly. Moreover, this study strives to investigate factors which make certain PR data points substantially less reliable in reflecting PV response to irradiation than others, thereby systematizing and providing the rationale for data filtering to obtain more reliable performance metrics for degradation rate estimation. 3.2. Estimation of Degradation Rate Degradation rate is usually estimated from time series of PV performance metrics. In this study, the statistical technique of classical decomposition is used to derive the degradation rate from monthly PR. Classical decomposition, in which the signal is partitioned into three components, the trend, the seasonality, and the irregular component, is mathematically expressed as: Yt “ Tt ` St ` et , for t “ 1, . . . , n (6) where Yt is the signal, Tt is the component of trend, St the component of seasonality, and et the remaining irregular component assumed to be random variables with zero mean and constant variance at time t [20,21]. In our study, Yt refers to the time series of monthly PR over the observed period of 60 months; hence, t ranges from 1 to 60 (n equals to 60). The component of trend is obtained from the original data by employing a two-step centered twelve-month moving average. For a 2ˆ m moving average, (here m = 12, the number of months in a year), the centered average at time t is arrived at by: ¨ Tt “ 1 ˝ 2m t`m {2´1 ÿ t`ÿ m{2 Yi ` i“t´m{2 ˛ Yi ‚ (7) i“t´m{2`1 where Tt is the trend for m/2 < t < n-m/2. The degradation rate is obtained by applying a linear regression on the extracted trend Tt : y “ ax ` b (8) where a is the slope of the line fitted to the series of Tt obtained in Equation (7) and b its corresponding intercept. b represents the estimated initial PR, and a represents the estimated monthly performance loss; thus, the annual degradation rate in percentage is expressed as: Rd “ 100 12a p%q b (9) With the classical decomposition method, the impact of seasonal variation on Yt is expected to be reduced. The component of seasonality in each month throughout the observation period is derived by subtracting the extracted trend from the original signal. To eliminate the variation of seasonality in a given month of different years, average the values of each respective month throughout the observation period under the assumption that the seasonality in each month does not vary from year to year. After the seasonality is known, and using the trend, the remaining irregular component is calculated with Equation (6). derived by subtracting the extracted trend from the original signal. To eliminate the variation of seasonality in a given month of different years, average the values of each respective month throughout the observation period under the assumption that the seasonality in each month does not vary from year to year. After the seasonality is known, and using the trend, the remaining Energies 2016, 9, 342 5 of 15 irregular component is calculated with Equation (6). Results and and Discussion Discussion 4.4. Results 4.1. 4.1.PV PVPerformance Performance 4.1.1. 4.1.1.Instantaneous InstantaneousPerformance Performance Instantaneous Instantaneousperformance performanceisisanalyzed analyzedon ononly onlyfive fiveconsecutive consecutivedays daysfrom from2828March Marchtoto1 1April April 2015. 2015.These Thesedays daysare areselected selecteddue duetototheir theirvariety varietyofofweather weatherconditions. conditions.Figure Figure11depicts depictsthe thePOA POA irradiation, power output, ambient temperature, and module temperature at time intervals of 1 min. irradiation, power output, ambient temperature, and module temperature at time intervals of 1 28minute. March 28 andMarch 29 March sunnywere dayssunny on which were no large fluctuations in irradiationin and were 29 March daysthere on which there were no large fluctuations over short time intervals. 30 March was a cloudy-sunny day, while 31 March was a sunny-cloudy irradiation over short time intervals. 30 March was a cloudy-sunny day, while 31 March was a day. 1 April wasday. a cloudy day on awhich theday fluctuation irradiation wasinsignificant. was sunny-cloudy 1 April was cloudy on whichinthe fluctuation irradiationThe waspower significant. roughly proportional to theproportional POA irradiation, and thisirradiation, relationshipand wasthis tighter on sunnywas daystighter than on The power was roughly to the POA relationship on cloudy days. Module temperature is related principally to ambient temperature, irradiation, and wind sunny days than on cloudy days. Module temperature is related principally to ambient temperature, speed. The module temperature fluctuated with fluctuating incident POA irradiation, and wind speed. The modulesynchronously temperature fluctuated synchronously withirradiation, fluctuating which wasPOA obviously observed on was cloudy days. observed on cloudy days. incident irradiation, which obviously (a) (b) Figure1.1.Measurements Measurements of meteorological PV output from 28 March to 12015: April (a) Figure of meteorological datadata and and PV output from 28 March to 1 April (a)2015: power power output and plane-of-array (POA) irradiation; (b) ambient and module temperature. output and plane-of-array (POA) irradiation; (b) ambient and module temperature. Theinstantaneous instantaneousPR PRduring duringthe thefive fiveconsecutive consecutivedays daysisispresented presentedininFigure Figure2.2.InInthe theearly early The morning, instantaneous PR increased quickly with increasing POA irradiation. This is explained morning, instantaneous PR increased quickly with increasing POA irradiation. This is explained partly partly by the decreasing solar angle-of-incidence, and by the variation ofspectrum. the solar spectrum. by the decreasing solar angle-of-incidence, and partly bypartly the variation of the solar Oblique, Oblique, that is, high, angles-of-incidence in early mornings result in small effective irradiation due that is, high, angles-of-incidence in early mornings result in small effective irradiation due to increased to increased optical loss. The variation of the solar spectrum is primarily due to the variation in the optical loss. The variation of the solar spectrum is primarily due to the variation in the air mass air mass through which the irradiation travels in mornings, mid-days, and evenings. The air mass through which the irradiation travels in mornings, mid-days, and evenings. The air mass is greatest inis greatest in mornings and evenings when the sun’s radiation atangle, an oblique angle, thus passing mornings and evenings when the sun’s radiation passes at anpasses oblique thus passing through a through a large mass of air, and is lowest at mid-day. Instantaneous PR tended to be steady large mass of air, and is lowest at mid-day. Instantaneous PR tended to be steady at around 88% atat around 88% at highofmid-day levels of irradiation, while dropping slightly prior hours due to high mid-day levels irradiation, while dropping slightly from prior hoursfrom due to higher module higher module temperature. In the late afternoon, instantaneous PR fell in pace with decreasing temperature. In the late afternoon, instantaneous PR fell in pace with decreasing POA irradiation due to POA irradiation due to the increasing angle-of-incidence asspectrum. well as the change near in solar spectrum. the increasing angle-of-incidence as well as the change in solar However, sunset when However, near sunset when the POA irradiation was close to zero, suspiciously high values the POA irradiation was close to zero, suspiciously high values of instantaneous PR, greater than 100%,of instantaneous PR, greater than 100%, from werethe observed. Thisofmay have resultedorfrom themeasurement inaccuracy of were observed. This may have resulted inaccuracy POA irradiation power POA irradiation or power measurement at very low levels of irradiation, since a small error the at very low levels of irradiation, since a small error on the power or irradiation measurement canon lead or irradiation measurement canwas lead to large in PR when the that truePR value very small. topower large error in PR when the true value very small.error Figure 2 also shows waswas more variable Figure 2 also shows that PR was more variable under cloudy conditions where solar irradiation under cloudy conditions where solar irradiation largely fluctuated. This confirms that wide fluctuation in irradiation can considerably reduce the precision of PR [15]. Thus, it is necessary to discard data points with large fluctuation in irradiation to obtain more reliable PV performance metrics. Energies 2016, 9, 342 Energies 2016, 9, 342 6 of 15 6 of 15 largely fluctuated. This confirms that wide fluctuation in irradiation can considerably reduce the largely fluctuated. This confirms that wide fluctuation in irradiation can considerably reduce the precision Energies 2016,of9,PR 342[15]. Thus, it is necessary to discard data points with large fluctuation in irradiation 6 of 15 precision of PR [15]. Thus, it is necessary to discard data points with large fluctuation in irradiation to obtain more reliable PV performance metrics. to obtain more reliable PV performance metrics. Figure 2. Instantaneous PR from 28 March to 1 April 2015. Figure 2. 2. Instantaneous Instantaneous PR PR from from 28 28 March March to Figure to 11 April April 2015. 2015. Figure 3 depicts the variation of PR as a function of POA irradiation. The impact of temperature Figure 3 depicts the variation of PR as a function of POA irradiation. The impact of temperature is removed by converting PR to 25 ˝°C using the maximal power temperature coefficient given in C using the maximal power temperature coefficient given in is removed by converting PR to 25 °C Table 1 with the equation PR25°C = PR/[1 + α(Tmod − 25)] where α is the maximal power temperature ˝ Table = PR/[1 α(Tmod 25)]where whereααisisthe themaximal maximal power power temperature Table 11with withthe theequation equationPR PR 25°C = PR/[1 ++ α(T 25)] mod− ´ 25 C coefficient and Tmod is the module temperature. In Figure 3, data points where the POA irradiation is coefficient and Tmod is the module temperature. In Figure 3, data points where the POA irradiation irradiation is modis the module temperature. In Figure 3, data points where the POA smaller than 50 W/m2 are dropped to eliminate misleading measurements at very low levels of 2 2 areare is smaller than W/m dropped eliminate misleading measurements at very levels smaller than 50 50 W/m dropped to to eliminate misleading measurements at very lowlow levels of irradiation. of irradiation. irradiation. Figure 3. Temperature-corrected PR as a function of POA irradiation Figure 3. Temperature-corrected PR as a function of POA irradiation Figure 3. Temperature-corrected PR as a function of POA irradiation. As can be seen from Figure 3, irradiation intensity-dependent instantaneous PR varied little As can be seen from Figure 3, irradiation intensity-dependent instantaneous PR varied little can be seenPOA from irradiation Figure 3, irradiation intensity-dependent instantaneous varied little when whenAsthe incident was greater than 600 W/m2; at this level of PR POA irradiation the 2; at this level of POA irradiation the when the incident POA irradiation was greater than 600 W/m 2 ; at this level of POA the incidentcaused POA irradiation greater than W/m the influences influences by solarwas spectrum and 600 solar angle-of-incidence wereirradiation small. The effects of influences caused by solar spectrum and solar angle-of-incidence were small. The effects of caused by solar spectrum and solar angle-of-incidence were small. The effects of angle-of-incidence angle-of-incidence and solar spectrum explain why on cloudy days instantaneous PR at low levels of angle-of-incidence and solar spectrum explain why on cloudy days instantaneous PR at low levels of 2 and solar spectrum why on cloudy PR at low levels irradiation, around irradiation, around explain 200 W/m , was greaterdays thaninstantaneous on sunny days. Suppose thatofthe POA irradiation irradiation, around 200 W/m2, was greater than on sunny days. Suppose that the POA irradiation 2 , was 200 greater than on sunny Suppose that the POA irradiation level on levelW/m on cloudy days at noon is close to days. the POA irradiation level in the early morning oncloudy sunny days days. level on cloudy days at noon is close to the POA irradiation level in the early morning on sunny days. at noon is close to POA days irradiation level in the morning on sunny POA POA irradiation onthe cloudy consists mainly of early diffuse irradiation whichdays. reaches theirradiation surface of POA irradiation on cloudy days consists mainly of diffuse irradiation which reaches the surface of on cloudyfrom daysall consists mainly of diffuse irradiation which reachesto the surface modules from all modules directions resulting in less optical loss compared the beam of component of POA modules from all directions resulting in less optical loss compared to the beam component of POA directions less optical to the beam of The POAdifference irradiationinat solar high irradiationresulting at high in oblique angleloss in compared the early morning on component sunny days. irradiation at high oblique angle in the early morning on sunny days. The difference in solar oblique angle in the early morning at onnoon sunny The difference in solar between spectrum between POA irradiation ondays. cloudy days and in the early spectrum morning on sunny POA days spectrum between POA irradiation at noon on cloudy days and in the early morning on sunny days irradiation noon on cloudy days and in the early morning on sunny days complicates the deviation complicatesatthe deviation of PR. complicates the deviation of PR. of PR.Analysis of the relationship between PR and POA irradiation strongly supports that, to obtain a Analysis of the relationship between PR and POA irradiation strongly supports that, to obtain a moreAnalysis reliable PR series, it is necessary to discard at lowstrongly levels ofsupports irradiation to to reduce the of the relationship between PR anddata POApoints irradiation that, obtain a more reliable PR series, it is necessary to discard data points at low levels of irradiation to reduce the impact of solar andto solar spectrum. more reliable PRangle-of-incidence series, it is necessary discard data points at low levels of irradiation to reduce the impact of solar angle-of-incidence and solar spectrum. impact of solar angle-of-incidence and solar spectrum. Energies 2016, 9, 342 7 of 15 Energies 2016, 9, 342 7 of 15 7 of 15 Energies 2016, 9, 342 4.1.2. Daily Performance 4.1.2. Daily Performance Figure 4 Performance presents the daily array yield and daily reference yield from 1 January 2011 to 4.1.2. Daily Figure2015, 4 presents dailytwo array yieldinand daily reference yieldoccurred from 1 January 2011 to 31 31 December whichthe shows peaks each year. The peaks on those dates when Figure 4 presents the daily array yield and daily reference yield from 1 January 2011 to 31 December 2015, which shows two peaks in each year. The peaks occurred on those dates when the the sun was positioned so that the PV module received the most energy radiation. The observed December 2015, which twoPV peaks in each year. The on those dates the sun was positioned soshows that the module received thepeaks mostoccurred energy radiation. The when observed maximal daily array yield 7.564the h (corresponding reference yield 8.581 h)radiation. occurred The on 14observed April 2013, sun was positioned so that PV module received the most energy maximal daily array yield 7.564 h (corresponding reference yield 8.581 h) occurred on 14 April 2013, while the observed maximal dailyhreference yield 9.207 h (corresponding array yield 6.808 h) occurred maximal array yield 7.564 (corresponding reference 8.581 h) occurred onyield 14 April 2013, while thedaily observed maximal daily reference yield 9.207 yield h (corresponding array 6.808 h) on 25 March 2013. Themaximal observeddaily POAreference irradiation and PV hpower output on array 25 March 2013 and while the observed yield 9.207 (corresponding yield 6.808 h) on occurred on 25 March 2013. The observed POA irradiation and PV power output on 25 March 2013 14 April 2013 shown in Figure 5 in explain why the irradiation day with maximal array yieldarray did coincide with occurred 25 March The observed power output on not 25 March and on 14onApril 2013 2013. shown Figure 5 POA explain why theand dayPV with maximal yield did2013 not the and observed maximal daily reference yield. It is clearly illustrated that in the morning from 7 on 14 April shown in Figure explain why the day with illustrated maximal array did notto 9 coincide with the 2013 observed maximal daily5 reference yield. It is clearly that inyield the morning coincide with the observed maximal daily reference yield. Itfully is clearly illustrated that in the morning o’clock March 2013, the PV2013, system didn’t fully respond to the incident irradiation, perhaps fromon 7 to25 9 o’clock on 25 March the PV system didn’t respond to the incident irradiation, from 7 to 9 o’clock on 25 March 2013, the PV system didn’t fully respond to the incident irradiation, because of shading, resulting in less power generated than expected. perhaps because of shading, resulting in less power generated than expected. perhaps because of shading, resulting in less power generated than expected. (a) (a) (b) (b) Figure 4. Daily yields from 1 January 2011 to 31 December 2015: (a) array yield; (b) reference yield. Figure 4. Daily yields December2015: 2015:(a)(a)array array yield; reference yield. Figure 4. Daily yieldsfrom from1 1January January2011 2011 to to 31 31 December yield; (b)(b) reference yield. (a) (b) (a) (b) Figure 5. Observed power and POA irradiation: (a) 25 March 2013; (b) 14 April 2013. Figure 5. Observed power and POA irradiation: (a) 25 March 2013; (b) 14 April 2013. Figure 5. Observed power and POA irradiation: (a) 25 March 2013; (b) 14 April 2013. Energies 2016, 9, 342 Energies 2016, 9, 342 8 of 15 8 of 15 Figure 66 depicts the daily dailyPR PRduring duringthe the period of observation. values varied Figure2016, depicts period of observation. MostMost values varied between Energies 9, 342 the 8 between of 1584% 84% Not much difference was observed fromtoday oryear fromtoyear year except for and and 92%.92%. Not much difference was observed from day daytoorday from yeartoexcept for some Figure 6 depicts the daily PR during the period of observation. Most values varied between 84% some irregular points. Those with points with lowresulted values from resulted from snowing, of the irregular points. Those points very lowvery values snowing, shading ofshading the PV array, anddown-time, 92%. Not down-time, much difference was observed from day to day from data year Shading to year except for someof the PV array, system component errors in or power collection. Shading system component failure, or failure, errors inorpower data collection. of the irradiation irregular points. Those withcollection verydata low values resulted from in snowing, shading thedaily PVof array, sensor or errors inorirradiation data resulted inresulted extremely high values ofofthe PR. The irradiation sensor errorspoints in irradiation collection extremely high values the daily system down-time, component failure, or errors in power data collection. Shading of the irradiation daily PRdaily can be to used identify operational problems. PR. The PRused can be to identify operational problems. sensor or errors in irradiation data collection resulted in extremely high values of the daily PR. The daily PR can be used to identify operational problems. Figure 6. 6. Daily Daily PR from 1 January 2011 to Figure to 31 31 December December 2015. 2015. Figure 6. Daily PR from 1 January 2011 to 31 December 2015. There aresome some gaps Figures 4a,b and 6. is due due tosystem system data unavailability from 19 There are gaps ininFigures 4a,b and 6. This is due to to system data unavailability from 19 There are some gaps in Figures 4a,b and 6. This This is data unavailability from 19 May to 82011, June 2011, and totoirradiation measurement error from June 23 July 2013. toMay 8 June and due todue irradiation measurement errorerror fromfrom 26 June to 23 July 2013. May to 8 June 2011, and due irradiation measurement 2626June to to 23 July 2013. 4.1.3.4.1.3. Monthly Performance Monthly Performance 4.1.3. Monthly Performance Figure 7 presents monthlyarray arrayyield, yield, reference reference yield, PR during thethe observed period; Figure presents thethe monthly array yield, and PR during observed period; Figure 77presents the monthly reference yield,and and PR during the observed period; it shows array yield and referenceyield yieldin in summer summer were than in in winter. TheThe observed shows thatthat array yield and reference yield weregreater greater than winter. observed ititshows that array yield and reference greater than in winter. The observed maximal monthly array yield andreference referenceyield yield were were 169.7 203.2 h respectively, both in April maximal monthly array yield and 169.7 and 203.2 hhrespectively, both in maximal monthly array yield and 169.7hhhand and 203.2 respectively, both inApril April 2012. Reference yield 90.6 h and array yield 76.9 h in May 2011 were abnormal compared to yields in 2012.Reference Referenceyield yield90.6 90.6h hand andarray arrayyield yield 76.9 May 2011 were abnormal compared yields in 2012. 76.9 hh inin May 2011 were abnormal compared to to yields in the the adjacent months as well as the yields in May of other years. This was a result of system data the adjacent months asaswell as theinyields inother Mayyears. of other years. This was a result of unavailability system data adjacent months as well the yields May of This was a result of system data unavailability from 19 May to 8 June 2011 as shown in Figures 4a,b and 6. From 26 June to 23 July unavailability 19 May to 8 June 2011 as4a,b shown Figures and 6.July From 26due June to 23 July from 19 May to to 8from June 2011 asmeasurement shown in Figures andinin 6.Figures From 264b4a,b June 2013, irradiation 2013, due irradiation error shown andto 6, 23 array yields weretogreater 2013,than duereference to error irradiation error in Figures 4b andthan 6, array yields were greater measurement shownmeasurement in Figures 4b and 6, shown array yields were greater reference yields. yields. than reference yields. Figure 7. Monthly reference yield, array yield, and PR from 1 January 2011 to 31 December 2015. Figure 7. Monthly reference yield, array yield, and PR from 1 January 2011 to 31 December 2015. Figure 7.7,Monthly reference array the yield, andin PRJuly from2013 1 January 2011extremely to 31 December In Figure the monthly PRyield, excludes data to avoid high2015. values due to measurement errors of irradiation. The monthly PR varied from 76.4% to 91.6%, and no obvious seasonal variation was observed. Energies 2016, 342 Energies 2016, 9, 9, 342 9 of915 of 15 In Figure 7, the monthly PR excludes the data in July 2013 to avoid extremely high values due to measurement errors of irradiation. The monthly PR varied from 76.4% 91.6%, and seasonal Investigation of daily and monthly PV performance shows thattooutliers dueno to obvious snowing, shading, variation was observed. system down-time, data availability, and measurement errors complicate performance metrics, making Investigation ofthe daily and monthly PVarray performance shows that outliers due to snowing, PR inefficiently reflect response of the PV to irradiation. shading, system down-time, data itavailability, andclear measurement errors complicate performance From analysis in this section, will become that performance data points in the early metrics,and making PR inefficiently reflect response is oflow, the PV irradiation. morning late evening when the POAthe irradiation andarray datato points on cloudy days with large Frominanalysis in thisas section, it will become clear are thatfar performance data points the early morningon fluctuation irradiation, well as certain outliers, more unreliable thaninthose at mid-day and late evening when the POA irradiation is low, and data points on cloudy days with large fluctuation sunny days and need to be filtered out in order to obtain more reliable long-term performance metrics. in irradiation, as well as certain outliers, are far more unreliable than those at mid-day on sunny days andPerformance need to be filtered in order to obtain more reliable long-term performance metrics. 4.1.4. afterout Data Filtering To exclude influences fromFiltering the aforementioned factors, the following steps were adopted to select 4.1.4. Performance after Data appropriate data points: To exclude influences from the aforementioned factors, the following steps were adopted to select appropriate points: ‚ Discarding datadata points with POA irradiation less than 600 W/m2 to reduce the impact of solar and solar • angle-of-incidence Discarding data points withspectrum. POA irradiation less than 600 W/m2 to reduce the impact of solar ‚ Eliminating data points change of POA irradiation is more than 20 W/m2 /min to reduce angle-of-incidence andwhen solar the spectrum. • theEliminating data fluctuations points wheninthe change ofonPOA irradiationofisperformance more than 20 W/m2/min to impact of large irradiation the precision metrics. reduce the impactbased of large in irradiation on theexceeding precision of performance metrics. ‚ Removing outliers onfluctuations instantaneous PR. Data points the lower or upper limit of • PR, Removing outliers based on instantaneous PR. Data points exceeding the lower or upper limit and data points with large deviation from the mean PR over a given period are considered of PR, and data points with large deviation from the mean PR over a given period are outliers. In this study, the lower limit and upper limit of instantaneous PR are 75% and 100%, considered outliers. In this study, the lower limit and upper limit of instantaneous PR are 75% respectively, and the limit of deviation is ˘5%. and 100%, respectively, and the limit of deviation is ±5%. Figure 8 depicts and the themonthly monthlyaverage averageambient ambient temperature Figure 8 depictsthe themonthly monthlyPR PRafter afterdata data filtering filtering and temperature of of selected seasonalityand andranged rangedfrom from84% 84%toto93% 93% over selecteddata datapoints. points. PR PR showed showed a a strong strong seasonality over thethe observation period. This seasonality resulted from the seasonal variation of ambient temperature, observation period. This seasonality resulted from the seasonal variation of ambient temperature, wind speed, and remaining andsolar solarspectrum. spectrum. wind speed, and remainingimpacts impactsof ofsolar solarangle-of angle-of incidence incidence and Figure8.8.Monthly MonthlyPR PR and and ambient ambient temperature Figure temperatureafter afterdata datafiltering. filtering. Annual DegradationRate Rate 4.2.4.2. Annual Degradation The three components classical decomposition, the the trend, the seasonality, and the The three components afterafter classical decomposition, the trend, seasonality, and the irregularity irregularity are shown in Figure 9 accompanied by the original monthly PR. The trend shown in are shown in Figure 9 accompanied by the original monthly PR. The trend shown in Figure 9b is Figure 9b is obtained by applying a two-step centered twelve-month moving average expressed in obtained by applying a two-step centered twelve-month moving average expressed in Equation (7) Equation (7) to the original monthly PR in Figure 9a. A linear regression is applied to the trend to to the original monthly PR in Figure 9a. A linear regression is applied to the trend to obtain the obtain the degradation rate. The fitted line using least square algorithm and its coefficient of 2 degradation rate. The fitted line using least square algorithm and its coefficient of determination R determination R2 are also shown in Figure 9b. The coefficient of determination is a regression arestatistic also shown in Figure coefficient determination a coefficient regressionof statistic which measures which measures9b. theThe goodness of fit.ofHigher values of is the determination imply thebetter goodness of fit. Higher values of the coefficient of determination imply better agreement between agreement between the fitted line and the observed data points [11]. The steps to obtain the fitted line and the observed data points [11]. The steps to obtain seasonal component and the remaining irregular component are well explained in Section 3.2. The estimated annual degradation rate was 0.44% using Equation (9). Energies 2016, 9, 342 10 of 15 seasonal component and the remaining irregular component are well explained in Section 3.2. Energies 2016, 9, 342 10 ofThe 15 10 of 15 estimated annual degradation rate was 0.44% using Equation (9). seasonal component and the remaining irregular component are well explained in Section 3.2. The estimated annual degradation rate was 0.44% using Equation (9). Energies 2016, 9, 342 (a) (a) (b) (b) (c) (d) (c) (d) Figure 9. Classical times series decomposition of the monthly PR: (a) original signal; (b) component Figure 9. Classical times series decomposition of the monthly PR: (a) original signal; (b) component of ofFigure trend and its linear regression line; (c) component of seasonality; (d)original component of(b) irregularity. 9. Classical times series decomposition of the monthly PR:(d) (a) signal; component trend and its linear regression line; (c) component of seasonality; component of irregularity. of trend and its linear regression line; (c) component of seasonality; (d) component of irregularity. Performanceofofthe theMono-Si Mono-SiPV PVSystem System 4.3.4.3. Performance 4.3. Performance of the Mono-Si PV System The monthly PR of the mono-Si PV system installed at the same place and studied during the The monthly PR of the mono-Si PV system installed at the same place and studied during the The monthly of the mono-Si installed at the same process place and studied during the same period of timePR is shown in FigurePV 10 system after a similar data filtering applied to the UMG-Si same period of time is shown in Figure 10 after a similar data filtering process applied to the UMG-Si same period of time decomposition is shown in Figure 10 after a similar datathe filtering applieddegradation to the UMG-Si PV system. Classical is used again to obtain annualprocess performance rate PV system. Classical decomposition isisused again to obtainthe theannual annualperformance performance degradation rate PV system. Classical decomposition used again to obtain degradation rate of the mono-Si PV system. The trend component and its linear regression are shown in Figure 11. The of the mono-Si PV system. The trend component and its linear regression are shown in Figure of the mono-Si PV system. The and its linear regression are shown in Figure 11. The 11. derived annual degradation ratetrend was component 0.71%. The derived derivedannual annualdegradation degradation rate 0.71%. rate waswas 0.71%. Figure 10. Monthly PR and ambient temperature for mono-Si PV. Figure 10. Monthly PR and ambient temperature for mono-Si PV. Figure 10. Monthly PR and ambient temperature for mono-Si PV. These over aa year’s year’s time, time,with withlarger largerPR PRinincooler cooler Thesetwo twoPV PVsystems systemsshow showaasimilar similar pattern pattern of of PR PR over These two PV systems show a similar pattern of PR over a year’s time, with larger PR in cooler winter PV system system was wasinstalled installedalmost almostthree three wintermonths monthsthan thanininhotter hottersummer summer months. months. The The mono-Si mono-Si PV years earlier UMG-Si PV At beginning ofPV thesystem common observation periodthe the winter months than in summer months. The mono-Si was installedperiod almost three years earlierthan thanthe thehotter UMG-Si PV system. system. At the the beginning of the common observation values of PR for two systems were almost same (Figure 8 vs. Figure 10) and the degradation years earlier than the UMG-Si PV system. At the beginning of the common observation period values of PR for the two systems were almost the same (Figure 8 vs. Figure 10) and the degradation the rate PV higher than that of the UMG-Si PVFigure system; hence, itisisdegradation expected values ofofthe PR for the two systems were almost the same (Figure 8 vs. 10) and it the rateof themono-Si mono-Si PVsystem systemwas was higher than that of the UMG-Si PV system; hence, expected rate of the mono-Si PV system was higher than that of the UMG-Si PV system; hence, it is expected that the PR of the mono-Si PV system at the beginning of its life would have been higher than that of the UMG-Si PV system. Energies 2016, 9, 342 11 of 15 Energies 11 of 15 that the2016, PR 9,of342 the mono-Si PV system at the beginning of its life would have been higher than that of Energies 2016, 9, 342 11 of 15 the UMG-Si PV system. that the PR of the mono-Si PV system at the beginning of its life would have been higher than that of the UMG-Si PV system. Figure11. 11.Trend Trendcomponent component and and its its linear linear regression Figure regressionfor formono-Si mono-SiPV. PV. Figure 11. Trend and its linear regression for mono-Si PV. of the UMG-Si PV The derived degradation ratecomponent of the mono-Si is higher than that The derived degradation rate of the mono-SiPV PVsystem system is higher than that of the UMG-Si system showing that PV modules made of UMG-Si have the potential to retain high quality in PV system showing that PV modules made of UMG-Si have the potential to retain quality The derived degradation rate of the mono-Si PV system is higher than that of thehigh UMG-Si PV in long-term performance. But it is difficult to conclude that PV modules made from UMG-Si have long-term performance. But it is difficult to conclude that PV modules made from UMG-Si system showing that PV modules made of UMG-Si have the potential to retain high quality have in smaller degradation rates than modules made from mono-Si since other factors, including effects of smaller degradation rates than made from mono-Si since other made factors, including effects long-term performance. But itmodules is difficult to conclude that PV modules from UMG-Si have of encapsulant and back sheet of PV modules, influences of dirtiness on the surface, and impacts of encapsulant and back sheet PVmodules modules, influences of dirtiness the surface, and impacts of other smaller degradation rates of than made from mono-Si sinceon other factors, including effects of other components like inverter of PV systems, are not taken into account. encapsulant and back sheet of PV modules, influences of dirtiness on the surface, and impacts of components like inverter of PV systems, are not taken into account. other components likePerformance inverter of with PV systems, not taken into account. 4.4. Comparison of PV Different are Data Filtering Criteria 4.4. Comparison of PV Performance with Different Data Filtering Criteria We have shown PR as a function of POAData irradiation Figure 3, and we filtered out the data 4.4. Comparison of PV Performance with Different FilteringinCriteria We have PR as a function of POA irradiation in Figure 3, andrate we estimation. filtered outIn thethis data 2 for PV points withshown POA irradiation less than 600 W/m degradation 2 We have shown PR as a function of POA irradiation in Figure 3, and we filtered out the data points with POA lessthreshold than 600 of W/m PV degradation estimation. In this subsection, 2 to 800 subsection, weirradiation will vary the POAfor irradiation for datarate filtering from 200 W/m 2 for PV degradation rate 2 to 2 inthis with POA irradiation less than 600for W/m estimation. In wepoints will vary the threshold of POA irradiation data filtering from 200 W/m 800 W/m steps 2 2 W/m in steps of 200 W/m to observe the impact on monthly PR and degradation rate. 2 2 subsection, weobserve will vary the threshold of POAPR irradiation for data filtering from 200 W/m to 800 of 200 W/m to the impact on monthly and degradation rate. Figure 12 depicts monthly PR for both PV systems with various data filtering thresholds of POA 2 in steps of 200 W/m2 to observe the impact on monthly PR and degradation rate. W/m Figure 12The depicts monthly PR forhigher both PV systems with various remained data filtering thresholds POA irradiation. pattern of PR being in winter than in summer almost the sameof with FigureThe 12 depicts monthly PR forhigher both PV systems with various data filtering thresholds of POA irradiation. pattern of PR being winter thanincreasing in summer remained same various thresholds for the whole observationin period. With thresholds, PR almost in eachthe month irradiation. The pattern of PR being higher in winter than in summer remained almost the same with with various However, thresholds themonths, whole observation period. With increasing thresholds, PR in each decreased. infor some especially May 2015, PR was unusual with suspiciously high various thresholds for the whole observation period. With increasing thresholds, PR in each month 2 2 values with lower thresholds (200 W/m andespecially 400 W/mMay ) of 2015, POA PR irradiation for data filtering. The month decreased. However, in some months, was unusual with suspiciously decreased. However, in some months, especially May 2015, PR unusual with suspiciously high 2 and 2 ) was unusually high values of PR with lower thresholds were explained by theirradiation fact that in for Maydata 2015filtering. there high values with lower thresholds (200 W/m 400 W/m of POA values with lower thresholds (200 W/m2 and 400 W/m2) of POA irradiation for data filtering. The more cloudy than in adjacentwere months and in May of other years. Thewere unusually high (rainy valuesorofovercast) PR withdays lower thresholds explained by the fact that in This Mayfact 2015 unusually high values of PR with lower thresholds were explained by the fact that in May 2015 there was demonstrated by the observed monthly reference yield in May 2015 which was less than in Aprilyears. or there were more cloudy (rainy or overcast) days than in adjacent months and in May of other were more cloudy (rainy or overcast) days than in adjacent months and in May of other years. This fact June 2015. is shown in Figure 3 that on cloudy days instantaneous PR was greater than on sunny days This fact wasItdemonstrated by the observed monthly reference yield May 2015less which less than was demonstrated by the observed monthly reference yield in May 2015inwhich was thanwas in April or at low levels of POA irradiation, leading to the unusually high values of monthly PR with lower data in April or June 2015. ItinisFigure shown in Figure 3 that oninstantaneous cloudy days PR instantaneous PR was greater than June 2015. It is shown 3 that on cloudy days was greater than on sunny days filtering thresholds POA of irradiation. The unusual PR intoMay 2015 was reduced for theofUMG-Si PV onat sunny days at POA lowoflevels POA irradiation, leading thevalues unusually high values monthly low levels of irradiation, leading to the unusually high of monthly PR with lower dataPR system and eliminated for the mono-Si PV system by increasing the threshold. with lowerthresholds data filtering thresholds of POA The unusual PR reduced in May 2015 was reduced filtering of POA irradiation. The irradiation. unusual PR in May 2015 was for the UMG-Si PVfor thesystem UMG-Si system and eliminated forsystem the mono-Si PV system by increasing the threshold. andPV eliminated for the mono-Si PV by increasing the threshold. (a) (a) Figure 12. Cont. Energies 2016, 9, 342 Energies 2016, 9, 342 Energies 2016, 9, 342 1215 of 15 12 of 12 of 15 (b) (b) Figure 12. Monthly PR for various data filtering thresholds of POA irradiation: (a) UMG-Si PV; Figure Monthly PR PR for for various various data data filtering (a) UMG-Si UMG-Si PV; PV; Figure 12. 12. Monthly filtering thresholds thresholds of of POA POA irradiation: irradiation: (a) (b) mono-Si PV. (b) (b) mono-Si mono-Si PV. PV. Figure 13 presents the estimated annual degradation rates with different data filtering thresholds Figure 13 presents the estimated annual degradation rates with different data filtering thresholds of POA irradiation for two PV systems using the classical decomposition method. Degradation rate of increased POA for systems using the classical decomposition method. Degradation rate POA irradiation irradiation for two two PV PV systemsfrom using theW/m classical decomposition method. 2 to 600 with increasing thresholds 200 W/m2, and tended to be steady above 22 to 600 W/m22 , and tended to increased with increasing thresholds from 200 W/m with increasing thresholds from 200 W/m 600 W/m , and tended to be steady above 2 600 W/m which was obvious for the mono-Si PV system while the tendency was weaker for above the 2 which was obvious for the mono-Si PV system while the tendency was weaker for the 2 600UMG-Si W/m W/m which was based obvious for the we mono-Si PVvalues systemwith data filtering threshold of 600 W/m2the PV system; on which, select the 2 as2 UMG-Si PV based on the values with data filtering threshold of 600 W/m UMG-Si PVsystem; system; based onwhich, which,we weselect select the values with data filtering threshold of 600 W/m as the final estimated annual degradation rates for the two PV systems. the final estimated annual degradation rates for for thethe twotwo PV PV systems. as the final estimated annual degradation rates systems. Figure 13. Degradation rate with different thresholds for data filtering 5. Performance Comparison Figure 13. 13. Degradation Degradation rate rate with with different different thresholds thresholdsfor fordata datafiltering. filtering Figure To better characterize the long-term field performance of the investigated UMG-Si PV system 5. Performance Comparison 5. Performance Comparison and the mono-Si PV system at the same place, we compare PR and Rd with conventional crystalline silicon-based PV systems shown in Table field 2. To better better characterize characterize the long-term long-term field performance performance of of the the investigated investigated UMG-Si UMG-Si PV PV system system To the and the the mono-Si mono-Si PV PV system system at at the the same same place, place, we we compare compare PR PR and and RRdd with with conventional conventional crystalline crystalline and silicon-based PV systems shown in Table 2. silicon-based PV systems shown in Table 2. Energies 2016, 9, 342 13 of 15 Table 2. Performance comparison with conventional crystalline silicon modules. Technology PR (%) Rd (%) Reference poly-Si 90–110 0.24–0.46 [15] mono-Si 90–110 0.64–0.92 [15] poly-Si mono-Si poly-Si mono-Si poly-Si 75–98 65–98 92.9 60–62 76–95 0.78–1.30 0.77–1.37 [14] [14] [22] [23] [24] mono-Si 0.25 [25] poly-Si 0.22 [26] mono-Si 0.71 UMG-Si 84-93 0.44 Present study Present study Comments Data filtering: G > 750 W/m2 , stability, and outliers Analytical: linear regression on monthly PR (3 years) Data filtering: G > 750 W/m2 , stability and outliers Analytical: linear regression on monthly PR (3 years) Analytical: classical decomposition on monthly PR (5 years) Analytical: classical decomposition on monthly PR (5 years) Annual average daily performance ratio of 1 year Annual average monthly performance ratio of 3 years Monthly performance ratio of 1 year Analytical: linear regression on monthly maximal power using PVUSA (8 years) Average decay of maximal power (I-V curves) of 70 modules over 20 years Data filtering: G > 600 W/m2 , stability, and outlier Analytical: classical decomposition on monthly PR (5 years) Data filtering: G > 600 W/m2 , stability, and outlier Analytical: classical decomposition on monthly PR (5 years) It is indicated by Table 2 that the performance of the UMG-Si modules and the performance of the mono-Si PV system fall in the performance range of crystalline silicon modules, though the performance varies from one system to another within a broad range. The wide variation of performance could result from factors such as difference in design of modules, local climate, and operating conditions of each system. Therefore, the performance of the UMG-Si PV system is acceptable compared to conventional crystalline silicon modules. 6. Conclusions In this study, long-term performance of a 1.26 kW UMG-Si PV system installed at NREL is investigated by analyzing continuously monitored data from 1 January 2011 to 31 December 2015. To make the investigation more meaningful, the performance of a mono-Si PV system installed at the same place and studied during the same period of time is presented for reference. Analysis of the instantaneous, daily, and monthly performance shows that: ‚ ‚ ‚ PR tends to be high and steady at high levels of POA irradiation where the impacts of solar angle-of-incidence and solar spectrum are weak. PR is more variable and unreliable under cloudy conditions where irradiation largely fluctuates. Outliers due to snowing, shading, system down-time, data availability, measurement errors, etc. make PR inefficiently reflect the response of the PV array to irradiation. Hence, it is necessary to discard data points at low levels of irradiation and data points with large fluctuations in irradiation, as well as outliers, to obtain a more reliable PR. After data filtering, the monthly PR ranged from 84% to 93% over the observation period and showed strong seasonality. The impact of POA irradiation threshold for data filtering on PR and estimated degradation rate is also studied, showing that the estimated value of degradation rate increases with increasing thresholds from 200 W/m2 to 600 W/m2 and tends to be steady above 600 W/m2 . Long-term performance annual degradation rate, Rd , was 0.44% for the UMG-Si PV system and 0.71% for the mono-Si PV system with a data filtering threshold of 600 W/m2 . A comparison of PR and Rd to conventional crystalline silicon-based PV systems shows that the performance of the UMG-Si PV system is acceptable. These results are of value for further performance analysis of the UMG-Si PV system, such as short-term and long-term power output prediction. More importantly, this long-term performance evaluation provides a preliminary understanding of UMG-Si PV outdoor performance, and offers Energies 2016, 9, 342 14 of 15 reference information for better and wider use of UMG-Si PV modules. Furthermore, the rationalization for the importance of data filtering will benefit analysis of long-term performance of other PV systems. Acknowledgments: The first author would like to thank City University of Hong Kong for financial support on his Ph.D. research. Author Contributions: Chao Huang developed the main part of this work. Michael Edesess contributed in writing the parts and editing the document. Alain Bensoussan and Kwok L. Tsui critically reviewed the paper and contributed in finalizing the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: AM AOI ARIMA NREL POA PR PV UMG-Si Air Mass Angle of Incidence Autoregressive Integrated Moving Average National Renewable Energy Laboratory Plane of Array Performance Ratio Photovoltaic Upgraded Metallurgical Grade Silicon References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. Gan, P.Y.; Li, Z.D. Quantitative study on long term global solar photovoltaic market. Renew. Sustain. Energy Rev. 2015, 46, 88–99. [CrossRef] del Coso, G.; del Canizo, C.; Sinke, W.C. The impact of silicon feedstock on the PV module cost. Solar Energy Mater. Solar Cells 2010, 94, 345–349. [CrossRef] Jeong, K.P.; Kim, Y.K. Electrical properties of the multi-crystalline silicon ingots grown with UMG (upgraded metallurgical grade) silicon materials. Solar Energy Mater. Solar Cells 2012, 107, 201–204. [CrossRef] Rougieux, F.; Samundsett, C.; Fong, K.C.; Fell, A.; Zheng, P.; Macdonald, D.; Degoulange, J.; Einhaus, R.; Forster, M. High efficiency UMG silicon solar cells: Impact of compensation on cell parameters. Prog. Photovolt. Res. Appl. 2016, 24, 725–734. [CrossRef] Modanese, C.; Di Sabatino, M.; Søiland, A.-K.; Peter, K.; Arnberg, L. Investigation of bulk and solar cell properties of ingots cast from compensated solar grade silicon. Prog. Photovolt. Res. Appl. 2011, 19, 45–53. [CrossRef] Odden, J.O.; Lommasson, T.C.; Tayyib, M.; Vedde, J.; Buseth, T.; Friestad, K.; Date, H.; Tronstad, R. Results on performance and ageing of solar modules based on Elkem Solar Silicon (ESS (TM)) from installations at various locations. Solar Energy Mater. Solar Cells 2014, 130, 673–678. [CrossRef] Zheng, P.; Rougieux, F.E.; Samundsett, C.; Yang, X.; Wan, Y.; Degoulange, J.; Einhaus, R.; Rivat, P.; Macdonald, D. Upgraded metallurgical-grade silicon solar cells with efficiency above 20%. Appl. Phys. Lett. 2016, 108, 122103. [CrossRef] Forster, M.; Wagner, P.; Degoulange, J.; Einhaus, R.; Galbiati, G.; Rougieux, F.E.; Cuevas, A.; Fourmond, E. Impact of compensation on the boron and oxygen-related degradation of upgraded metallurgical-grade silicon solar cells. Solar Energy Mater Solar Cells 2014, 120, 390–395. [CrossRef] NREL. Available online: http://maps.nrel.gov/pvdaq (accessed on 15 January 2016). Yang, H.; Wang, H.; Wang, H.; Ding, J. Experimental verification of upgraded metallurgical silicon photovoltaic power plant. Clean Technol. Environ. Policy 2014, 17, 281–285. [CrossRef] Nofuentes, G.; Garcia-Domingo, B.; Munoz, J.V.; Chenlo, F. Analysis of the dependence of the spectral factor of some PV technologies on the solar spectrum distribution. Appl. Energy 2014, 113, 302–309. [CrossRef] King, D.L.; Kratochvil, J.A.; Boyson, W.E. Measuring solar spectral and angle-of-incidence effects on photovoltaic modules and solar irradiance sensors. In Proceedings of the 1997 Twenty-Sixth IEEE Conference on Photovoltaic Specialists, Anaheim, CA, USA, 29 September–3 October 1997; pp. 1113–1116. Energies 2016, 9, 342 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 15 of 15 Jordan, D.C.; Kurtz, S.R. Photovoltaic degradation rates-an analytical review. Prog. Photovolt. Res. Appl. 2013, 21, 12–29. [CrossRef] Makrides, G.; Zinsser, B.; Schubert, M.; Georghiou, G.E. Performance loss rate of twelve photovoltaic technologies under field conditions using statistical techniques. Solar Energy 2014, 103, 28–42. [CrossRef] Ishii, T.; Takashima, T.; Otani, K. Long-term performance degradation of various kinds of photovoltaic modules under moderate climatic conditions. Prog. Photovolt. Res. Appl. 2011, 19, 170–179. [CrossRef] Jordan, D.C.; Kurtz, S.R. The Dark Horse of Evaluaitng Long-Term Field Performance—Data Filtering. IEEE J. Photovolt. 2014, 4, 317–323. [CrossRef] Phinikarides, A.; Kindyni, N.; Makrides, G.; Georghiou, G.E. Review of photovoltaic degradation rate methodologies. Renew. Sustain. Energy Rev. 2014, 40, 143–152. [CrossRef] Jordan, D.C.; Kurtz, S.R. Analytical improvements in PV degradation rate determination. In Proceedings of the 35th IEEE Photovoltaic Specialists Conference, Honolulu, HI, USA, 20–25 June 2010; pp. 2688–2693. International Eelectrotechnical Commission (IEC). IEC Standard 61724: Photovoltaic System Performance Monitoring-Guidelines for Measurement, Data Exchange, and Analysis; IEC: Geneva, Switzerland, 1998. Damrongkulkamjorn, P.; Churueang, P. Monthly energy forecasting using decomposition method with application of seasonal ARIMA. In Proceedings of the 2005 International Power Engineering Conference, Singapore, Singapore, 29 November 2015–2 December 2005; pp. 224–229. Theodosiou, M. Forecasting monthly and quarterly time series using STL decomposition. Int. J. Forecast. 2011, 27, 1178–1195. [CrossRef] Canete, C.; Carretero, J.; Sidrach-de-Cardona, M. Energy performance of different photovoltaic module technologies under outdoor conditions. Energy 2014, 65, 295–302. [CrossRef] Mondol, J.D.; Yohanis, Y.; Smyth, M.; Norton, B. Long term performance analysis of a grid connected photovoltaic system in Northern Ireland. Energy Convers. Manag. 2006, 47, 2925–2947. [CrossRef] Sharma, V.; Kumar, A.; Sastry, O.S.; Chandel, S.S. Performance assessment of different solar photovoltaic technologies under similar outdoor conditions. Energy 2013, 58, 511–518. [CrossRef] Osterwald, C.R.; Adelstein, J.; del Cueto, J.A.; Kroposki, B.; Trudell, D.; Moriarty, T. Comparison of degradation rates of individual modules held at maximum power. In Proceedings of the 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion, Waikoloa, HI, USA, 7–12 May 2006; pp. 2085–2088. Polverini, D.; Field, M.; Dunlop, E.; Zaaiman, W. Polycrystalline silicon PV modules performance and degradation over 20 years. Prog. Photovolt. Res. Appl. 2013, 21, 1004–1015. [CrossRef] © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).