Photovoltaic system monitoring for high latitude locations

Abstract

Reliable monitoring of PV systems is essential to establish efficient maintenance routines that minimize the levelized cost of electricity. The existing solutions for affordable monitoring of commercial PV systems are however inadequate for climates where snow and highly varying weather result in unstable performance metrics. The aim of this work is to decrease this instability to enable more reliable monitoring solutions for PV systems installed in these climates.Different performance metrics have been tested on Norwegian installations with a total installed capacity of 3.3 MW: i) comparison of specific yield, ii) temperature corrected performance ratio, and iii) power performance index based on both physical modelling and machine learning. The most influential effects leading to instability are identified as snow, low light, curtailment, and systematic irradiance differences over the system. The standard deviation of all the performance metrics is reduced when filters targeting these four effects are applied. Compared to general low irradiance or clear sky filtering, a greater reduction in the variation of the metrics is achieved, and more data remains in the useful dataset. The most suitable performance metrics are comparison of specific yield and performance index based on machine learning modelling.The analysis highlights two paths to accomplish increased reliability of PV monitoring systems without increased hardware costs. First, better reliability can be achieved by selecting a suitable performance metric. Second, the variability of the performance metric can be reduced by utilizing filters that specifically target the origin of the variability instead of using standard literature thresholds.

Modeling and Experimental Studies of the Photovoltaic System Performance in Climate Conditions of Poland

“…The highest irradiation during the analyzed period (2022) was in June (190 kWh/m 2 ), whereas the lowest value was in December (barely 11 kWh/m 2 ). Such a significant disparity in the number of sunshine hours over the year is characteristic and was observed in previous studies [29,43] and similar research conducted for high-latitude locations [44][45][46].…”

Vertical bifacial PV systems: irradiance modeling and performance analysis of a lightweight system for flat roofs

“…There are also other explanatory factors for the low PR values calculated in the winter months for all pilots. In the winter months, all PV systems in Norway experience losses because of the general low irradiance, also documented in Figure 7, where both the module and inverter efficiency are lower than at more optimal irradiance conditions [19]. Pilot 3, which is situated above the polar circle, has close to zero irradiance in the months December-February.…”

Hybrid PV Systems and Colocalization of Charging and Filling Stations for Electrification of Road Transport Sector

“…[18] The model is, however, not very well tested for ground-mounted PV systems, and it is possible that not all influential effects are included in the model, but we believe that the model is the best available tool for giving an indication of the snow losses for our location. To estimate monthly snow losses, the implementation of the Marion et al model described by Øgaard et al [19] was used. Using irradiance and temperature from PVGIS [20] and snow data from senorge.no, [21] the monthly snow losses for 11 years (2005-2016) were modeled for PV systems with different tilts for south facing modules.…”