Segmentation and quantification of solar PV losses using data-driven algorithms to help better design and operational monitoring
preprintposted on 21.07.2021, 07:04 by Vijay Bhaskar Chiluveru
In the current scenario of increasing demand for solar Photo-voltaic (PV) systems, the need to predict their feasibility and monitor performance is more than ever. Although PV systems are known for their reliability, they are not above the damaging effects of their surroundings. Various lossy phenomena affect overall plant performance. In this paper, several of such losses, namely thermal, soiling, module degradation and inverter clipping, are discussed. Algorithms to evaluate these losses are proposed which are data-driven and empirical in nature. This is done as an effort to leverage the analytical capabilities provided by the plant data. The paper also compares the estimated losses with those obtained using the PVsyst simulation. As the latter is an independent industrial standard, it helps in understanding the ground reality of PV performance and insights for better operational monitoring. These insights are of immense business value and are aimed at optimizing performance and thereby revenue. As part of our asset management, all the solar PV plant components have sensors whose measurements are sent to the servers on a real-time basis. This is incorporated into our analytics portal which is used for operations and monitoring. The data used for this study is time-series in nature with a temporal least count of 5 minutes (instantaneous values spaced every 5min throughout the period of data capture). The actual data and its list of parameters is dependent on solar plant capacity and design site. For the reference dataset, a grid-connected solar rooftop PV plant in India was studied and its loss parameters were estimated. The plant components are discussed in the prologue of the results section. Solar PV is such a technology which has been enjoying increasing demand and this market scenario is quite favourable for innovation in energy research. This paper hopes to not only introduce the context of PV losses but also tries to engage the motivation to adopt data-driven and empirical methodologies to understand modern systems. This approach is better in the sense that it only gets better at prediction as time goes by and there is more data. Industrial research such as the above work in critical analysis of PV systems not only helps identify possible limitations but also suggest room for improvement. Since energy generation and project cost are key towards maximizing revenue, these estimation models aimed at predicting PV losses are to be deemed indispensable. As with any estimation, there is no one unique way of hitting the bull’s eye that is to know the exact value. The algorithms proposed above are very much dependent on the quality and quantity of data. However, the comparison between losses estimated using plant data and standard simulation using energy modelling can act as feedback towards improving the design and maintenance of such PV systems.