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Soiling on photovoltaic (PV) systems reduces operating efficiency, requiring frequent cleaning and maintenance. To quantify soiling loss directly from the PV performance time-series data, an essential step is to detect cleaning events accurately. However, analyzing the time-series data of utility-scale PV plants comes with its own set of challenges. Sensors not calibrated or maintained correctly, missing or inconsistent time stamps, defective components (PV modules or inverters), and incomplete maintenance logs can affect the process. Due to these reasons, daily variations in the PV performance ratio time-series data tend to be high, making it noisy. To overcome the above issue, a modified 1-sigma Hampel filter is used in this work, which aims to remove noise from the PV performance time-series data. The Hampel filter uses a rolling window and median absolute deviation to detect outliers in time-series data. Compared with the recent state-of-the-art filters used in time-series analysis, the proposed filter can effectively deal with noise with a Gaussian or non-Gaussian distribution, is easy to implement, and is resilient to local outliers in the data. The filter is applied to three PV datasets of different capacities to detect cleaning events accurately. In addition, using SCADA data analysis, we also discuss the role of wind speed and relative humidity in PV modules' cleaning, which was reported to result in panel cleaning under specific controlled experiments. Through the comprehensive analysis of SCADA data and accompanying metainformation, we show that our algorithm can detect cleaning under high wind speed and low relative humidity.

In this work, a nanosecond green laser (532 nm) is used to generate narrow openings by removing an ultra-thin (85 nm) SiNxlayer that is coated on a silicon substrate for application in the fabrication of Passivated Emitter and Rear Contact (PERC) solar cells. An experimental analysis is presented to identify the optimal range of laser parameters for an efficient ablation with minimal damage to the silicon substrate. The ablated samples were characterized using a 3D profilometer to obtain the surface profiles and scanning electron microscope imaging to observe the surface quality. Further, energy-dispersive X-ray line analysis and atom probe tomography were performed to evaluate the nitrogen content on the surface and along the depth, respectively. The experimental results suggest that the SiNxlayer starts to ablate only above a threshold laser fluence of 1.4 J/cm2, while the surface bulged out for laser fluence slightly below the ablation threshold. The central part of the ablated region was clean with a negligible nitrogen concentration at the surface, about ∼0.03% at a fluence of 2.4 J/cm2. Nitrogen concentration reduces continuously and almost becomes zero at 80 nm depth, suggesting complete ablation of the SiNxlayer for establishing electrical contacts. The ablation width was close to the laser spot diameter only at lower values of the laser fluence. The lowest value of ablation depth was about 180 nm, suggesting that only about 95 nm layer of the silicon is ablated. The study demonstrates that nanosecond laser ablation is a potential technique for ablation of the SiNxlayer of PERC solar cells but requires choosing the optimal parameters.