Photovoltaic power station photovoltaic panel cleaning cycle

How to optimize the cleaning cycle of PV power plants?

Cleaning cycle optimization with application of VWSC It is mentioned that PV power plants usually use a fixed cleaning cycle of 3 months, 2 months, or 1 month. Based on the field working conditions, a comparison of the optimal cleaning cycle and the cost evaluation with different cleaning cycles between MDCA and VWSC is given in Table 2.

What is the cleaning cycle of PV power plants in Thailand?

To determine the cleaning cycle, Mani and Pillai (2010) recommended appropriate cleaning cycle to mitigate the impact of dust based on average temperature, annual precipitation, and latitude of PV power plants. Considering the influence of geographic area, the cycle can be expanded to 2 months in Thailand (Sakarapunthip et al. 2017).

How often should PV modules be cleaned?

They suggested the modules cleaning frequency for desert regions should be approximately 20 days based on particle deposition velocity and accumulation density. Currently, research on cleaning cycle based on dust monitoring and cost evaluation for PV power plants in China has been very limited.

How much does it cost to clean a PV power plant?

The field test of MDCA has observed that the cleaning cost of dust is 125.37 $/MW at the recommended cleaning rate of 1.5 h/MW. Experiments and calculations carried out in a PV power plant in northeastern China have demonstrated that the optimal cleaning cycle is 10.1 days and the power conversion efficiency is reduced by 4.5%.

How often should PV power plants be cleaned?

Alternatively, they adopt a fixed cleaning cycle of 1 month, 2 months, or 3 months. To determine the cleaning cycle, Mani and Pillai (2010) recommended appropriate cleaning cycle to mitigate the impact of dust based on average temperature, annual precipitation, and latitude of PV power plants.

How can a PV system be optimally cleaned?

The need for an optimal cleaning intervention by using advanced scientific tools rather than by visual inspection is drawing the attention of PV experts. The authors finally suggest a schematic of a decision-making model which involves the use of probable parameters, data processing techniques and machine learning tools.