Nature inspired evolutionary algorithm integrated performance assessment of floating solar photovoltaic module for low-carbon clean energy generation

Development and deployment of FSPV systems are still in the nascent stages hence long-term performance, control and feasibility study of FSPV systems are not well addressed. Precise and robust estimation of FSPV panel parameters will play an important role in determining the actual performance, carbon savings and long-term feasibility studies of FSPV systems. Here, hybrid stochastic firefly algorithm (HSFA) is used for parameter estimation and optimization. The hybrid algorithm is used to find out the parameters of single diode model, double diode model and FSPV module. An experiment is also performed using FSPV modules under varying irradiance conditions. The accuracy of model is evaluated by comparing the simulated results with the experimental results and computing the relative error and root mean square error (RMSE). The parameters extracted using the proposed method has a very low RMSE value of 9.83002E-04. Assessment of the experimental and estimated results show that the relative error for measured electricity on a sunny day is 0.57% while for an overcast day it is 0.89%. For partial shading condition, the relative error and RMSE was 0.79% and 6.5%, respectively. The results which are in well agreement with the experimental values demonstrate the superior performance of the model in determining the FSPV parameters. Proper estimation of the FSPV parameters will help researchers, scientists, engineers and all actors associated with solar PV systems in making sound judgements towards the deployment of FSPV systems and help the society in developing a sustainable ecosystem towards implementation of industry 4.0 by adapting to low-carbon power generation methods.