Bio-inspired metaheuristic algorithms have been widely applied in estimating the extrinsic parameters of a photovoltaic (PV) model. These methods are capable of handling the nonlinearity of objective functions whose derivatives are often not defined as well. However, these algorithms normally utilize multiple agents in the search process, and thus the solution process is extremely time-consuming. In this regard, it takes much time to search the possible solutions in the whole search domain by sequential computing devices. To overcome the limitation of sequential computing devices, parallel swarm algorithm (PSA) is proposed in this work with the aim of extracting and estimating the parameters of the PV cell model by utilizing the power of multicore central processing unit (CPU) and graphical processing unit (GPU). We implement this PSA in the OpenCL platform with the execution on Nvidia multi-core GPUs. Simulation results demonstrate that the proposed method significantly increases the computational speed in comparison to the sequential algorithm, which means that given a time requirement, the accuracy of a solution from the PSA can be improved compared to that from the sequential one by using a larger swarm size. photocurrent generated by photovoltaic cell saturation current of a diode diode ideality constant series resistance parallel resistance 
Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model