Salp Swarm Optimization Algorithm for Estimating the Parameters of Photovoltaic Panels Based on the Three-Diode Model

Abstract

Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera KC200GT and Solarex MSX60, which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function.

Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm

“…This was achieved by implementing another optimization algorithm to obtain the GA parameters that best fit the minimization of the problem. In this case, we used the PSO algorithm proposed in [50,51] to tune the following values: population size p (individuals or particles), with a search range of 2-100; number of iterations iter max (maximum iterations allowed in the GA), with a search range of 1-10,000; and number of non-improvement iterations iter nmax (this determines the point at which the algorithm stops performing convergence or minimizing the objective function), with a search range of 1-10,000. The results obtained from this tuning process were p = 11, iter max = 8761 and iter nmax = 4061.…”

Adjusting the Stiffness of Supports during Milling of a Large-Size Workpiece Using the Salp Swarm Algorithm

“…Although the SSA is a relatively new algorithm (published in 2017), it has already gained recognition because of its simplicity and properties. Some applications, mostly related to energy distribution and production systems, include, for example, optimization of wind turbine location [ 59 ], optimization of power system operations [ 60 ], estimation of the parameters of photovoltaic panels [ 61 ], and prediction of wind power [ 62 ]. Some other examples include UAV path planning [ 63 ], design of PID-fuzzy control against an earthquake for a seismic-exited structural system [ 64 ], and prediction of pressure burst in pipelines [ 65 ].…”

Aplicación del algoritmo de optimización por enjambre de salpas para la estimación de parámetros en transformadores monofásicos empleando medidas de tensión y corriente

“…Se define la población inicial de la cadena de las salpas de forma aleatoria a partir de la ecuación 13, donde cada salpa es una posible solución al problema de optimización (i. e., una fila dentro de la matriz) [28].…”

“…Este movimiento se da para la población restante, es decir, la población que está desde la mitad de la cadena hasta el final de la misma; para ello se utiliza la tercera ley del movimiento de Newton para representar el movimiento de las salpas seguidoras (ver ecuación 17). Este movimiento se basa en compartir la información de la salpa adyacente y así generar nuevas ubicaciones en el espacio de solución [28].…”