An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems

Parametric estimation in photovoltaic modules using the crow search algorithm

“…The application of the CSA to the problem of parametric estimation in PV modules produced the results reported in Table 2, where the best 10 solutions are presented after 100 consecutive evaluations. From results in Table 2, we can observe that: i) the value of the objective function related with the mean square error (see (7)) that evaluates the error regarding the open-circuit point, short-circuit point, and MPP provided by the manufacturer of the PV module and the calculated values using the single-diode model are lower (i.e., better) than 1 × 10 −29 , which can be considered null for any practical implementation. In this context, as mentioned in [5], all the parameters represent optimal solutions, moreover, these improve the conclusion reported in [10] wherein values lower than × −15 were considered optimal; ii) the solutions in the range from 3 to 8 present the same objective function value, that is, 7.8886 × 10 −31 , which confirm the multimodal nature of the problem of the parametric estimation in PV modules since there are different combinations of the decision variables that have the same numerical performance; iii) the electrical parameter that presents more variations along the optimal solutions is the parallel resistance since the minimum value reached for this parameters is found in the solution 10 with a value of 55.0001 Ω and the maximum value is found in the solution 5 with a value of 188.3342 Ω, that is, a difference superior than 120 Ω between both solutions; and iv) the average processing times reported by the CSA to find the numerical results reported in Table 2 was about 1.80 s with a standard deviation of 0.20 s, which demonstrates the efficiency of the CSA to find the global optimal solution.…”

“…It should be noted that to find the value of the objective function defined in (7), it is necessary to know the values of the parameters a, R s , and R p (decision variables) in conjunction with the simultaneous solution of the ( 4) and ( 5) for the inverse saturation and the PV current. To complete the optimization model for parametric estimation in PV modules considering manufacturer data, we assign the lower and upper bounds for the decision variables as in (11) [10]:…”

“…It should be observed that the proposed optimization model defined from (7) to (11) added with the equality constraints (4) and ( 5) corresponds to a continuous non-linear non-convex optimization problem. It implies that multiple solutions for the parameters a, R s , and R p can exist with the same numerical performance, that is, multimodal optimization behavior [22].…”

A high-performance MPPT algorithm combining advanced three-point weight comparison and temporary stopped running strategy for PV systems

“…A PV module's output power is, however, plagued by irradiance levels, temperature, loads, and so on, which undoubtedly impair conversion efficiency. A number of studies in the literature have addressed such problems and improved the overall conversion efficiency of a PV system [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Ahmed et al proposed a design strategy of perturbation parameters based on the HC algorithm for PV MPPT control [5].…”

“…Kumar et al presented a new version of P&O tracking algorithm, including self-predictive and decision taking abilities for PV maximum power extraction [9]. Al-Soeidat et al introduced an analog, BJT-tuned voltage reference MPPT method for PV modules [10]. Kumar et al proposed a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the PV array, which has inherent decision taking and self-adaptive ability [11].…”