An Maximum Power Point Tracker of Photovoltaic Module Arrays Based on Improved Firefly Algorithm

Abstract

In this paper, an improved firefly algorithm (FA) was proposed for application on photovoltaic module arrays (PVMAs) with partial modules under shading so that the maximum power point tracking (MPPT) could be implemented. Firstly, a new model of high voltage step-up converter was developed for executing the MPPT of the PVMA. For the energy storage inductor of the developed converter, the architecture of the coupled inductor was adopted so that the converter switch did not need to operate under an excessive duty cycle, which could increase the voltage gain and reduce the ripple of the output voltage at the same time. To prevent shading on the partial modules within the PVMA and generate more than one peak on the power-voltage (P-V) output characteristic curve, where the conventional MPPT could only track the local maximum power point (LMPP) and reduce the output power of the PVMA, the maximum power tracker, based on the improved FA, was proposed in this paper. Such a tracker could perform an automatic online adjustment on the FA iteration parameters, according to the slope of the P-V output characteristic curve, for the PVMA and, at the same time, perform at 0.8 times of the maximum power point (MPP) voltage for the module array under standard test condition (STC), serving as the initial tracking voltage for implementing the global maximum power point tracking (GMPPT). Lastly, the actual test results were applied to verify that the proposed converter indeed contained a high voltage step-up and low-ripple output voltage. The improved FA could also track GMPP faster and further improve the power-generating efficiency of the PVMA.

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

“…Table 6 demonstrates that the two proposed types of improved intelligent bat algorithms (IIBAs) outperform the improved firefly algorithm (IFA) [25] and the modified gray wolf optimization algorithm (MGWOA) [20] in terms of dynamic tracking speed and steady-state response.…”

“…As a result, the algorithms in this paper reduced the system's overall calculation load, shortening tracking time. In [25] the author adopted the gradient of the P-V curve to modify traditional firefly algorithms. However, in his paper, the author adjusted the tracking pace by dividing the work interval of the P-V curve.…”

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

“…Those algorithms draw inspiration from the social behavior of various species and have shown promising results in finding the MPP in solar PV systems. Some notable SI algorithms include particle swarm optimization (PSO) [17], artificial bee colony algorithm (ABC) [18], genetic algorithm (GA) [19], ant colony optimization (ACO) [20], firefly algorithm (FA) [21], grey wolf optimizer (GWO) [22], whale optimization algorithm (WOA) [23], cuckoo search (CS) algorithm [24], artificial fish swarm algorithm (AFSA) [25] and so on. These swarm MPPT techniques leverage the collective behavior and self-organization of agents in a swarm to efficiently explore the solution space and find the optimal solution presented in the operating point of PV systems [26].…”

An Adaptive Maximum Power Point Tracker for Photovoltaic Arrays Using an Improved Soft Computing Algorithm

“…The adopted high-voltage boost converter is illustrated in Figure 1 [42]. The rated input and output voltages of the converter are 80 and 400 V, respectively.…”