A tool for performance evaluation of MPPT algorithms for photovoltaic systems

Abstract

The use of photovoltaic (PV) systems as electrical power generator is increasing rapidly and this growth is expected to be continuous. As well known in PV systems the tracking of the maximum power point (MPP) is fundamental. For this reason, many testing systems and maximum power point tracking (MPPT) algorithms have been developed. Testing systems are used for the development as well as for assessing the quality of PV products. The typical problem related to the evaluation of PV system performances and MPPT algorithm comparison is the guarantee of the repeatability of the testing conditions (e.g. temperature and radiation). In this paper, a testing system for PV applications has been developed and characterized. Flexibility, ease of use, low cost, capability to evaluate the characteristic curves of more than one PV panel simultaneously (same environmental conditions), the capability of algorithms simulation, and capability of tracking MPP on the base of different MPPT algorithms are some of the main characteristics. Experimental tests have been carried out to show the capability of the developed architecture. Different MPPT algorithms have been compared in the same environmental conditions both in the steady and dynamic state.

Comparing the Performances of MPPT Techniques for DC-DC Boost Converter in a PV System

“…Extensive research has been done on MPPT methods classified in Figure 4. Several MPPT methods can be found in practice and the literature [14][15][16][17]. These techniques are capable of reaching up to 99 % of the MPP efficiency.…”

Model‐based maximum power point tracking for photovoltaic panels: parameters identification and training database collection

“…From each curve, the MPP voltage and current can be accurately extracted by means of proper techniques. This approach, which is the most intuitive choice also adopted in previous papers [40][41][42][43][44], suffers from several limitations. First of all, it requires hardware having this capability, but most importantly, the panel cannot produce deliver energy to the grid when training data is collected.…”

“…To overcome this limitation, the authors of the present work have studied a model‐based (MB) MPPT algorithm that requires measuring the solar radiation only during the estimation of the model parameters, but not during regular operation [40, 41]. Furthermore, [42] proposes an innovative, iterative MB‐MPPT method that allows completely avoiding the measurement of solar radiation level; comparison with other, well‐known algorithms show the effectiveness of the approach [43]. As for the other MB techniques, it requires a preliminary identification of the parameters before it can be put in operation.…”

A review of recent advances in metaheuristic maximum power point tracking algorithms for solar photovoltaic systems under the partial-shading conditions

“…MPPT is a solution to escalate the productivity of electric power with the DC-DC converter circuit and control algorithm as its main parts. According to various studies, this system consists of different algorithms, such as incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), perturb-and-observe (P&O), grey wolf optimizer (GWO), firefly algorithm, ant colony optimization (ACO), and artificial bee colony (Dorigo & Birattari, 2006;Mirjalili et al, 2014;Safarudin et al, 2015;Soulatiantork et al, 2018). MPPT is used in PV systems to maximize tracking and for continuous tracking.…”