In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I−V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I−V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I−V curves.@en

In many applications, the current versus voltage curve of a photovoltaic cell, module, string, or field is acquired. A high number of samples are usually acquired, but the curve contains the main information in the open-and short-circuit points, as well as where it has a strong change in the slope. In this paper, these parts are called 'the fingerprint' of the photovoltaic generator. The fingerprint allows us to recognize the working conditions of the photovoltaic generator, e.g., if it is affected by a partial shadowing or not. Saving the fingerprint and discarding the other points of the original curve allows us to minimize the memory needs for storing the curve without losing the main information content. In this paper, a numerical technique for selecting, from among the samples of the acquired current versus voltage curve of any photovoltaic generator, the ones to be included in the fingerprint is proposed. The processing steps and the memory needed to achieve the result are minimized in order to allow an implementation of the algorithm also in a low-cost processor for on-field real-time applications. The technique is validated through curves generated by using analytical models as well as by means of some curves acquired experimentally in outdoor conditions.@en

The way of implementing an adaptive maximum power point tracking algorithm for photovoltaic (PV) applications in a field programmable gate array (FPGA) is described in this paper. A dual Kalman filter allows estimating the settling time of the whole system, including the PV source and the dc/dc converter controlling the operating point thereof, so that the tracking algorithm self adapts its parameters to the actual weather conditions. The real-Time identification need of this application requires an FPGA platform, so that the intrinsic algorithm parallelism is exploited and the execution time is reduced. The tradeoff solutions proposed in this paper, accounting for the algorithm complexity and the limited FPGA hardware, as well as some solutions for optimizing the implementation are described. The proposed adaptive algorithm is implemented in a low-cost Xilinx Spartan-6 FPGA and it is validated through experimental tests.@en

The maximum power point (MPP) tracking function allows one to extract the maximum power from the photovoltaic (PV) system in any operating condition. The perturb and observe technique is one of the mainly used algorithms aimed at this function. Its performances depend on the values of the two design parameters, which are the amplitude and the frequency of the perturbations that it applies in identifying the position of the MPP. In this paper, a method for the online optimization of the perturbation period is presented. It is based on the identification of the whole system, including the PV source and the dc/dc converter controlling it. The system impulse and frequency responses are evaluated by using the cross-correlation method. Such a technique, as well as the tracking algorithm, is effectively implemented by using a field-programmable gate array, and it is validated by means of simulation and experimental results.@en

In this paper, the use of the Dual Kalman Filter for the identification of photovoltaic system parameters is presented. The system includes the photovoltaic source, the dc/dc converter and the battery/dc bus and both its states and parameters in the actual operating conditions are identified. In particular, the proposed approach gives the confidence interval for the system settling time, which is used for the real-time optimization of the perturbative maximum power point tracking algorithm. The proposed technique is implemented by using a Field-Programmable Gate Array and it is validated by means of both simulation and experimental results.@en

The dynamical reconfiguration of the photovoltaic array is a feasible method for maximizing the electrical power it produces in presence of partial shading. Although in literature some algorithms aimed at determining the best configuration to be settled for a given shadowing pattern have been presented, no indications about the electrical conditions that can trigger the reconfiguration procedure have been discussed. In this paper some criteria for understanding if the photovoltaic array working conditions have changed, so that a new electrical connection among the modules can increase the produced power, are discussed. Moreover, some conditions revealing that the reconfiguration process can be delayed are analyzed as well. Simulation results are used for achieving the results shown in the paper and give some indications for improving the criteria formulated herein.@en

Electrochemical Impedance Spectroscopy (EIS) is a widely used tool for detecting degradation phenomena in electrochemical systems. In this paper a new approach for the fast estimation of fuel cell impedance is described. It is based on the use of a Pseudo-Random Binary Sequence as perturbation signal and the application of a Fast Fourier Transform algorithm to calculate the voltage and current harmonics in the frequency domain. With respect to the other approaches shown in literature, it is more suitable to run on an embedded system, thus allowing to perform the on-line diagnosis of fuel cell systems. The digital implementation of such method on a low cost FPGA device has been also proven.@en

In this paper a power processing system suitable for photovoltaic applications to sustainable mobility is introduced. The topology and the control technique have been developed in order to marry the maximization of the power produced by different small photovoltaic strings of cells subjected to different irradiation level and the minimization of the system cost, weight and complexity together. The system adopts a centralized maximum power point tracking controller measuring the output current only and avoiding a large number of sensors dedicated to each one of the strings. The switching converter topology requires a number of components that is smaller than the one required by a classical architecture. The proposed system has been modeled analytically and numerically simulated.@en

One of the main functions any photovoltaic system is equipped with is the maximum power point tracking. The perturb and observe technique is one of the mainly used algorithms for tracking the optimal operating point of the photovoltaic source. Its performances depend on the values of the two design parameters, that are the amplitude and the frequency of the perturbations it applies for identifying the position of the maximum power point. In this paper, two methods for an on-line optimization of the perturbation period are presented. They are based on the identification of the dc/dc converter, that controls the photovoltaic source, by means of its impulse and frequency response, both evaluated by using the cross-correlation method. Such methods, as well as the tracking algorithm, are effectively implemented by using a Field Programmable Gate Array. The feasibility of the proposed methods has been confirmed by Matlab simulations.@en

Shadowing, manufacturing tolerances and parametric drifts introduce mismatching effects in photovoltaic (PV) arrays. The consequent power drop can be faced by using power optimizers, which are module dedicated dc/dc converters, or micro-inverters. The main drawbacks of such devices are the high cost and the reduced plant efficiency during the day hours in which the PV array works under a uniform irradiation level. This usually happens in the middle of the day, just when the PV power reaches the peak. Moreover, both the dc/dc and the dc/ac module dedicated converters introduce further components that may reduce the system lifetime, e.g. large electrolytic capacitances. A recent alternative is represented by the dynamical re-configuration of the PV array by means of active switches: in this way, the efficiency under uniform irradiation conditions is preserved and, in presence of mismatching, the best series/parallel modules configuration is achieved. This solution is cheaper than the one involving power optimizers and, besides the possibility of plant monitoring, offers additional advantages in terms of safety in case of fire. An overview of the main reconfiguration algorithms and devices presented in literature and available on the market is given in this paper. Moreover, a novel approach to the PV string dynamical reconfiguration is sketched.@en

The aim of this paper is to present a Field Programmable Gate Array (FPGA) implementation of an adaptive perturb and observe (P&O) maximum power point tracking controller for photovoltaic application. The implemented controller is able to adapt the perturbation period of the P&O algorithm in order to face the changing environment conditions as well the aging of the photovoltaic system. The proposed implementation allows significantly improving the timing performances and reducing the FPGA resources. A complete time/area performance analysis and the experimental results confirm the feasibility and the efficiency of the proposed controller implementation.@en