Non-uniform conditions within a PV module lead to significant energy losses in conventional topologies. Some of the energy lost due to partial shading can be recovered by installing reconfigurable modules. In order to explore the potential benefits of reconfigurable topologies in installations where non-uniform conditions are dominant, three shading scenarios are simulated throughout the year, both for reconfigurable and conventional modules. This allows the evaluation of the performance of each topology and estimation of the long-term gains. Using a cost function, which includes the additional investment cost, the energy gain can be translated to financial terms. Through this, locations where reconfigurable topologies can be beneficial, both in energy and financial terms, can be identified. The methodology is applied to the case of reconfigurable PV modules with a snake-like configuration. Simulation results show that these PV modules allow for higher energy production compared to conventional modules under all the simulated shading scenarios. However, they remain economically profitable over 20 years only in locations where partial shading is expected to occur daily, either throughout the full year or at least in the period of the year where energy generation is high.

Photovoltaic modules may be subject to significant ageing during their lifetime. This is evident especially through the value of the series resistance, which is one of the five parameters appearing in the single diode model of the module. The identification of the series resistance value drift on the basis of on-field measurements is not trivial, because of the measurement noise and limitations in reaching the open circuit voltage conditions. In this paper, an approach to the identification of the operating parameters of the module through outdoor measurements is proposed. The method shows interesting features in view of its application to detecting module ageing phenomena during its lifetime.

With the growing competition of utility-scale photovoltaic (PV) plants to provide electricity to the grid, accurate energy yield simulation is becoming essential. Apart from irradiance, forced convection can have an equally significant impact on solar cell temperature, which in turn affects the power output, hence the energy yield. As a consequence, the wind must be taken into account for an accurate estimation of PV installations' energy yield. However, no earlier studies have systematically investigated the impact of wind on precise energy yield simulation. In this study, we investigate how sensitive is PV module's output to forced convection. First, we reveal a significant deviation between the simulated PV module's energy yield and cell temperature compared to experimental results in case dynamic wind effects are neglected. Secondly, for controlled incremental forced convection with a steady state scenario, we show that for higher wind speed the change of PV module's cell temperature and power output is small with different forced convection. Whereas, for the low wind speed, small changes of forced convection imply a larger variation of the PV module's cell temperature and power output. This shows that for high wind speed region energy yield estimation is less sensitive on modelling errors. Whereas for the low wind speed region it is highly sensitive.

Photovoltaic modules parametric identification is often performed based on data available in the data sheet and referred to standard test conditions. On-line identification is trickier when irradiance and temperature sensors are not available. In this paper, an identification procedure is presented aiming to identify some module fault conditions that often occur during its lifetime. The proposed approach is applied to a large database of current vs voltage curves that are experimentally measured on a real photovoltaic plant.

PV module testing under standard conditions is an important and well-established procedure, which plays a vital role in module rating. However, PV modules rarely operate at standard conditions therefore their field performance should be predicted based on long term outdoor monitoring or by means of models – so called energy yield models, which combine PV module characteristics with varying environmental conditions. The present work employs a bottom-up, physics-based energy yield modelling approach, which accounts to the interacting optical, thermal and electrical mechanisms in a detailed manner. Additionally, measured data is used for the accurate calibration of the models. Such an approach permits to explore the influence of cell- and module technology details on energy yield under any specific environmental conditions. The present work employs such a method to evaluate the influence of Silicon solar cell technology on energy yield under desert and moderate climates, where the interplay of different irradiance and ambient temperature levels result in a challenging PV performance prediction problem. The purpose of this work is to identify the best-suited solar cell technologies and to understand the underlying mechanisms, which lead to superior PV performance under specific climate conditions. The study is performed by means of physics-based exploratory energy yield simulations with detailed resolution of the thermal effects. Our comparison of four different cell technologies in monofacial modules highlights that superior illumination-dependent performance can contribute to annual energy yield enhancement under both moderate and desert climates amounting to 1.75% and 0.4%, respectively; while a 0.04%/°C advantage in relative temperature coefficient increases annual energy yield (by 1.2%) only under a desert climate.

Reconfigurable photovoltaic modules represent an effective solution to improve PV system resilience to partial shading. Indeed, the availability of different configurations increases energy generation under non-uniform conditions. However, the additional components that are active under uniform conditions lead to higher losses compared to equivalent static solutions. This paper presents a methodology for the design of optimized reconfigurable PV modules, balancing losses under uniform conditions and gain under partial shading. First, feasible reconfigurable module instantiations are selected given some design constraints. Then, the search space is further reduced by taking into account the typical operating conditions of the module. Finally, the best module layouts are chosen based on performance consideration. Results for a specific case study are presented to show the feasibility of the proposed methodology.

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.

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.

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.

In this paper, the different aging mechanisms taking place in photovoltaic modules are discussed, and the cause-effect links, which exist among such mechanisms, are evidenced. It is also shown that a closed-loop link exists between aging and mismatching since aging (which is nonuniform by its nature) causes mismatching among cells, whereas mismatching, in turn, mainly due to its thermal effects, leads to nonuniform aging.

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.

In case of mismatch (due to clouds, shadows, dirtiness, manufacturing tolerances, aging, different orientation of parts of the PV field, thermal gradients, etc.), the Power versus Voltage (P-V) characteristic of a PV field may exhibit more than one peak, because of the adoption of bypass diodes, therefore Centralized Maximum Power Point Tracking (CMPPT) algorithms can fail. The consequent power drop can be avoided 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. In this paper a novel approach to the PV string dynamical reconfiguration is presented and discussed.

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.

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.

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.

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.

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.

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.

The goal of this work is to investigate the influence of the Na incorporation method into CuIn _1-xGa _xSe ₂ (x=Ga/(In+Ga)) (CIGSe)-based solar cells on polyimide (PI) foil. In particular we want to compare the effect of a NaF precursor layer with that of NaF co- and post-deposition. Secondary ion and neutral mass spectroscopies (SIMS/SNMS) are used to study the distribution of the elements through the CIGSe layers. Cross-sectional scanning electron microscopy (SEM) shows the dependence of the absorber microstructure on the method of how Na is supplied with and without Ga present. Adding Ga the device microstructure is generally characterized by smaller CIGSe grains next to the Mo back contact, which indicates the very low process temperature used. The use of a NaF precursor, our standard method for the supply of Na, modifies the growth kinetic of the absorber layer and emphasizes the importance of growth parameters such as the Cu flux and max Cu content during the deposition process. Optimization of the deposition process that uses a NaF precursor, so far led to a max efficiency of 15.9 % (ta = 0.95 cm ²). In the case of NaF post-deposition the use of a low process temperature is argued to reduce the Na diffusion throughout the absorber layer, which may reduce the quality of the CIGSe/Mo back interface. This may explain the lower FF observed generally for this process in comparison to that where Na is supplied by a precursor layer.