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Photovoltaic arrays may suffer from a number of temporary and permanent faults. Partial shading and soiling belong to the former group, while cell cracking and delamination fall within the latter one. In these cases, the shape of the current vs voltage curve around the maximum power point shows features that are different from those ones of an array operating in normal conditions. The shape change should allow triggering, in case of a temporary fault, control actions that might improve the electrical power production. Instead, if the fault is permanent, the identification of the modified shape should activate a procedure for a more in-depth analysis of the problem and a maintenance action. In this paper, the conditions leading to a change in the array behavior during its delivering of the maximum power are examined. The change of curvature of the current vs voltage curve around its maximum power point is suitably detected to trigger the fault mitigation action. The feature is caught through an ensemble of artificial neural networks, which analyzes the current vs voltage curve and classifies the module as healthy or faulty. It is demonstrated that few samples around the maximum power point are required, this meaning that the proposed approach is compatible with the operation of any perturbative maximum power point tracking algorithm and its application does not lead to any power production drop. In addition, the approach does not require neither temperature nor irradiance measurements as inputs. The neural networks are trained through synthetic data, so that their application is not limited to arrays including a specific photovoltaic module. The method is also validated through experimental data. [Display omitted] • Some types of PV faults lead to a change of the shape of the I – V curve around the MPP. • An Artificial Neural Network catches this change to classify a curve as healthy or faulty. • Only few samples around the MPP are required without irradiance or temperature data. • The model is trained with synthetic data being not limited to a specific module. • Best trained neural networks can be combined into an ensemble to improve the results. [ABSTRACT FROM AUTHOR]

In this paper, a procedure aimed at the automatic extraction of the features from polymer electrolyte membrane fuel cell impedance spectra is proposed. An artificial neural network that is trained by exploiting the similarity learning concept has been used. The network learns the features of the impedance spectra and maps each of them into the embedding space by clustering them accurately and by emphasising differences among spectra corresponding to different faults. The siamese network structure is optimised and the quality of the learnt representation is evaluated by analysing the clusters obtained in the features space. The dataset of experimental spectra has been augmented in two different ways and the results are compared. The clustering quality of the proposed siamese network is compared with the one of other state of the art approaches. [Display omitted] • A Siamese Neural Network is used to analyse the impedance spectra of a PEM fuel cell. • The SNN is able to extract the features corresponding to each faulty condition. • Two different data augmentation approaches are used to train the SNN and their performance is analysed. • The approach is tested on a set of noisy impedance spectra. [ABSTRACT FROM AUTHOR]

The nine papers in this special section focus on photovoltaic module and sub-module level power electronics. Grid connected photovoltaic energy systems have experienced an explosive growth over the last decade, with a cumulative installed capacity surpassing the 400 GW milestone as of 2017. Among PV system configurations, distributed module-level converter architectures can lead to a higher energy yield by mitigating partial shading, persistent shading (soiling, snow, bird droppings, and fallen leaves), mismatch, and aging, through a higher maximum power point tracking (MPPT) efficiency. Also, distributed electronics might be the key for implementing diagnostic and prognostic actions at a module level. Among these configurations, microinverters (also known as ac-module inverter), which connect a single PV module to the grid, and PV power optimizers, which are dc–dc converters performing the MPPT function at a module level, have attracted the academic and industrial interest in the last decade. So much so, that both microinverters and dc–dc power optimizers are commercialized by tens of companies around the world, with a great variety of circuit topologies, which comprise combinations of one or more power stages, interleaved converters, resonant converters, topologies with and without isolation, etc. [ABSTRACT FROM AUTHOR]

The photovoltaic literature contains a wide range of methods for translating the I–V curves of a solar device to other conditions of irradiance and cell temperature, different from those under which the measurements were performed. Some of these translation methods are included as part of the International Standard IEC 60891. In this paper, these techniques are classified, reviewed, and implemented to perform a deep comparative analysis between them and to discuss their suitability for converting the I–V curves of photovoltaic modules under different scenarios of irradiance and temperature. From the analysis conducted, it can be seen that the interpolation method proposed in IEC 60891 achieves accurate results when it is applied to correct small and medium irradiance and temperature gaps. If no interpolation is possible and for large irradiance corrections, other procedures described in IEC 60891 can be applied. However, certain explicit methods based on the single-diode model or on the double-diode model can overcome the most well-known approaches proposed by the standard. [ABSTRACT FROM AUTHOR]

Highlights: [•] Minimizing hydrogen consumption in fuel cell applications. [•] Perturbation strategy for optimization. [•] Virtual sensing of the oxygen excess ratio. [•] Hardware in the loop test of a FPGA controller. [ABSTRACT FROM AUTHOR]

TEODI is the acronym of a maximum power point tracking (MPPT) technique which has been recently presented in the literature. Such a technique is particularly suitable in distributed MPPT photovoltaic (PV) applications, and it is based on the equalization of the output operating points, of the dc/dc converters dedicated to each subsection of the PV array, in correspondence of the forced displacement of their input operating points. The main advantages of TEODI, its performances in terms of MPPT efficiency and speed of tracking, and the intrinsic capability to attenuate the effect, on the voltages of the PV modules, of the 100/120 Hz disturbances coming from the grid are shown by means of simulation results and experimental findings obtained by using a laboratory prototype developed by means of analog circuitry only. [ABSTRACT FROM PUBLISHER]

In this paper, a novel maximum power point tracking algorithm based on the perturb-and-observe (P&O) technique is introduced. The novelty of the approach is represented by the perturbation of more control variables, rather than just one. This allows the increase of the power extracted from the photovoltaic (PV) field as compared to the case of perturbation of a single control variable. The proposed technique overcomes the limitations of any existing tracking technique dedicated to PV arrays exhibiting a unique maximum power point, thus not only of the P&O approach, whenever dynamic constraints ensuring the correct system behavior must be fulfilled too. The proposed multivariable approach is described by means of its application to a single-stage one-cycle controlled PV inverter. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Maffezzoni and colleagues on an electrothermal macromodel for photovoltaic (PV) module, one by Velasco-Quesada and colleagues on an electrical array configuration approach on the PV plant and another by Romero-Cadaval and colleagues on a grid-connected PV system which employs two parallel-connected inverters.

Power processing systems will be a key factor of future photovoltaic (PV) applications. They will play a central role in transferring, to the load and/or to the grid, the electric power produced by the high-efficiency PV cells of the next generation. In order to come up the expectations related to the use of solar energy for producing electrical energy, such systems must ensure high efficiency, modularity, and, particularly, high reliability. The goal of this paper is to provide an overview of the open problems related to PV power processing systems and to focus the attention of researchers and industries on present and future challenges in this field. [ABSTRACT FROM AUTHOR]

This paper introduces an evolutionary approach to the variability analysis of nonlinear systems. The algorithm shows some advantages with respect to classical stochastic approaches and, in particular with respect to Monte Carlo-based methods. First, it allows a significant reduction of the underestimation error that affects any nondeterministic approach; in other words, it guarantees a considerable saving of simulation time. Moreover, the technique ensures a higher reliability in pursuing the true range of variation of the system performance of interest. The method has been applied to a practical problem of great interest: the variability analysis of a stress control tube made of a composite material used for electric stress relief in cable accessories. The cable termination has been modeled by means of a lumped nonlinear circuit. The proposed technique also provides reliable results whenever large parameter variations need to be taken into account and/or nonlinear functions of varying parameters are considered. Simulation results prove it to be of great support for the designer in cable termination dimensioning and in composite material selection. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary approach to worst case tolerance design of magnetic devices. The algorithm improves on the classical nominal design, accounting for parameter variations and tolerances, so that the system performance does not exceed upper and lower specifications imposed in advance by the designer. The evolutionary algorithm adjusts central values and tolerances of parameters so that a user-defined objective function is optimized and the design constraints are met. The feasibility of the candidate sets of design parameters and tolerances is estimated by means of vertex analysis. The algorithm ensures that the optimized design set of parameters and tolerances is 100% feasible in a large number of practical cases. This paper gives an example of the design of a magnetic device and discusses the salient features and limitations of the approach. [ABSTRACT FROM AUTHOR]

Presents a study which featured a method for steady-state analysis of pulsewidth modulation (PWM) hard-switching direct current-to-direct current (dc/dc) regulators. Characteristics of the steady-analysis; Schematic representation of a PWM dc/dc switching regulators; Search of the steady-state solution; Examples of dc/dc regulators.

Focuses on a study which proposed a unified method for steady-state analysis of hard-switching and soft-switching direct current-to-direct current (dc-to-dc) converters. Illustration of the proposed method; Examples of applications; Discussion on dc-to-dc converters.

Analyzes the synchronous commutations in switching converters. Definition of zero-order and first-order master-slave connection matrix; Inclusion of feedback circuitry and soft-switching cells; Reduced circuit for the analysis of resistive linear networks.

Discusses the steady-state analysis of soft-switching converters. Break-and-cut strategy for interval contraction; Analysis of commutations by means of compensation theorem; Algorithm for accelerated steady-state analysis.

Discusses a study which presented an approach to the calculation of the true worst case in circuit tolerance analysis (TWC-CTA) with parameters characterized by large uncertainties. Calculation of the inner solution in TWC-CTA problems using genetic algorithms; Calculation of the outer solution in TWC-CTA problems using affine arithmetic; Examples.

Deals with a study which presented an approach for improving the efficiency and accuracy of interval arithmetic based on worst case circuit tolerance analysis (TA). Formulation of the problem; Discussion on large-change and small-change TA; Methods for solving TA problems; Interval arithmetic in TA problems; Evolution program for uncertainty interval partitioning optimization; Conclusions.

[Display omitted] • A neural network is used to detect faults in photovoltaic modules. • According to the shape of the current vs voltage curve, the faults are classified. • The training uses synthetic curves and the test is performed also on experimental data. • The points are normalized and resampled radially to improve the results. • With two hidden layers of 100 neurons each, the hit rate reaches more than 98.5%. In this paper a model-based procedure for fault detection and diagnosis of photovoltaic modules is presented. A four-layered feedforward artificial neural network learns the correlation between the features of the current vs. voltage curve and the environmental variables, which are the irradiance and the temperature. This correlation describes the behavior of the module at normal conditions. Moreover, the effect of anomalous variation of some parameters is learnt and correlated to the shape of the same curve, thus associated to a specific failure mechanism and to some assigned ranges quantifying the fault severity. The neural network is trained by using synthetic curves simulated by employing the single diode model and some well assessed and validated translation formulae. The obtained results over the simulated set of curves with different failures allow to achieve a classification error lower than 1.5%. The proposed approach has been also validated for detecting anomalous increases of the series resistance in a large experimental set of curves; in this case, a classification error of 2.7% has been achieved. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on a cell-layer-scale multidomain dynamic one-dimensional (1-D) polymer electrolyte membrane (PEM) fuel cell (FC) stack model using very high speed integrated circuit hardware description language analog-and mixed-signal extension modeling language, another on a large-signal dynamic model of PEM FC, and a report comparing two PEM FC control objectives.

The article discusses various reports published within the issue, including one by D. Sera on optimized versions of maximum power point tracking (MPPT) and another by Y. Ueda on the proposal for significant analysis founded on a large database of experiment data, of losses due to the grid voltage rise.

The articles in this special section focus on photovoltaic power systems (PVS). The market for photovoltaic power systems has increased exponentially, but the government subsidies have been reduced dramatically in recent years. The return of the investment mainly relies on selling the energy produced during the PV plant lifetime. Thus, the PV system must be designed and controlled properly in order to ensure its lifetime and reliability but also new features are required: 1) real-time monitoring of the energy production; 2) on-site diagnostic functions; and 3) estimation of the residual life. The degradation of electronic components and PV cells can be accelerated by various unpredictable and unavoidable phenomena. [ABSTRACT FROM PUBLISHER]

• Parametric identification of a Fuel Cell stack based on the Interval Arithmetic. • Robustness with respect to the uncertainties affecting the EIS measurements. • Smart Branch and Bound algorithm for reducing computational burden and memory. • Suitable for the on line diagnosis of electrochemical systems as FC and batteries. In this paper, the identification of the equivalent circuit parameters of a polymer electrolyte fuel cell stack is performed for diagnostic and monitoring purposes. An interval arithmetic branch and bound method is developed for the identification of parameters of the Fouquet model, frequently used for fuel cell modeling in the frequency domain. The proposed method allows to bound each parameter by an interval of real values. It also provides the model sensitivity with respect to the parameters. The method is also able to account for uncertainties affecting the impedance measurements at the assigned frequency values. For the specific application to fuel cells, the approach overcomes the limitations, in terms of interval contraction capability, characterising standard interval analysis-based techniques. The features of the proposed approach, shown with various examples also involving experimental measurements, are useful in monitoring the stack performance, to the final aim of improving its lifetime, and ensuring the expected electrical power production. The proposed results allow to envisage an application of the method also in real time conditions, when the fuel cell model parametric identification has to be run by using an embedded system instead of a personal computer. [ABSTRACT FROM AUTHOR]

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel method to identify the SDM parameters and operating conditions of a PV module. • It does not require irradiance nor temperature, but identifies both accurately. • The method can be applied using only a small portion of the curve around the MPP. • Fort the first time, a comparison of various fitting approaches is presented. • It is proven that fitting approach is a factor to be considered for I – V curve fitting. One solution for online condition monitoring of photovoltaic (PV) modules is to identify single-diode model parameter values from measured current–voltage (I – V) curves. By this way, use of expensive thermal cameras and radiometric sensors utilized in traditional monitoring methods can be avoided. Unfortunately, most of the parameter identification methods require measurements of the operating conditions, i.e., irradiance and temperature. This article proposes a novel procedure for identification of the single-diode model parameter values along with the operating irradiance and temperature values from measured I – V curves without needing any other measurement. The only inputs of the proposed procedure are the I – V curve measurements at the actual operating conditions together with the parameter values of the module model in standard test conditions. The proposed procedure is experimentally validated using I – V curves of three PV module types measured from two different locations. Both the whole I – V curves or only a part of them, in a limited voltage range, are considered. Moreover, I – V curve measurements with an emulated increase of the series resistance are used to demonstrate the correctness of the identified series resistance values. It is shown that the procedure identifies the operating irradiance and temperature with high accuracy even during sharp irradiance transitions and low irradiance conditions and identifies series and shunt resistances very reliably under nearly constant high irradiance conditions. Moreover, for the first time, a comprehensive comparison of various fitting approaches based on root-mean-square error (RMSE) minimization, including two novel approaches, is presented. The results show that the different fitting approaches based on RMSE minimization affect the accuracy of the parameters identification in a different way, this meaning that the used fitting approach is a factor that should be considered when implementing model parameter identification by curve fitting. [ABSTRACT FROM AUTHOR]

The performance of four thin-film photovoltaic modules is analyzed after an initial stabilization period and a subsequent outdoor exposition. The seasonal variations and the degradation rates of a single-junction hydrogenated amorphous silicon (a-Si:H) module, a tandem amorphous microcrystalline Silicon (a-Si/ μ c-Si) module, a heterostructure cadmium sulfide-cadmium telluride (CdS/CdTe) module and a copper indium gallium selenide (CIGS) are examined and correlated to spectral changes. The I–V curves have been measured every 5 min; the electrical parameters and parasitic resistances have been identified. By exploiting a number of experimental measurements acquired within a narrow interval of irradiance and cell temperature, a novel mathematical model has been developed and fitted: it considers a stationary seasonal variation component and a linear long-term degradation component. The results show annual power degradation rates of 4.0% for the a-Si:H module, 3.4% for the a-Si/ μ c-Si and 3.5% for the CdS/CdTe, whereas for the CIGS module the annual degradation is not significant, i.e. 0.2%. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, the procedure for designing a sliding-mode controller for maximum power point tracking photovoltaic (PV) applications is proposed. It is applied to a single-ended primary inductor converter, thus to a fourth-order topology, but it can be extended to a wide class of converters suitable for PV applications. The reachability and existence conditions give rise to a number of design inequalities that add to the classical steady-state conditions in order to have the desired closed-loop converter's performances. The superiority of the control technique over the classical tracking methods is analytically demonstrated through the procedure proposed in this paper. The analytical results have been validated by means of both simulations and experiments. [ABSTRACT FROM PUBLISHER]

Highlights: [•] The steady state MPPT efficiency of the P&O algorithm has been analysed and the optimal operating conditions have been identified. [•] An improvement of the P&O algorithm based on a two steps perturbation has been proposed. [•] The simplicity and the computational burden of the classical P&O algorithm have been preserved in the proposed algorithm. [•] The quantization errors and numerical approximations introduced by the digital implementation have been properly compensated. [•] The new algorithm is suitable for an implementation on cheap digital controllers. [Copyright &y& Elsevier]

The study of a double-stage single-phase inverter for fuel-cell-based applications is proposed in this paper. A novel control strategy aimed at reducing the low-frequency oscillations of the fuel-cell (FC) current in order to guarantee the FC safety operating conditions is proposed. The reduction of such oscillations increases the FC lifetime and avoids high mechanical stress of the membrane and unnecessary consumption of reactants. Furthermore, it allows one to design a strategy for extracting the maximum power from the FC stack with a total control of the concentration losses. Simulation and experimental results confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In double-stage grid-connected photovoltaic (PV) in. verters, the dynamic interactions among the dc/dc and dc/ac stages and the maximum power point tracking (MPPT) controller may reduce the system performances. In this paper, the detrimental effects, particularly in terms of system efficiency and MPPT performances, of the oscillations of the PV array voltage, taking place at the second harmonic of the grid frequency are evidenced. The use of a proper compensation network acting on the error signal between a reference signal provided by the MPPT controller and a signal that is proportional to the PV array voltage is proposed. The guidelines for the proper joint design of the compensation network (which is able to cancel out the PV voltage oscillations) and of the main MPPT parameters are provided in this paper. Simulation results and experimental measurements confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The DC-link capacitor is one of the components that are more prone to faults in energy-distributed systems based on voltage source inverters. A predictive maintenance approach should allow to foresee the risk of an unexpected system shutdown. In this study, a two-stage diagnostic approach that is aimed at determining the health status of the DC-link capacitor in a single-phase grid-connected PV system was proposed. The equivalent series resistance (ESR) and the capacitance (C) values were used as indicators in the estimation of the degradation stage. Electrochemical impedance spectroscopy (EIS) was used to estimate the impedance curve of the DC-link capacitor, and a multi-fitting algorithm allowed us to determine the ESR and C parameters. A comparison between the estimated values C and ESR and the nominal values was used to quantify the fault severity. It was demonstrated that the EIS allowed the determination of the capacitor impedance regardless of the actual operating conditions of the photovoltaic generator, such as during irradiance changes and with the maximum power point algorithm turned off. By using the capacitor simplified model and a multi-fitting algorithm, the C and ESR values were estimated with an error that was lower than 1%. An analysis of the hardware required to implement the proposed approach in real applications by achieving the desired accuracy was also proposed. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point (MPP) which depends on panels temperature and on irradiance conditions. The issue of MPPT has been addressed in different ways in the literature but, especially for low-cost implementations, the perturb and observe (P&O) maximum power point tracking algorithm is the most commonly used method due to its ease of implementation. A drawback of P&O is that, at steady state, the operating point oscillates around the MPP giving rise to the waste of some amount of available energy; moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper it is shown that, in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such parameters is also carried out. Results of experimental measurements are in agreement with the predictions of theoretical analysis. [ABSTRACT FROM AUTHOR]

Online/on-board diagnosis would help to improve fuel cell system durability and output power. Therefore, it is a feature the manufacturers may wish to provide for final users to increase the attractiveness of their product. This add-on requires suitable stack models, parametric identification tools and diagnostic algorithms to be run on low-cost embedded systems, ensuring a good trade-off between accuracy and computation time. In this paper, a computational approach for the impedance parameter identification of polymer electrolyte membrane fuel cell stack is proposed. The method is based on an evolutionary algorithm including sub-population and migration features, which improves the exploration capability of the search space. The goal of the evolutionary algorithm is to find the set of parameters that minimizes an objective function, representing the mismatch between two impedance plots in a normalized plane. The first plot is associated with experimental impedance and the second is computed on the basis of the identified parameters using a circuit model. Three kinds of impedance models, characterized by increasing computational complexity, are used, depending on the experimental data—a linear model made of resistors and capacitors, the Fouquet model and the Dhirde model. Preliminary analysis of the experimental impedance data may evidence correlations among parameters, which can be exploited to reduce the search space of an evolutionary algorithm. The computational approach is validated with literature data in a simulated environment and with experimental data. The results show good accuracy and a computational performance that fits well with the commercial embedded system hardware resources. The implementation of the approach on a low-cost off-the-shelf device achieves small computation times, confirming the suitability of such an approach to online/on-board applications. From a diagnostic perspective, the paper outlines a diagnostic approach based on the identified impedance parameters, on the basis of a small set of experimental data including fuel cell stack faulty conditions. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) arrays are usually formed by strings of panels connected in parallel creating the series-parallel (SP) configuration. Additional connections can be implemented among the strings of the SP configuration to mitigate a particular mismatching profile. Those connections may follow regular or irregular patterns to form regular configurations , e.g. Total Cross-Tied (TCT), Honey-Comb (HC) or Bridge-Linked (BL), or irregular configurations , respectively. In literature there are proposed models for particular array configurations; however, there has not been reported yet a general model capable to describe the behavior of any regular or irregular configuration. Therefore, testing multiple configurations require to parameterize or design multiple models. This paper introduces a procedure for reproducing the electrical behavior of any PV array operating under uniform or mismatched conditions, which eliminates the requirement of using multiple models to evaluate different configurations. The procedure is based in dividing the array into sub-arrays to construct a system of non-linear equations for each sub-array, which is solved by using the Trust-Region Dogleg method. The proposed approach is validated by comparing the results provided by the proposed model with the circuital implementation of the same PV array in a commercial software and with the data produced with an experimental test bed. Such comparisons put into evidence the satisfactory performance of the general modeling procedure in the reproduction of the electrical behavior of both regular and irregular arrays. [ABSTRACT FROM AUTHOR]

A zero-current switching full-bridge isolated dc/dc converter based on a semiresonant L-C cell has been developed in this paper. With respect to the classical full-bridge configuration, no additional switches are required, thus by preserving its simplicity and robustness. The proposed architecture allows achieving zero-current switching at both primary and secondary sides. A 50-kW prototype has been developed and tested in different operating conditions. The experimental results are in agreement with the simulations by confirming the goodness of the proposed solution. Efficiency up to 97% at full load operating condition has been reached in the laboratory prototype. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

The long power production warranties guaranteed by the manufacturers of photovoltaic (PV) modules (25 years) are a vital factor for the economical viability of PV systems. Nevertheless, recent studies have demonstrated that the modules may experience an early degradation, which turns into the reduction of the power production or even the damage of the entire PV module. In this paper, a method aimed at detecting the module degradation is introduced. It detects and quantifies the degradation by means of two indicators, which are aimed at estimating the increment of the modules series resistance, this parameter being one of the most affected by some degradation mechanisms. The increase of the series resistance value is estimated through the evaluation of the error in predicting the position of the maximum power point, which is obtained by comparing the experimental measurements with the single-diode model. The proposed method is experimentally validated, and it is compared with other methods reported in the literature. The results show that the proposed indicators are able to accurately estimate the increments of the series resistance with a low sensitivity to the irradiance and temperature values. This feature improves the accuracy and the reliability of the diagnosis processes and monitoring systems. [ABSTRACT FROM PUBLISHER]

Photovoltaic (PV) systems are one of the main actors in distributed power generation. In particular, in urban contexts, the PV generators can be subjected to mismatching phenomena due to the different orientation of the modules with respect to the sun rays or due to shadowing. In these cases, the maximum power point tracking (MPPT) function must be designed carefully. In this paper, architecture, including one dc/dc converter for each PV generator, is considered. The converters' output terminals are series connected to a high-voltage dc bus, where also a bidirectional dc/dc converter managing the power from/to a storage device is plugged. The functional constraints deriving from the dc/dc converters' connection, the mismatching phenomena, the MPPT capabilities of the inverter, connected with its input terminals at the dc bus, are taken into account in order to determine the best operating point of the system as a whole. The real-time constrained optimization problem is solved by using the particle swarm optimization method, which needs the knowledge of the actual current versus voltage curve of each PV generator. The practical impact of this need is also discussed in the paper. The feasibility and the performances of the proposed approach are experimentally validated by using a laboratory prototype. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

In this paper a computationally efficient optimization approach to the parametric identification of a fuel cell equivalent circuit model is presented. It is based on the inverse model and on machine learning regressions. During the training phase, the inverse model is built numerically by means of advanced regression approaches, i.e., the support vector machine regression, the least squares support vector machine regression and the Gaussian process regression. The training set is synthetically generated to the aim of exploring the parameter space and to characterize different stack operating conditions, including normal and faulty ones. The accuracy of the considered approaches is investigated by employing a test set including many experimental data, consisting of impedance spectra measured through the electrochemical impedance spectroscopy and referring to very different stack operating conditions. The results show that all the considered machine learning methods allow to identify the parameters of the fuel cell model with a low computational burden, so that they fit with the hardware resources of low cost embedded processors. This feature allows to envisage that the proposed approaches are good candidates for a model-based on-line diagnosis of fuel cell stacks. [Display omitted] • Parameter identification of equivalent circuit model performed through Inverse Model. • Parameter search space is first constrained in feasibility regions. • Inverse Models built through GPR, SVM and LS-SVM machine learning regression models. • The identification results are shown for both synthetic and experimental data. • Performance and Resource Usage of the embedded implementation are explored. [ABSTRACT FROM AUTHOR]

This paper introduces a novel maximum power point tracking (MPPT) technique aimed at maximizing the power produced by photovoltaic (PV) systems. The largest part of the MPPT approaches presented in the literature are based on the sensing of the PV generator voltage. On the contrary, in this paper, a current-based technique is proposed: the sensing of the current in the capacitor placed in parallel with the PV source is one of the innovative aspects of the proposal. A dual control technique based on an inner current loop plus an outer voltage loop allows to take profit of the fast current tracking capability of the inner current loop while the voltage loop benefits from the logarithmic dependency of the PV voltage on the irradiation level. The features of the proposed algorithm, particularly in terms of tracking of irradiation variations and disturbance rejection, are supported by theoretical analysis, simulations, and experimental results. The technique described in this paper is patent pending. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper a current controller is used to reject the 100-Hz oscillation of the voltage of the photovoltaic field taking place in any grid connected system. With respect to the classical linear voltage control the current-based sliding-mode approach ensures a wider regulation band but, unfortunately, in presence of a fast irradiance variation the sliding conditions might be violated, so that the operating point, which should be the maximum power point, might not be the expected one. In order to prevent such a drawback, an appropriate voltage compensation loop is used to interface the current-based sliding mode with the Maximum Power Point Tracking algorithm. Simulation and experimental results confirm the goodness of the proposed algorithm. [Copyright &y& Elsevier]

In this paper, a polymer-electrolyte-membrane fuel-cell (FC) model that is useful for simulation and control purposes is presented. The model uses both electrical-circuit components and functional blocks to reproduce both static and dynamic FC behaviors. Its main feature is in the reproduction of the oxygen-excess-ratio behavior, but it is also able to interact with any electrical device connected at the FC terminals, e.g., a load or a switching converter. Consequently, the proposed model can be used to develop new control strategies aimed at avoiding the oxygen-starvation effect and/or minimizing the fuel consumption. The model has been customized for a Ballard Nexa 1.2-kW power system, and this has allowed an experimental validation by means of measurements performed on a real FC device. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to design the output filter of low-voltage/high-current buck dc-dc switching regulators. The method is based on the concept of acceptability boundary curves (ABCs). ABCs are the curves bounding the regions in the space of parameters, including commercial components which ensure an acceptable output voltage ripple and a load-transient surge based on design specifications. ABCs help in quickly finding tradeoff design solutions, as well as in a better understanding of the functional correlations among power components. Examples regarding buck converters for point-of-load applications are discussed to highlight the flexibility and reliability of the ABC-based design method. Experimental measurements confirm the analytical results and numerical predictions. [ABSTRACT FROM AUTHOR]

The design of fourth-order dc-dc converters is discussed in this paper. The Principal Components Analysis is applied to find the set of passive components which allows to avoid double resonance in the control-to-output transfer function. The design of a synchronous SEPIC converter has been studied as an application of the proposed design methodology by using a database of real passive components. The performances of the SEPIC converter designed with the proposed methodology are verified through both time domain and frequency domain simulations and they are compared with those ones achieved by standard ripple-based design procedures. [ABSTRACT FROM AUTHOR]

In this paper, the design of the one-cycle controller of a single-stage inverter for photovoltaic applications is carried out by means of a multiobjective strategy to optimize inverter performance at both high and low insolation levels. Design constraints that account for different weather conditions are adopted. The optimization algorithm also provides useful information concerning the system sensitivity with respect to each of the controller parameters. This allows the design of a maximum power point tracking perturb and observe controller that significantly improves inverter performance. Experimental measurements confirm the predictions of theoretical and simulation results. [ABSTRACT FROM AUTHOR]

One of the major drawbacks of photovoltaic (PV) systems is represented by the effect of module mismatching and of partial shading of the PV field. Distributed maximum power point tracking (DMPPT) is a very promising technique that allows the increase of efficiency and reliability of such systems. Modeling and designing a PV system with DMPPT is remarkably more complex than implementing a standard MPPT technique. In this paper, a DMPPT system for PV arrays is proposed and analyzed. A dc and small-signal ac model is derived to analyze steady-state behavior, as well as dynamics and stability, of the whole system. Finally, simulation results are reported and discussed. [ABSTRACT FROM AUTHOR]

The article discusses papers published within the issue, including one on an advanced technique for maximum power point tracking of wind turbines and another on the concept of smart panel device.