Silvano Vergura

Descriptive and Inferential Statistics for Supervising and Monitoring the Operation of PV Plants

This paper deals with the problem of supervising and monitoring a photovoltaic (PV) plant. First, an offline descriptive and inferential statistical procedure for evaluating the goodness of system performance is presented. Then, an online inferential algorithm for real-time monitoring and fault detection is introduced. The two methodologies utilize the energy output of inverters as input data and are valid for both Gaussian and non-normal distribution of data. The procedures have been tested on a real PV installation, and results are reported for the case of a grid-connected PV plant in Italy for which one PV module over 132 resulted in being badly connected.

A Finite-Element Approach to Analyze the Thermal Effect of Defects on Silicon-Based PV Cells

The paper introduces the issue of the typical defects in photovoltaic (PV) cells and focuses the attention on three specific defects: linear edge shunt, hole, and conductive intrusion. These defects are modeled by means of finite-element method and implemented in Comsol Multiphysics environment to analyze the temperature distribution in the whole defected PV cell. All the three typologies of silicon-based PV cells are considered: monocrystalline, polycrystalline, and amorphous. Numerical issues (simulation times, degrees of freedom, mesh elements, and grid dependence analysis) are reported.

Inferential statistics for monitoring and fault forecasting of PV plants

This paper proposes a procedure, based on both descriptive and inferential statistics for diagnosis of PV plants. This study aims to developing an algorithm able to recognize accurately among a degradation status and a system abnormality before a fault occurs. The statistical approach, based on the ANOVA and Kruskal-Wallis tests, is effective in locating abnormal operating conditions even in the presence of a reduced availability of energy measures. The proposed algorithm has been applied to a case study and advantages and limitations are presented.

Scalable model of PV cell in variable environment condition based on the manufacturer datasheet for circuit simulation

The paper, starting from the well-known five-parameters model, proposed a new mathematical model of a Photo-Voltaic (PV) cell which can be used also for modeling a PV module or a PV string in any environment condition. The proposed approach allows to write a new descriptive equation, whose terms are function of the information always available in the modern datasheet of the PV modules manufacturers. This implies that no pre-processing of the datasheets parameters is needed to use the proposed model, whichever are the solar irradiance and the cell/module temperature. Moreover, this model is interpreted from a circuital point of view, providing an electrical circuit constituted by basic electrical components. Particularly, in order to take into account the variability of the environment parameters, a variable resistor and several voltage-controlled sources are used.

Reference strings for statistical monitoring of the energy performance of photovoltaic fields

The paper deals with the issues to monitor the energy performance of Photo Voltaic (PV) fields by means of low cost hardware. In fact, the monitoring systems for low or medium rated power PV plants are often constituted by a limited number of sensors and low processing capacity. These systems allow a supervision of the PV fields when strong reductions of the produced energy happen, but they are ineffective to alert the end user about a gradual energy reduction. These issues are related to the natural ageing of PV modules, the dust or dirt accumulation on the PV modules, and so on. This paper proposes a methodology based on inferential tools, which return information about the correct operation of the PV field. The methodology needs an initial training that allows to define one or more reference strings, which will be used as benchmarks for future comparisons.

A GUI based analysis of infrared images of PV modules

The recent laboratory tests are showing progresses in both materials and production technology of Photo-Voltaic (PV) modules, nevertheless their efficiency yet results relatively low. Moreover, efficiency is also a very critical issue for already installed PV modules, because they were produced employing a less performing technology. In this scenario, a diagnostic approach able to check the current state of health of each single PV cell assumes a strategic importance. This paper introduces a thermography-based diagnostics able to provide a detailed, clear and unambiguous information, thanks to a computer aided investigation based on the InfraRed (IR) analysis: the proposed method, in fact, provides a fine diagnostics (both in numerical and in qualitative form) for each cell of the PV module. In addition, the paper proposes a software platform that implements the workflow and automatically generates a report, so resulting a fast and effective tool for the professionals of the Operation and Management (O&M).

On the photovoltaic explicit empirical model: Operations along the current-voltage curve

Modelling of PhotoVoltaic (PV) devices is today used for a number of activities such as systems monitoring, power forecasting, fault diagnosis, etc. Explicit empirical models based on the datasheet parameters are quite easy to implement in computer-aided calculations and therefore they allow fast evaluations of the electrical behavior, whatever is the test condition (steady-state, transient, etc.). This paper focus on the introduction of a correction factor for one of these models in order to improve its performance for operating points different than maximum power point (MPP). The comparison between the current-voltage (I-V) characteristics predicted by the former and the proposed models clearly shows the effectiveness of the proposed correction factors. The model is tested for different environment conditions and for different materials constituting the PV cells.

Transport issues and sustainable mobility in smart cities

This paper is part of a wider work- developed in the context of URBANETS project - Sustainable Management of Urban Networks with the Use of ICT- and of scientific research projects, mainly, on traffic and environmental requirements for Brindisi and Gallipoli. The paper is structured by considering different aspects having as an objective the satisfaction of transport knowledge needs with regard to Brindisi and Gallipoli, two significant Italian cities The aim is to supply policy indications in the light of population, economic operators and stakeholders exigencies. In the first part an analysis of territorial characteristics of Brindisi and Gallipoli, developed through a cluster analysis based on indicators referred to population, social aspects and economic elements, is presented. Further, the paper contains results stemming from a consultation process of citizens, tourists, public employees and stakeholders. Finally, indications on perspectives and final conclusions are presented. The authors believe that this paper can represent a useful tool for applied research, for those who operate in the transport sectors and for policymakers as well. It contributes to explain critical aspects of the transport sector, which are of great importance for citizens and for the economy of both the examined realities, and that can be extended to similar geographical sites.

Integration of a solar system in the greenhouse architecture

The paper deals with the study of the energy necessary to the greenhouse for the roses cultivation. Each cultivation requires specific micro-climate condition, then generic design is not an effective solution. In order to determine the best material to be used, a comparison has been made among three typologies, i.e. single glass, polycarbonate, and double glass. A solution with an integrated PV plant has been considered for the three cases, taking into account the energy needed but also a correct positioning that does not interface the natural growth of the cultivation.

Neural network-based diagnostics for PV plant

The energy performance of PhotoVoltaic (PV) fields has to be constantly monitored after setting a PV plant up. For this aim the paper proposes a Neural Network (NN)-based diagnostic methodology, able to detect the performance losses due to typical PV plants failure modes like ageing, dust deposition on modules glass, and so on. The NN has a feedforward architecture, with one input layer, one hidden layer and one output layer, and is trained through a supervised learning approach. In the training phase, the NN learns the correlation between the power produced by a PV string and environmental parameters such as modules temperature and solar radiation. Moreover, a dependency on the previous monitored operating point is given in order to provide the NN with a sort of dynamicity. Then, the main parameter evaluated is the error computed by the algorithm in the estimation of the cumulative sum of produced energy for a fixed number of successive time instants. This quantity is analyzed through a suitable statistical test so that, in the end, information on the operative state of the PV string can be extracted. The procedure has been tested on an operating PV plant and results confirm the goodness of the proposed approach.