Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions

Silvestre, S.; Kichou, Sofiane; Chouder, Aissa; Nofuentes, G.; Karatepe, Engin

doi:10.1016/j.energy.2015.03.123

Cited by 94 publications

(35 citation statements)

References 29 publications

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“…where α and β are the relationship between the ratios of current in case of one faulty string and fault-free operation and the ratio between the voltage ratios in case of one bypassed PV module in a string of the PV array and fault-free operation respectively (Silvestre et al, 2014(Silvestre et al, , 2015.…”

Section: Fault Detection Proceduresmentioning

confidence: 99%

“…Two more parameters can be also obtained from the model simulations in real time: I mpo and V mpo , the current and voltage at the maximum power point of the output of the PV array in absence of faults and normal operation of the PV array (Silvestre et al, 2014 (Silvestre et al, 2014) as well as inverter disconnection or partial shading conditions of work (Silvestre et al, 2015). These thresholds were defined by the following equations:…”

Section: Fault Detection Proceduresmentioning

confidence: 99%

“…In this work a procedure for automatic fault detection in grid connected PV systems is used. This procedure is based on a technique for the evaluation of current and voltage indicators recently reported that was experimentally validated and can work in real time without using sophisticated software tools (Silvestre et al, 2014(Silvestre et al, , 2015Chine et al, 2014). The integration of this fault detection procedure along with OPC monitoring, results in a powerful tool for automatic supervision and fault detection of grid connected PV systems.…”

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confidence: 99%

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Remote supervision and fault detection on OPC monitored PV systems

et al. 2016

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One of the main difficulties involved in monitoring systems is the inability to add new devices or new ways of evaluating the performance of these systems without significantly changing the topology of the monitoring system. Firstly, the incorporation of new devices, in the absence of standard communication protocols, requires the development of software for acquiring data from these devices and it is also necessary to add the functionality of each of the data that are acquired. Moreover, in photovoltaic (PV) plants connected to the grid each inverter has its own communication protocol and issues its own program online or locally to access data and plant information. These programs do not allow the inclusion of data from other inverters or for other plants even in the case of inverters from the same manufacturer. Also, it is not possible to AbstractThis paper presents a new approach for automatic supervision and remote fault detection of grid connected photovoltaic (PV) systems by means of OPC technology-based monitoring. The use of standard OPC for monitoring enables data acquisition from a set of devices that use different communication protocols as inverters or other electronic devices present in PV systems enabling universal connectivity and interoperability. Using the OPC standard allows promoting interoperation of software objects in distributed-heterogeneous environments and also allows incorporating in the system remote supervision and diagnosis for the evaluation of grid connected PV facilities. The supervision system analyses the monitored data and evaluates the expected behaviour of main parameters of the PV array: Output voltage, current and power. The monitored data and evaluated parameters are used by the fault detection procedure in order to identify possible faults present in the PV system. The methodology presented has been experimentally validated in the supervision of a grid connected PV system located in Spain. Results obtained show that the combination of OPC monitoring along with the supervision and fault detection procedure is a robust tool that can be very useful in the field of remote supervision and diagnosis of grid connected PV systems. The RMSE between real monitored data and results obtained from the modelling of the PV array were below 3.6% for all parameters even in cloudy days.

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Section: Fault Detection Proceduresmentioning

confidence: 99%

Section: Fault Detection Proceduresmentioning

confidence: 99%

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confidence: 99%

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Remote supervision and fault detection on OPC monitored PV systems

et al. 2016

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“…Nevertheless, these approaches regard single modules of PV plants and they are useful when a defect has been roughly individualized. When there is no information about the general operation of the PV plant, other techniques have to be considered, as neural networks [20], check of the electrical variables [21], AutoRegressive Integrated Moving Average (ARIMA) models [22], statistics, and so on. For example, in [23], the authors proposed a decision algorithm based on both the descriptive and inferential statistics.…”

Section: Introductionmentioning

confidence: 99%

Statistics to Detect Low-Intensity Anomalies in PV Systems

Vergura

Carpentieri

2017

Energies

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Abstract:The aim of this paper is the monitoring of the energy performance of Photovoltaic (PV) plants in order to detect the presence of low-intensity anomalies, before they become failures or faults. The approach is based on several statistical tools, which are applied iteratively as the data are acquired. At every loop, new data are added to the previous ones, and a proposed procedure is applied to the new dataset, therefore the analysis is carried out on cumulative data. In this way, it is possible to track some specific parameters and to monitor that identical arrays in the same operating conditions produce the same energy. The procedure is based on parametric (ANOVA) and non-parametric tests, and results effective in locating anomalies. Three cumulative case studies, based on a real operating PV plant, are analyzed.

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“…Nevertheless, these approaches regard single modules of the PV plants. When there is no information about the general operation of the PV plant, other techniques can be considered to prevent failures and to enhance the energy performance of the PV system, such as artificial neural networks [3,14], statistics [15][16][17], and checking the electrical variables [18][19][20]. More in detail, some of the PV fault detection algorithms are based on electrical circuit simulation of the PV generator [21,22], while other researchers use approaches based on the electrical signals [23,24].…”

Section: Introductionmentioning

confidence: 99%

Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters

Vergura

2018

Energies

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This paper deals with the monitoring of the performance of a photovoltaic plant, without using the environmental parameters such as the solar radiation and the temperature. The main idea is to statistically compare the energy performances of the arrays constituting the PV plant. In fact, the environmental conditions affect equally all the arrays of a small-medium-size PV plant, because the extension of the plant is limited, so any comparison between the energy distributions of identical arrays is independent of the solar radiation and the cell temperature, making the proposed methodology very effective for PV plants not equipped with a weather station, as it often happens for the PV plants located in urban contexts and having a nominal peak power in the 3÷50 kWp range, typically installed on the roof of a residential or industrial building. In this case, the costs of an advanced monitoring system based on the environmental data are not justified, consequently, the weather station is often also omitted. The proposed procedure guides the user through several inferential statistical tools that allow verifying whether the arrays have produced the same amount of energy or, alternatively, which is the worst array. The procedure is effective in detecting and locating abnormal operating conditions, before they become failures.

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Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions

Cited by 94 publications

References 29 publications

Remote supervision and fault detection on OPC monitored PV systems

Remote supervision and fault detection on OPC monitored PV systems

Statistics to Detect Low-Intensity Anomalies in PV Systems

Hypothesis Tests-Based Analysis for Anomaly Detection in Photovoltaic Systems in the Absence of Environmental Parameters

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