The I-V characteristic curve of solar cells under different temperature.

The I-V characteristic curve of solar cells under different temperature.

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In order to shorten the maintenance time and make sure of the photovoltaic (PV) power generation system steadily in operation, a fault diagnosis system for photovoltaic power generation system was proposed in this paper. First, a PSIM software is used to simulate a 2.2 kW PV system, it can take the operating date of the PV system under different su...

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... Indeed, in addition to being directly dependent on the irradiance, the production of a photovoltaic system also depends on ambient temperature and wind speed and direction. According to the type of PV cell used, the cell temperature (which depends on the incident irradiance, air temperature and wind) modifies the voltage and current intensity that the cell produces when absorbing the energy from the sun's rays [WAN12]. In most cases, an increase in cell temperature results in a decrease in voltage, an increase in current to a lesser extent and consequently to a decrease in power output. ...
... In most cases, the link between ambient temperature and the maximum output power of the module is mentioned in the datasheet using the PV cell temperature coefficient (linked to the power), generally in [%/°C] 2 . In addition to influencing the output power of the module, the ambient temperature also affects the voltage of the PV module in an almost linear way [GYS20] [WAN12]. Indirectly, the ambient temperature, therefore, affects the performance of the inverter (by changing the DC voltage and power). ...
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L’Université Libre de Bruxelles (ULB) is composed of various university campuses where energy consumption is significant. In order to be more and more autonomous in terms of energy consumption and to reduce its carbon footprint, ULB has invested in solar photovoltaic. In 2018, ULB installed 1690 kWp of solar panels and thirty solar inverters on its three different campuses. From that moment, continuous monitoring of the photovoltaic (PV) installations is carried out. Recently, ULB has noticed that some of its inverters experiencing high operating temperatures. ULB intends to undertake appropriate actions in case these high inverter temperatures have an impact on the production of these installations. In this regard, the objective of this master thesis is to study the PV installations of ULB and investigate whether the operating temperature of the solar inverters has an impact on their performance. In order to investigate the effect of the temperature of the inverter on its performance, a performance indicator is calculated, the performance ratio (PR). The PR is used in this work because it is a very powerful tool to describe the correct operation of photovoltaic systems. It has many advantages, it is relatively simple to compute and it allows the comparison between different installations. To obtain the PR, a transposition model is applied to the daily horizontal irradiation and the powers at five-minute granularity are aggregated. Once the PR is obtained, a correction is made to account for losses in the photovoltaic cells due to the temperature. This results in an improved PR, the PRSTC. The next step of the methodology is the creation of a fault detection tool based on the PR and PRSTC. Using the mean absolute deviation, the standard deviation is estimated and a confidence interval around the PR and PRSTC is created. The last step of the methodology is the analysis of the PR and PRSTC with the inverter temperature in summer, the season characterised by the highest inverter temperatures recorded. The results of the performance ratios analysis show that the solar inverters characterised by the highest temperature have also a relatively high PR and PRSTC compared to other ULB solar inverters. The inverters that record the highest temperature are also those with the highest installed peak power and annual production. The analysis of the performance ratios also indicates that the PRSTC remains relatively constant as the inverter temperature rises except for the SolarEdge SE25K. The latter, which is the only solar inverter of ULB with forced cooling, has its PRSTC slightly reduced when the temperature rises significantly. Lastly, the application of the fault detection method to the daily PR and PRSTC shows that the solar inverters with the highest temperatures do not present significant faulty operations. Moreover, most inverter malfunctions are detected in winter when the inverter temperature is at its minimum. Finally, this master thesis concludes that the temperature of the solar inverter has no significant effect on its performance. The high temperatures of the solar inverters are the consequence of the high power load of these inverters rather than the cause of a malfunction of the latter.
... Various statistical features could be applied in the identification of DC arc faults, including entropy, RMS value, standard deviation, mean, and the highest value of the input signal. Basing on time-domain analysis, this approach is present in [60][61][62][63][64][65][66][67][68]. Although the rate of changes in the loop current in the time domain was suggested in [63] to indicate the arc fault event, it would receive the impact of random spike disturbance. ...
... Although the rate of changes in the loop current in the time domain was suggested in [63] to indicate the arc fault event, it would receive the impact of random spike disturbance. It is shown in [61] and [64] that the contrast between the highest and lowest value of current within a certain duration is identified as an indicator. Despite the simplicity of the approach, it is relatively efficacious in identifying arc fault, particularly in the initial phase. ...
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... In general, PV system defects cause energy losses [27], [28]. This study developed an energy prediction system that predicts the energy generated by the PV generator to detect and eliminate faults. ...
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... The faults of PV modules are varied, unpredictable and heavily influenced by external factors, and thus difficult to be diagnosed [9][10][11][12]. Fortunately, the neural network (NN) can characterize the relations between the states and causes of PV faults with structures, connection weights and thresholds [13]. ...
... Based on a matter-element model and extension correlation function, an extension diagnosis method of steam turbine vibration was established by Wang [18], which was applied to the vibration fault diagnosis of an automobile generator set. Wang and Chen [19] applied the extension theory to a fault diagnosis system of a photovoltaic power system, a two-phase diagnosis system based on this theory was put forward. In addition, the extension theory and multiple optimization fusion theory was applied to diagnose transformer fault types by Shao [20]. ...
... Using (19), calculate the extension distance between the test sample and each cluster. ...
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... PV generator (PVG) is commonly in front of many defects, because of its direct outdoor exposition, which could relatively affect its reliability and cause a power losses, or PV cells damaging which makes an uncomfortable PV module substitution notably for the big PV installation buildings. Fault diagnosis procedure is useful to reduce the anomalies effect or at least ensure the prevision of cell cracking.The recent years, several fault diagnosis techniques were proposed in the literature with different approaches [4][5][6][7][8][9]. Power losses evaluation without using sophistical tools approach is the well utilized, either by using traditional or analytical procedure investigation [4][5][6], or based on modern intelligent techniques [7][8][9]. ...
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... On the other hand, the accuracy of failure judgment is increased by grouping some of PV modules by using a new voltage and current detection method [5]. In [16], a two stage fault diagnosis method based on the extension theory is proposed [16]. In another study [17], Bayesian network is built to intelligently reason about potential causes of faults. ...
... On the other hand, the accuracy of failure judgment is increased by grouping some of PV modules by using a new voltage and current detection method [5]. In [16], a two stage fault diagnosis method based on the extension theory is proposed [16]. In another study [17], Bayesian network is built to intelligently reason about potential causes of faults. ...
... Experiment results (N.U: non-uniform, NF: no-fault, SC: short circuit, and OC: open circuit).16.7% for Case-3.2 ...
Chapter
The technological advancements and lower energy costs have provided a smooth pathway for solar photovoltaic (PV) technology to grow as one of the leading renewable energy resources in the twenty-first century. However, for long-term operations, the reliability of a PV module is compromised throughout its lifetime as a result of various degradation mechanisms. It is necessary to address the issue of degradation in order to accurately assess the power declination with time as well as to overcome the financial losses. This manuscript provides a detailed review of the major degradation processes acting on the PV cells or modules which gradually diminish their power generation capabilities and result in lower output. The main causes of these deteriorating effects and the extent to which these exploit various performance characteristics of PV modules have also been discussed. This paper also gives a short overview of detection techniques used for visualization of defects in PV modules. Discoloration, delamination and corrosion are the most dominating modes of PV module degradation, while light-induced degradation (LID) can affect the module in its early stages. High ambient temperature, moisture and UV radiations strongly enhance the possibility of this phenomenon to occur. Thus, in order to have long-term operations, time-to-time monitoring and maintenance of modules are recommended.
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