Simultaneous fault detection algorithm for grid-connected photovoltaic plants

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK

Effect of micro cracks on photovoltaic output power: case study based on real time long term data measurements

This study analyses the impact of micro cracks on photovoltaic (PV) module output power performance and energy production. Electroluminescence imaging technique was used to detect micro cracks affecting PV modules. The experiment was carried out on ten different PV modules installed at the University of Huddersfield, United Kingdom. The examined PV modules which contain micro cracks shows large loss in the output power comparing with the theoretical output power predictions, where the maximum power loss is equal to 80.73%. LabVIEW software was used to simulate the theoretical output power of the examined PV modules under real time long term data measurements

The impact of cracks on photovoltaic power performance

This paper demonstrates a statistical analysis approach, which uses T-test and F-test for identifying whether the crack has significant impact on the total amount of power generated by the photovoltaic (PV) modules. Electroluminescence (EL) measurements were performed for scanning possible faults in the examined PV modules. Virtual Instrumentation (VI) LabVIEW software was applied to simulate the theoretical IV and PV curves. The approach classified only 60% of cracks that significantly impacted the total amount of power generated by PV modules

Detecting Defective Bypass Diodes in Photovoltaic Modules using Mamdani Fuzzy Logic System

In this paper, the development of fault detection method for PV modules defective bypass diodes is presented. Bypass diodes are nowadays used in PV modules in order to enhance the output power production during partial shading conditions. However, there is lack of scientific research which demonstrates the detection of defective bypass diodes in PV systems. Thus, this paper propose a PV bypass diode fault detection classification based on Mamdani fuzzy logic system, which depends on the analysis of Vdrop, Voc , and Isc obtained from the I-V curve of the examined PV module. The fuzzy logic system depends on three inputs, namely percentage of voltage drop (PVD), percentage of open circuit voltage (POCV), and the percentage of short circuit current (PSCC). The proposed fuzzy system can detect up to 13 different faults associated with defective and non-defective bypass diodes. In addition, the proposed system was evaluated using two different PV modules under various defective bypass conditions. Finally, in order to investigate the variations of the PV module temperature during defective bypass diodes and partial shading conditions, i5 FLIR thermal camera was used

Multi-layer photovoltaic fault detection algorithm

This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function. The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system

Fault detection algorithm for multiple GCPV array configurations

In this paper, a fault detection algorithm for multiple grid-connected photovoltaic (GCPV) array configurations is introduced. For a given set of conditions such as solar irradiance and photovoltaic module temperature, a number of attributes such as power, voltage and current are calculated using a mathematical simulation model. Virtual instrumentation (VI) LabVIEW software is used to monitor the performance of the GCPV system and to simulate the theoretical I-V and P-V curves of the examined system. The fault detection algorithm is evaluated on multiple GCPV array configurations such as series, parallel and series-parallel array configuration. The fault detection algorithm has been validated using 1.98 kWp GCPV system installed at the University of Huddersfield. The results indicates that the algorithm is capable to detect multiple faults in the examined GCPV plant and can therefore be used in large GCPV installations

Output Power Enhancement for Hot Spotted Polycrystalline Photovoltaic Solar Cells

Hot spotting is a reliability problem in photovoltaic (PV) panels where a mismatched cell heats up significantly and degrades PV panel output power performance. High PV cell temperature due to hot spotting can damage the cell encapsulate and lead to second breakdown, where both cause permanent damage to the PV panel. Therefore, the development of two hot spot mitigation techniques are proposed using a simple and reliable method. PV hot spots in the examined PV system was inspected using FLIR i5 thermal imaging camera. Multiple experiments have been tested during various environmental conditions, where the PV module I-V curve was evaluated in each observed test to analyze the output power performance before and after the activation of the proposed hot spot mitigation techniques. One PV module affected by hot spot was tested. The output power during high irradiance levels is increased by approximate to 1.26 W after the activation of the first hot spot mitigation technique. However, the second mitigation technique guarantee an increase in the power up to 3.97 W. Additional test has been examined during partial shading condition. Both proposed techniques ensure a decrease in the shaded PV cell temperature, thus an increase in the PV output power

The impact of cracks on the performance of photovoltaic modules

This paper presents a statistical approach for identifying the significant impact of cracks on the output power performance of photovoltaic (PV) modules. Since there are a few statistical analysis of data for investigating the impact of cracks in PV modules in real-time long-term data measurements. Therefore, this paper will demonstrate a statistical approach which uses two statistical techniques: T-test and F-test. Electroluminescence (EL) method is used to scan possible cracks in the examined PV modules. Moreover, virtual instrumentation (VI) LabVIEW software is used to predict the theoretical output power performance of the examined PV modules based on the analysis of I-V and P-V curves. The statistical analysis approach has been validated using 45 polycrystalline PV modules at the University of Huddersfield, UK

Thermal impact on the performance ratio of photovoltaic systems : a case study of 8000 photovoltaic installations

Investigating the thermal impact including the fluctuations of the solar irradiance and ambient temperature of photovoltaic (PV) systems is a topic of great interest by industry and policymakers, due to the limited case studies reported so far by the PV research community. Therefore, this article presents the analysis of 8000 PV systems distributed across England using the well-known metric, monthly performance ratio (PR). The PV systems were operated over five years, while the PR is calculated using the newly developed model by the US national renewable energy laboratory (NREL). Remarkably, it was found that the average monthly PR for all examined PV systems is equal to 85.74%, where the Midlands region in the UK has the highest monthly PR of 88.12%. We have also investigated the seasonal thermal impact on the performance of PV systems, where it was concluded that Spring and Summer seasons intend to have higher monthly PR compared to Autumn and Winter. Finally, a detailed experiment of three different PV modules affected by various hotspots, including cell-based and string-based, will be comprehensively discussed

Estimating the impact of azimuth-angle variations on photovoltaic annual energy production

The performance of a photovoltaic (PV) installation is affected by its tilt and azimuth angles, because these parameters change the amount of solar energy absorbed by the surface of the PV modules. Therefore, this paper demonstrates the impact of the azimuth angle on the energy production of PV installations. Two different PV sites were studied, where the first comprises PV systems installed at –13°, –4°, +12° and +21° azimuth angles in different geographical locations, whereas the second PV site included adjacent PV systems installed at –87°, –32°, +2° and +17° azimuth angles. All the investigated PV sites were located in Huddersfield, UK. In summary, the results indicate that PV systems installed between –4° and +2° presented the maximum energy production over the last 4 years, while the worst energy generation were observed for the PV system installed at an azimuth angle of –87°. Finally, the probability projections for all observed azimuth angles datasets have been assessed. Since PV systems are affected by various environmental conditions such as fluctuations in the wind, humidity, solar irradiance and ambient temperature, ultimately, these factors would affect the annual energy generation of the PV installations. For that reason, we have analysed the disparities and the probability of the annual energy production for multiple PV systems installed at different azimuth angles ranging from –90° to +90° degrees, and affected by different environmental conditions. These analyses are based on the cumulative density function modelling technique as well as the normal distribution function