Monitoring Of Photovoltaic Modules Research Articles

Automated detection and tracking of photovoltaic modules from 3D remote sensing data

This study addresses the growing demand for increased performance and reliability of photovoltaic (PV) installations by developing innovative monitoring technologies. The strategy consists of flying an unmanned aerial vehicle (UAV) equipped with a dual camera (RGB and thermal) over the PV plant of interest, followed by the generation of photogrammetric 3D models derived from the overlapped aerial images. The resulting datasets involve orthoimages and point clouds by processing RGB and thermal imagery. The key contribution of this study is twofold: (1) the thermal image mapping on dense and high-resolution point clouds that represent the status and geometry of PV solar modules, and (2) the automatic identification of individual solar panels in 3D space and their thermal characterization along their oriented surface. Then, the vector layer of each PV panel is projected onto the 3D thermal point cloud to extract the thermal values associated with each panel. To evaluate the capability of the proposed method, it was replicated in different scenarios, considering rural and urban environments with different light conditions and PV structures. The results demonstrate the robustness of our method, which achieves a remarkably high detection rate, around 99.12% of true positives, and a low false positive rate, close to 0.88%. Consequently, this method means an advance over previous work by proposing a comprehensive and automated solution for individual and highly detailed monitoring of each solar panel from 3D remotely sensed data. This study opens up new frontier research related to real-time monitoring of photovoltaic modules, an inspection of solar photovoltaic cells, the simulation of solar resources and forecasting, the development of digital twins, solar radiation modelling, and analysis of modular floating solar farms under wave motion.

PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network

Fast and reliable performance monitoring of photovoltaic modules is essential for economic forecasting in large-scale installations. Deep Learning methods, such as convolutional neural networks, have the potential to predict module power directly from electroluminescence images in an automated workflow. However, neural networks must be trained using large numbers of electroluminescence images of defective modules. Due to budgetary or technical limitations, these training images will always be biased, limiting generalization. Here, we demonstrate a transparent method to discriminate the information learned by a convolutional neural network into generally valid physics and bias. Learning of physics is assessed by providing an artificial, unbiased feature list, which is converted into synthetic electroluminescence images. Using these images, we compare the predictions of the neural network trained on the biased dataset to those of a physics-based equivalent. Bias is assessed by a closer look at the deviations between the predictions from the equivalent circuit model and the trained neural network. The assessment of physics knowledge incorporated by a Deep Learning method gives insight into how the method achieves its predictive capacity.

Photovoltaic single diode model parameter extraction by dI/dV-assisted deterministic method

In this work, a state-of-the-art deterministic method is proposed for photovoltaic single diode model parameter extraction from experimental current–voltage (I-V) curves. This new method takes advantage of the numerical derivative (dI/dV) information, such that a high-quality solution can be obtained by linear least squares technique. The solution can be further improved through a two-step nonlinear optimization procedure using the trust-region-reflective or Levenberg-Marquardt algorithm. Compared with previous methods, the proposed method has achieved the best accuracy with the lowest computation cost up till now in the benchmark test of two commonly-used case studies. When comparing the computation costs of different works, it is pointed out for the first time that not only the number of iteration steps but also the single-step computation complexity should be taken into account if different deterministic optimization algorithms are implemented. Furthermore, the robustness of the proposed method is verified through a large-scale dataset containing more than one million I-V curves, showing that the method can be used for realtime monitoring of photovoltaic modules. Finally, a MATLAB app is developed and freely provided to the public.

In situ quantification of temperature and strain within photovoltaic modules through optical sensing

AbstractThe ability to quantify internal strain levels within a photovoltaic (PV) laminate is essential to aid in the development of reliable and sustainable PV modules. This need is even greater for emerging applications with a high degree of integration such as vehicle and infrastructure integrated PV. Within this work, we demonstrate a scalable optical sensing solution, which allows for the in situ thermo‐mechanical strain and temperature monitoring of photovoltaic modules. Using a combination of two optic fibers with fiber Bragg gratings (FBGs) with a different packaging, an absolute in‐laminate temperature accuracy of 0.3°C has been achieved along with the ability to detect changes in strain as low as 0.248 μϵ. The sensor solution provides the potential to monitor and quantify various failure modes or degradation at various stages in the development process up to in‐field monitoring. Furthermore, it provides a platform for the direct validation of physics‐based simulations.

High-voltage DC converter for solar power station

In the article the circuit design solution of DC-DC regulated resonant converter has been proposed for using with hybrid photovoltaic modules which has cooling equipment and solar concentrators in order to maximize electric power generating by such module. By using computer simulation based on multiple iterations algorithm we significantly increase the accuracy of determining the resonance circuit optimal parameters for build up DC–DC converters to work in a wide range of electric powers. Based on optimal values of the resonance LLC scheme parameters, achived by numerical calculation it can be show high values of electrical energy transformation efficiency for photovoltaic energy station equipped with high efficiency hybrid photovoltaic modules. Implementation of microprocessor-based control into design of DC–DC back-boost converters create a new possibility to build control algorithms for increase reliability and conversion efficiency, rapid and precision stabilization of maximum power point, implementation network monitoring of photovoltaic modules, converters itself and the whole photovoltaic station parameters.

Edge Computing-Based ERBS Time Synchronization Algorithm in WSNs

In the practical application of large-scale photovoltaic module monitoring, adopting wireless sensor network (WSN) technology is a method worth researching. With increasing nodes in the wireless sensor network, widely existing clock skew, increased geometrically, is bringing about greater energy consumption. Due to the random distribution of nodes, in order to improve the transmission efficiency and reduce the computational load of the coordinator, the node processor needs to the use edge computing for preliminary analysis. This paper puts forward an improved energy-efficient reference broadcast synchronization algorithm (ERBS). This algorithm firstly calculates the average phase offset of nonadjacent nodes in the network after receiving a message. It then uses the least square method to solve the clock skew to achieve high-precision synchronization of the whole network. Simulation results show that compared with RBS, the time synchronization precision of ERBS is greatly improved and synchronization times are greatly reduced, decreasing energy consumption significantly.

Robust Detection, Classification and Localization of Defects in Large Photovoltaic Plants Based on Unmanned Aerial Vehicles and Infrared Thermography

The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10–-15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects.

Design and development of a wireless sensor network time synchronization system for photovoltaic module monitoring

Since the working situation of photovoltaic modules cannot be tracked in the real time and defective components cannot be located and controlled specifically, a remote monitoring system of photovoltaic modules based on wireless sensor networks is designed to improve the management efficiency of photovoltaic power plants. The monitoring data (current, voltage, and illumination, etc.) of photovoltaic modules are collected by the wireless sensor network nodes in real time, and then, there are orderly transmitted to the coordinator and server by the ZigBee communication protocol with the time synchronization algorithm. Time synchronization algorithm based on Gaussian delay model performs best in synchronization accuracy and energy-efficient reference broadcast synchronization algorithm is the most advantageous in synchronization energy consumption. Adaptive energy–efficient reference broadcasting synchronization algorithm is proposed with a balance between energy consumption and accuracy, which is applicable to large networks and thus conducive to the application of wireless sensor network in large-scale photovoltaic module monitoring. With the analysis and processing of the received data, the situation of the photovoltaic modules can be accordingly judged and thus the information management of photovoltaic power plant can be realized. For a photovoltaic power plant with a total power output of 100 kW and with 400 photovoltaic modules, experimental results show that the overall efficiency of the photovoltaic power plant can be increased by a wide margin.

Research on Adaptive Energy-Efficient Reference Broadcasting Synchronization

For large-scale photovoltaic module monitoring, wireless sensor network technology has been widely used, in which time synchronization is a significant factor. However, there are still outstanding issues in existing time synchronization algorithms for large-scale networking. To this end, this paper proposes a new Adaptive Energy-efficient Reference Broadcasting Synchronization method (AERBS) for wireless sensor networks, aiming to reduce the energy consumption whilst improving the synchronization precision. Moreover, the proposed method can reduce the number of synchronization information exchanges and self-adaptively determine the re-synchronization cycle. Simulation experiments demonstrate that our proposed AERBS outperforms the existing ERBS algorithm, in terms of reducing the synchronization number and the achieved reduction in energy consumption. The algorithm herein can be fully applied in the monitoring of large-scale photovoltaic module. At the same time, as a typical application of IoT (Internet of Things), it also provides a reference for distributed edge computing.

Analysis of Photovoltaic String Failure and Health Monitoring with Module Fault Identification

In this paper, photovoltaic (PV) string failure analysis and health monitoring of PV modules based on a low-cost self-powered wireless sensor network (WSN) are presented. Simple and effective fault detection and diagnosis method based on the real-time operating voltage of PV modules is proposed. The proposed method is verified using the developed health monitoring system which is installed in a grid-connected PV system. Each of the PV modules is monitored via WSN to detect any individual faulty module. The analysis of PV string failure includes several electrical fault scenarios and their impact on the PV string characteristics. The results show that a degraded or faulty module exhibits low operating voltage as compared to the normal module. The developed health monitoring system also includes a graphical user interface (GUI) program which graphically displays the real-time operating voltage of each module with colors and thus helping users to identify the faulty modules easily. The faulty modules identification approach is further validated using the PV module electroluminescence (EL) imaging system.

Photovoltaic Power Generation System Utilizing Temporary Error Compensation and Photovoltaic Array Failure Detection

Recently, as new and renewable energy has been actively supplied, numerous photovoltaic (PV) power generation facilities aimed at constructing zero-energy houses and reducing energy have rapidly developed. In this study, a monitoring device capable of continuously measuring the voltage and temperature data generated by individual PV modules was developed to determine whether a given module is abnormal. To examine the operating characteristics of the developed PV module monitoring device, a 3-kW PV power generation facility was built to conduct a comparative analysis of the changes in the voltage, current, and temperature between the normal group and the abnormal group. As a result, it was possible to determine whether each module was abnormal based on changes in the voltage and temperature measured using the developed monitoring device. Since a simply structured module monitoring device was used to determine whether a PV module was abnormal without using digital algorithms, it was expected that this monitoring device would be applicable to small-scale PV power generation facilities.

Solar hydrogen production via alkaline water electrolysis

Electricity generation via direct conversion of solar energy with zero carbon dioxide emission is essential from the aspect of energy supply security as well as from the aspect of environmental protection. Therefore, this paper presents a system for hydrogen production via water electrolysis using a 960 Wp solar power plant. The results obtained from the monitoring of photovoltaic modules mounted in pairs on a fixed, a single-axis and a dual-axis solar tracker were examined to determine if there is a possibility to couple them with an electrolyzer. Energy performance of each photovoltaic system was recorded and analyzed during a period of one year, and the data were monitored on an online software service. Estimated parameters, such as monthly solar irradiance, solar electricity production, optimal angle, monthly ambient temperature, and capacity factor were compared to the observed data. In order to get energy efficiency as high as possible, a novel alkaline electrolyzer of bipolar design was constructed. Its design and operating UI characteristic are described. The operating UI characteristics of photovoltaic modules were tuned to the electrolyzer operating UI characteristic to maximize production. The calculated hydrogen rate of production was 1.138 g per hour. During the study the system produced 1.234 MWh of energy, with calculated of 1.31 MWh , which could power 122 houses, and has offset 906 kg of carbon or an equivalent of 23 trees.

PV Module Monitoring System Based on Low-Cost Solutions: Wireless Raspberry Application and Assessment

The integration of renewables into power systems has led to multiple studies and analysis in terms of grid-power quality, reliability, and/or feasibility. Among different resources to be considered as alternative energy systems, wind and solar emerge as the most mature technologies. With regard to photovoltaic (PV) installations, monitoring problems requires detailed analysis, since solar-radiation fluctuations, soiling on solar panels, or deficiency of PV-panel performance can involve unexpected power-output oscillations and, subsequently, undesirable power-generation oscillations. Under this framework, this paper describes and assesses a wireless low-cost PV-module monitoring system based on open-source solutions. Our proposal allows us to monitor installations at the PV-module level, giving detailed information regarding PV power-plant performance. The proposed monitoring system is based on the IEC-61724 standard requirements, as a flexible and ad hoc solution with relevant connectivity options. Meteorological and electrical data are collected from the developed nodes and available for subsequent analysis. Detailed information of the solution, as well as extensive results collected in Spanish PV power plants connected to the grid, are also included in the paper.

Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization

The installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red monitoring of photovoltaic modules is a non-invasive inspection method which provides information of possible failures, by relating thermal behaviour of the modules to the operational status of solar panels. An adequate thermal measurement module strongly depends on the proper camera angle selection relative to panel’s surface, since reflections and external radiation sources are common causes of misleading results with the unnecessary maintenance work. In this work, we test a portable ground-based system capable of detecting and classifying hot-spots related to photovoltaic module failures. The system characterizes in 3D thermal information from the panels structure to detect and classify hot-spots. Unlike traditional systems, our proposal detects false hot-spots associated with people or device reflections, and from external radiation sources. Experimental results show that the proposed diagnostic approach can provide of an adequate thermal monitoring of photovoltaic modules and improve existing methods in 12% of effectiveness, with the corresponding financial impact.

A Research on Power Line Communication Based on Parallel Resonant Coupling Technology in PV Module Monitoring

Intelligent photovoltaic (PV) module monitoring can automatically locate the position of panels and monitor the status of PV modules, which is of great significance for PV plants operation and maintenance. As a new communication method in the field of PV monitoring, the monitoring of PV modules based on power line communication is the future trend of PV power plants monitoring due to its low maintenance cost and strong anti-interference ability. This paper presents an intelligent PV module monitoring scheme based on the parallel resonant coupling unit, which uses the dc bus as the communication channel and modula-tes the monitoring data into a high-frequency form to carry out the carrier communication. By using the parallel resonant coupling, the high-frequency carrier wave is injected into the dc bus, which improves the system efficiency and anti-interference ability. In addition, an adaptive level decision circuit is proposed to improve the flexibility and scalability of the communication system by adaptively changing the reference level of the decision circuit. Experimental results achieved by a developed power line transceiver demonstrate both feasibility and validity of the proposed scheme.

<italic>In-Situ</italic> Monitoring of Moisture Ingress in PV Modules Using Digital Humidity Sensors

A new in-situ moisture monitoring technique for photovoltaic (PV) modules is proposed using miniature digital humidity and temperature sensors. The sensors were embedded in three different ethylene-vinyl-acetate (EVA) stacks and proved to be resistant to lamination conditions. The fact that they are in direct contact to EVA does not affect their performance, since their saturated relative humidity (RH) reading is proportional to the external RH in the air. By exposing the sensors to elevated temperature and RH conditions, water vapor transmission rate of the backsheet and diffusion coefficient of the EVA can be determined. Obtained coefficients agree with reference values within their measurement uncertainties. Besides determining material moisture ingress properties, this monitoring technique is also applicable for long-term outdoor PV module monitoring. It shall provide valuable location and installation specific information of RH and temperature stress conditions, especially as feedback information to manufacturers of materials and PV modules.

Intercomparison of Temperature Sensors for Outdoor Monitoring of Photovoltaic Modules

Solar cells' temperature is a very important parameter that affects performance of photovoltaic (PV) modules since main electrical parameters of PV cells and modules are temperature dependent regardless the technology. The present study evaluates and compares different sensor types and mountings for long term outdoor temperature monitoring of PV modules along with a standardized method for determination of cell's temperature from open-circuit voltage. For that purpose, a special multicrystalline silicon PV module with miniature in situ Pt1000 temperature sensors was used for reference temperature measurement. On the back side of the PV module different temperature sensors were attached, including thermocouple (TC), platinum Pt1000 (PT) and digital temperature sensors DS18B20 (DS). All sensors except one were covered by a 1 cm thick insulation block. The whole setup was mounted on the outdoor PV testing site and all temperatures were monitored for several days with selection of different environmental conditions. On the basis of measurement results, deviations of different temperature sensors are investigated and compared to temperature calculated from open-circuit voltage measurement according to standard EN 60904-5. Among sensors attached at the back side, covered PT and TC sensors deliver the best results in range of 1–2 °C of lower temperature in average; while the covered DS sensor gives additional 1–2 °C underestimated temperature values. The worst measurement results demonstrate the PT sensor without insulation. All temperature sensors exhibit similar and adequate time response regarding the thermal capacitance of the PV module. DS sensors, although showing somewhat worse results, offer great advantages if several temperatures have to be acquired simultaneously and require very simple data acquisition equipment. They feature comparable measurement accuracy than commonly used Pt1000 temperature sensors if they are covered by insulation with 10 mm thick walls in lateral direction to avoid micro-environmental changes.

Passive and Active Thermographic Assessment as a Tool for Condition-Based Performance Monitoring of Photovoltaic Modules

Today, to ensure efficient operation of a photovoltaic (PV) system renders as an undoubtedly major concern. Toward this direction, scope of this work is the assessment of infrared thermography as a PV module’s condition monitoring and, subsequently, performance evaluation method. The idea is based on the fact that any abnormality to the temperature pattern of an under inspection equipment implies a dysfunction case. In particular, specific thermographic measurements were applied to a PV module with known, abnormally low performance. Following the basic processing of the acquired thermal images, the extracted temperature data were contrasted with the PV module’s expected operating temperature. The results validated the presence of a problematic solar cell that refers to a “hot spot” within the tested PV module. This paper concludes with a discussion regarding the advantages, the limitations, and, ultimately, the potentiality of the intended approaches as reliable condition monitoring method through performance evaluation of PV modules.