Percentage Of Power Loss Research Articles

Binary firefly algorithm based reconfiguration for maximum power extraction under partial shading and machine learning approach for fault detection in solar PV arrays

The functioning of a photovoltaic (PV) array in the shadow presents significant issues owing to power loss, which affects the harvested power. One of the greatest promising methods for reducing the effect of shadow over the array is reconfiguration. Therefore, this work proposes a modern metaheuristic Binary Firefly Algorithm (BFA) technique for solving the reconfiguration mechanism of a partial shade PV array. The proposed BFA is tested on a 9 × 9 panel PV array with four established shadow arrangements: short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The proposed BFA approach yields configurations superior when compared to existing reconfigurations. The best GMP boost obtained with the proposed BFA concerning TCT configuration is 36% in the SW shadow pattern, 30 in the LW arrangement, while the least is 7% in the SN and LN patterns. To evaluate the ability of the proposed system, it is compared with other reconfiguration methods tested across various performance metrics such as fill factor, mismatch power loss, percentage of power loss, and power enhancement. The energy estimates and revenue generation demonstrate that the proposed BFA approach generates 15% more power than the TCT setup and other methods. Furthermore, the Nave Bayes based Machine Learning (ML) approach is applied to detect physical degradation in PV panels. To validate the performance proposed ML method and other techniques are undergone with both faulty and non-faulty conditions. The output of the proposed ML technique-based identification of faults approach is compared to existing techniques. The acquired results supported the proposed BFA with ML's ability and superiority in optimally reconfiguring the shaded array.

DC Cabling of Large-Scale Photovoltaic Power Plants

The development of Floating Solar Photovoltaic (FPV) systems is a sign of a promising future in the Renewable Energy field. Numerous solar modules and inverters are mounted on large-scale floating platforms. It is important to design the system so that the inverter operates in its optimum range most of the time. In order to achieve this goal on the DC side, serial and parallel connections of solar modules are used. As a result, the cabling of the PV array architecture is an important issue. Modern electrical installation design requires reducing costs in cabling materials, equipment installation, and maintenance. The reduction of losses and the amount of time required to complete the design are also significant. Therefore, the main topic of this paper is DC cabling in large-scale FPV power plants (>1 MV). The serial-parallel (SP) connection scheme of solar modules and the percentage of power loss in DC cables are considered. Furthermore, a general method for determining cable lengths for FPV power plants is defined. The temperature influence on losses in DC cables is analyzed. A new method for determining the current at the maximum power point (MPP) as a function of temperature is proposed. A case study is conducted using a hypothetical 3 MW FPV power plant, and the obtained results are presented and analyzed.

Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM

For lifelong and reliable operation, advanced solar photovoltaic (PV) equipment is designed to minimize the faults. Irrespectively, the panel degradation makes the fault inevitable. Thus, the quick detection and classification of panel degradation is pivotal. Among various problems that promote panel degradation, hot spots and micro-cracks are the prominent reliability problems which affect the PV performance. When these types of faults occur in a solar cell, the panel gets heated up and it reduces the power generation hence its efficiency considerably. In this study, the effect of the hotspot is studied and a comparative fault detection method is proposed to detect different PV modules affected by micro-cracks and hotspots. The classification process is accomplished by utilizing Feed Forward Back Propagation Neural Network technique and Support Vector Machine (SVM) techniques. The investigation of both the techniques permits a complete analysis of choosing an effective technique in terms of accuracy outcome. Six input parameters like percentage of power loss (PPL), Open-circuit voltage (VOC), Short circuit current (ISC), Irradiance (IRR), Panel temperature and Internal impedance (Z) are accounted to detect the faults. Experimental investigation and simulations using MATLAB are carried out to detect five categories of faulty and healthy panels. Both methods exhibited a promising result with an average accuracy of 87% for feed-forward back propagation neural network and 99% SVM technique which exposes the potential of this proposed technique.

Parallel power extraction technique for maximizing the output of solar PV array

Solar energy is one of the clean energy sources which does not causes any pollution to the environment. The photovoltaic system will play a vital role in the world’s power production for the energy demand in future. Partial shading is a major concern have to be considered while installing the PV system. The effect of shaded panel will affect the performance of the non-shaded PV modules that leads to the mismatch losses. This work proposes a new technique to overcome the partial shading effect and also to enhance the total power generation from the PV array. The mismatch losses in the PV system is nullified in the proposed technique, by operating each row of the PV array with even current generation by extracting mismatch power from each row. The proposed technique is experimented in the 3 × 3 Total Cross Tied (TCT) PV array with nine numbers of 10 W PV modules. The experimental setup has been validated under the all possible shading patterns which shows that, the proposed technique nullifies the mismatch losses and also the performance has been compared with the other existing methods. The Percentage Power Loss (PLL) is greatly reduced in the proposed technique. The comparative analysis has been presented for eight shading patterns in the result and discussion in terms of Mismatch Loss (ML), Percentage of Power Loss (PPL) and Fill Factor (FF).

A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array

Harvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 ×9,6×20,16×16, and 25 × 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization–reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization–photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values across the independent runs.

Photovoltaic Array Reconfiguration System for Maximizing the Harvested Power Using Population-Based Algorithms

Massive infiltration of photovoltaic (PV) systems into electric supply networks creates numerous challenges in the present era, as the PV systems become an alternative to non-renewable energy resources. Partial shading, nevertheless, is an essential problem which affects the productivity and life of PV plants. PV reconfiguration is known as a powerful technique to resolve this effect. It is achieved by rearranging the PV modules according to their temperature and levels of shade. Therefore, in this paper, we have utilized three simple population-based optimization algorithms that are known as the flow regime algorithm (FRA), the social mimic optimization algorithm (SMO), and the Rao optimization algorithm to dynamically restructure the PV array. The effectiveness of the proposed algorithms is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the results obtained are compared with a regular total-cross-tied (TCT) connection and recently published techniques such as the competence square (CS) and genetic algorithm (GA). Furthermore, to demonstrate the suitability of proposed approaches in real-time implementation, real-time irradiation data of a particular location are considered and fed into the proposed algorithms for effective shade dispersion. After successful shade dispersion, the total energy generated using the three proposed algorithms is calculated and compared with the TCT reconfigured system for one year. The presented energy calculations and revenue generation confirm that the power produced by the proposed FRA technique is 13% higher than that generated by the TCT configuration. Furthermore, the presented PV characteristics show a reduced number of multiple peaks in the system. Thus, the proposed FRA technique can be endorsed as a technique that is superior to other existing methods.

A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System

Large-scale solar photovoltaic (PV) plants play an essential role in providing the increasing demand for energy in recent time. Therefore, in the purpose of achieving the highest harvested power under the partial shading conditions as well as protecting the PV array from the hot-spot calamity, the PV reconfiguration strategy is established as an efficient procedure. This is performed by redistribution of PV modules according to their levels of shading. Motivated by this, the authors in this article have introduced a novel population-based algorithm that is known as marine predators algorithm (MPA) to restructure the PV array dynamically. Moreover, a novel objective function is introduced to enhance the algorithm performance rather than utilizing the regular weighted objective function in the literature. The effectiveness of the proposed algorithms based on the novel objective function is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the obtained results are compared with a regular total-cross-tied (TCT) connection, manta ray foraging optimization (MRFO), harris hawk optimizer (HHO) and particle swarm optimizer (PSO) based reconfiguration techniques. Furthermore, to demonstrate the suitability of the proposed methods, large scale PV arrays of $16\times16$ and $25\times25$ are considered and evaluated. The results reveal that MPA enhanced the PV array power by percentage of 28.6 %, 2.7 % and 5.7 % in cases of $9\times9$ , $16\times16$ and $25\times25$ PV arrays, respectively. The comprehensive comparisons endorse that MPA shows a successful shade dispersion; hence the number of multiple peaks in the PV characteristics has reduced, and high values of power have been harvested with least mean execution time in comparison with PSO, HHO and MRFO. Moreover, the Wilcoxon signed-rank test has been accomplished to confirm the reliability and applicability of the proposed approach for the PV large scale arrays as well.

Photovoltaic Hot-Spots Fault Detection Algorithm Using Fuzzy Systems

Faults in photovoltaic (PV) modules, which might result in energy loss and reliability problems are often difficult to avoid, and certainty need to be detected. One of the major reliability problems affecting PV modules is hot-spotting, where a cell or group of cells heats up significantly compared to adjacent solar cells, hence decreasing the optimum power generated. In this article, we propose a fault detection of PV hot-spots based on the analysis of 2580 PV modules affected by different types of hot-spots, where these PV modules are operated under various environmental conditions, distributed across the U.K. The fault detection model comprises a fuzzy inference system (FIS) using Mamdani-type fuzzy controller including three input parameters, namely, percentage of power loss (PPL), short circuit current ( $\text{I}_{\mathrm{ sc}}$ ), and open circuit voltage ( $\text{V}_{\mathrm{ oc}}$ ). In order to test the effectiveness of the proposed algorithm, extensive simulation and experimental-based tests have been carried out; while the average obtained accuracy is equal to 96.7%.

Novel Photovoltaic Hot-Spotting Fault Detection Algorithm

In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80%.

Evaluating Power Loss and Performance Ratio of Hot-Spotted Photovoltaic Modules

The impact of photovoltaic (PV) hot-spots is assessed through the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. PV hot-spots were categorized into eight different groups using the percentage of power loss. All hot-spots groups were modeled using the cumulative density function, state-of-the-art geographical mapping, and performance ratio (PR) analysis. Significantly, it was found that 92.15% of the PV modules affected by hot-spotted PV string are located in northern U.K., where the effect of low-temperature levels, heavy snow, and hoarfrost are more significant. Finally, it was found that the distribution of PV modules affected by only one hot-spotted solar cell are likely (82.41%) located in coastal locations. Hence, coastal locations expect to have lower risks for causing multiple hot-spotted solar cells in PV modules, compared to central and colder locations. The PR of all examined PV modules was analyzed. It was evident that the mean PR is significantly reduced due to the existence of hot-spots in the PV modules. The least difference in the PR between healthy and hot-spotted PV modules is equal to −0.83%, whereas the most difference is calculated at −15.47%.

Laser power loss through polystyrene plates for cell culture

Dear Editor, In 2009, we published a paper about in vitro release of fibroblast growth factors by human gingival fibroblasts [1]. The methodology for laser irradiation in cell cultures is to apply laser on the bottom of the 96-well plate avoiding the light transmission through the growth medium. Ideally the light would be delivered directly to the cell monolayer. The cell growth plates are made of polystyrene with a 1-mm width on the bottom. In that study, we used TPP plates (Trasadingen, Switzerland), and according to manufacturers, it has excellent optical characteristics, suitable for precise optical measurements, mainly on bottom reading. Challenged by this information, we investigated the interference of this polystyrene layer on low-level laser transmission. We cut a disk from the 96-well plate and introduced in a power meter (Laser Check power meter, Coherent, Inc., Santa Clara, CA, USA) to verify the output power of the laser equipment (MMOptics, Sao Carlos, Brazil). These measurements were compared to others made without the disk and we found interesting results. For visible red laser set in 40 mW, the measurements were 44.1 and 38.8 mW, without and with the disk, respectively. It showed a reduction of power of 12 %. For infrared laser set in 40 mW, the measurements were 40.0 and 35.3 mW, without and with the disk, respectively. This also revealed a 12 % loss of power. These differences were statistically significant (p<0.05). We consider that this information is fundamental to studies with laser and culture cells. The percentage of power loss must be considered and added to the final power settings in the device.

Design of a Two Layer Microwave Absorber

A design procedure for a microwave absorber is presented. The absorber consists of two lossy planar layers on a conducting plate. One of the layers is magnetically lossy while the other is electrically lossy. With a prescribed percentage of power loss in each layer and a partial knowledge of the layer parameters, a complete design procedure is outlined. It is shown that the bandwidth over which the reflection loss is above a certain limit depends on the ratio of power loss in the two layers. It is shown that high reflection loss can be achieved over frequency ranges as high as 3:1 ratio.

Partial shadowing protection without diodes

Abstract Partial shadowing on a photovoltaic module causes serious power loss in the photovoltaic module output. The percentage power loss is generally much larger than the percentage of the shadowed area of the module. A model has been developed — taking into account both the series and shunt resistances of the solar cell — to obtain a relationship between the percentage of power loss and percentage of shadowed area. The results obtained showed that the shunt resistance of the solar cells plays the major role in determining the relation between power loss and shadowed area of the module. An approximate formula for the power loss as a function of both shadowed area and shunt resistance is given. It is also proved that the power loss is determined by the total shadowed area of the module, regardless of the number of shadowed cells, i.e. if two cells are 50% shadowed or a single cell is totally shadowed, the same power loss results. In this work an alternative solution for the partial shadowing problem is tried. The concept is based on connecting the solar cells in the same module in a parallel series configuration, instead of the conventional series connection. The power loss of such modules is proved to be less. It is also shown that the number of series-connected cells does not influence the power loss due to partial shadowing as long as the percentage of shadowed area is kept constant.