Performance Of Solar Panel Research Articles

Energy Efficiency and Economic Survivance Appraisal of a 375 kWp Rooftop Solar PV System Under Hot and Dry Indian Climate

ABSTRACTIndia's rooftop solar photovoltaic (PV) installations are experiencing rapid growth due to favorable regulations. As climate change becomes a growing concern, researchers are turning their attention to the effects of weather patterns on the performance of rooftop solar panels, and also to optimize their efficiency in a changing environment. Consequently, industry players in the solar sector have been conducting performance validation and feasibility assessments of these plants. A 375 kWp rooftop PV plant is studied as a case example from April 1, 2022 to March 31, 2023, generating 543,666 kWh annually for the grid. The NMBE and MBE were assessed using simulation tools like PVGIS and PV Watts. In addition, a cost–benefit analysis of carbon credits was conducted with and without their inclusion. The energy payback time is calculated at 4.5 years post‐inclusion. Over a 25‐year lifespan, the embodied energy of the PV plant amounts to 2,552,265 kWh. This plant can mitigate CO2 emissions annually by 10,173.57 tons which is equivalent to INR 5,464,925. The current study highlights both environmental and economic benefits by incorporating carbon credits into the project. Further advancement in simulation tools, PV technologies, climate change adaptations are expected which will improve the rooftop system efficiency with shorten pay pabck periods and maximum reductions in CO2 emissions.

Automatic cleaning suggestion adapting to real-time soiling status of solar farms

Solar farms are subject to various environmental factors that can affect their performance and output. One of the major factors is soiling. Consequently, it is essential to monitor and mitigate the effects of soiling on solar panels to ensure optimal performance and maximum profitability of a solar farm. In this paper, we present a novel cleaning intervention strategy that adapts to the present solar panel performance status to detect cleaning requirements for maximizing profit. This algorithm works with onsite insolation and panel data to give live feedback on cleaning requirements. Moreover, unlike other models in the literature, this method does not require historical or a priori insolation, weather, or soiling data. We have comprehensively tested the algorithm through experiment and simulation under various conditions (i.e., seasonal rain, few annual rainfall or sandstorms, unpredictable dust accumulation). The results show that the adaptive cleaning algorithm, even with no historical insolation or soiling information, outperforms optimal periodic cleaning strategies in terms of net revenue. The adaptive cleaning suggestion system shows significant advantages especially in locations with high variability in weather and soiling conditions. The algorithm is also suitable for practical deployment in solar farms near industrial or construction zones where sudden dust accumulation can occur. Our adaptive model will maximize profit in such unpredictable soiling scenarios, while in comparison, it is not even possible to utilize historical information-based cleaning cycle model in such cases.

PENGARUH KECERAHAN TERHADAP ENERGI LISTRIK YANG DIHASILKAN OLEH PANEL SURYA

The reduction in fossil energy production, particularly petroleum, along with the global commitment to reducing greenhouse gas emissions, encourages the government to continuously improve the role of new and renewable energy to maintain energy security and independence. Renewable energy is a” source of energy derived from nature that can be used freely, continuously renewed, and unlimited, such as“solar energy. This study aims to analyze the performance of solar panels” in terms of battery charging and discharging duration, current, voltage, and power under sunny and cloudy conditions. The research method was conducted directly in the field, using a solar panel with a capacity of 10WP (Watt Peak) as the study object. The results showed that the performance of solar panels under sunny conditions achieved the highest average power of 4.81 Watts and the fastest battery charging time of 6 hours. Under cloudy conditions, the average power obtained was 4.34 Watts, and the battery charging time was 6 hours and 20 minutes. The battery discharging duration for a 5-watt lamp was 16 hours and 8 minutes, for a 10-watt lamp, it was 8 hours and 4 minutes, and for a 15-watt lamp, it was 5 hours and 6 minutes.

Evaluation of self-cleaning mechanisms for improving performance of roof-mounted solar PV panels: A comparative study.

Solar panel installation is generally exposed to dust. Therefore, soiling on the surface of the solar panels significantly reduces the effectiveness of solar panels. Accumulation of dust also shortens their lifespan and reduces efficiency by about 15% to 20%. A significant reduction in the efficiency of solar photovoltaic panels has been observed due to inadequate insulation and dust deposition or shading. To harness maximum solar energy from solar panels up to their rated capacity, they need to be cleaned periodically. Therefore, the current study focuses on the comparative performance analysis of two distinct types of self-cleaning mechanisms, namely self-cleaning wiper (SCW) and nano-coating method. These methods are economical and sustainable for the standard atmospheric conditions of Pakistan. Solar panels (reference, nano-coated, and self-cleaning wiper mechanism) were placed on the roof of the Mechanical Engineering Department MUST Mirpur AJK for five weeks. Solar irradiance, dust density & performance parameters of these three panels were recorded on weekly basis. It was observed that an increase in the rate of dust deposition negatively affects the conversion efficiency of solar panels. When dust density was increased from 7.5 to 18.15 (g/m2), the percentile drops in rated power (50W) for reference, nano-coated, and self-cleaning wiper mechanisms are 37%, 33% and 23%, respectively. Moreover, the payback period of nano-coated and SCW is 1.07 years and 2.79 years, respectively.

Advancing Solar Power Efficiency: Innovations in Material Science and System Optimization for Enhanced Solar Energy Conversion

This study aims to advance solar power efficiency through innovations in material science, surface engineering, and system optimization. The research integrates experimental testing and computational modeling to assess improvements in energy conversion efficiency. The methodology includes testing photovoltaic materials such as perovskite and multi-junction cells, anti-reflective coatings, self-cleaning surfaces, and optimizing panel orientation, cooling systems, and Maximum Power Point Tracking (MPPT) algorithms. Perovskite cells demonstrated a 22% increase in efficiency, while multi-junction cells achieved up to 35% efficiency. Anti-reflective coatings and self-cleaning surfaces improved energy capture by 4% and 7%, respectively. System optimization, including dual-axis trackers and AI-enhanced MPPT algorithms, provided additional efficiency gains of up to 25% and 10%, respectively. These findings demonstrate significant advancements in solar panel performance, providing a clear path for enhancing scalability and commercial viability in diverse environmental conditions. The integration of innovative materials and system optimizations presents a promising solution for making solar energy more sustainable and cost-effective. Future research should focus on improving the durability and reducing the costs of these technologies for widespread adoption.

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Phthalocyanine and metal phthalocyanine are hole transport buffer layers for perovskite solar cell fabrication

This approach allows you to more accurately evaluate the performance of solar panels and identify any prob- lems or degradation in their operation. The LUMO value of mPc closer to the LUMO value of CH3NH3I3PbClx increased conversion energy and short circuit current density (Jsc), which reached a maxi- mum value of 15.97 mA/cm² using the HTL layer of CuPc, and the open circuit voltage (Voc) reached a max- imum at 0.97 V. The change in Jsc corresponds to the fill factor (FF) change. The filling factor (FF) reached a maximum value of 67.35 % when using the HTL layer of CuPc and a minimum value (FF) of 54.23 % when using the HTL layer of H2Pc. The lowest series resistance (R1) and interface resistance (R3) of 11.2 and 39.9 Ohms, respectively, were shown with the HTL layer of CuPc, Capacitive element CPE1 — 305 pF, Ca- pacitive element CPE2 — 0.95 pF. CPE (Constant Phase Element) is an element used in equivalent circuits to describe the non-ideal dielectric properties of materials. The decrease in CPE1 may indicate a decrease in the heterogeneity of the dielectric properties of the perovskite material after coating with different HTL mPc lay- ers. This may be due to changes in structure or mutual actions between layers as a result of exposure to dif- ferences in energy levels.

Enhancing Reliability and Regeneration of Single Passivated Emitter Rear Contact Solar Cell Modules through Alternating Current Power Application to Mitigate Light and Elevated Temperature‐Induced Degradation

The study explores a novel method to combat the Light and Elevated Temperature‐Induced Degradation (LeTID) in solar cell modules, which significantly reduces their efficiency and lifespan. This method involves applying alternating current (AC) of various waveforms (triangular, sinusoidal, and square) and frequencies (5 and 100 kHz) to boron‐doped p‐type passivated emitter rear contact (p‐PERC) solar cell modules. This approach effectively lowers the series resistance at the critical junction between the silver (Ag) contact and the silicon emitter layer of the PERC solar cell, thereby reducing charge recombination hindered by high resistance, especially at elevated temperatures. As a result, there is an improved flow of electrical charges, leading to decreased energy loss and increased solar cell efficiency. The study's findings indicate that a slow, smooth sinusoidal AC waveform at 100 kHz is particularly effective, restoring about 100% of the original performance of the panel. Moreover, oscillations at 5 kHz also show considerable efficacy, recovering more than 96% of the performance. The sinusoidal waveform is noted to surpass both triangular and square waveforms in recovery efficiency. This research highlights the use of high‐frequency AC electricity as a viable strategy to extend the lifespan and enhance the performance of solar panels.

Assessing the Features of PV System’s Data and the Soiling Effects on PV System’s Performance Based on the Field Data

This paper assesses the features/characteristics of a photovoltaic system’s data, investigates the relationship between the soiling and solar panel performance, and leverages real-world data obtained from a solar site in Shams Solar Facility located at the German University of Technology in Oman. Through an experimental approach, different parameters were scrutinized to unravel the dynamics at play. Due to the lack of studies on how to assess the features of a PV System’s data, and in order to model the PV System’s data, extensive analyses were conducted based on a big dataset containing 36,851 observations of each parameter (environmental factors) of the study. In addition, diverse environmental factors, operational conditions, and the collected data were analyzed by various mathematical/statistical measures, and inferential statistical measures were applied to obtain accurate and significant results that explain the level of each parameter (environmental factors), and are developed to examine the features/characteristics and performance of PV Systems and reveal the influence of soiling accumulation on the energy output. The research findings do not only deepen the understanding of the features of PV Systems data and the impact of soiling on solar panels, but also underscore the significance of considering geographical and climatic variations. This research contributes significantly to advancing knowledge within the realm of solar energy systems and provides actionable insights for optimizing the performance and reliability of PV installations in real-world settings. The discussion, conclusions, limitations, and future directions have been discussed.

Assessing the performance of a monocrystalline solar panel under different tropical climatic conditions in Cameroon using artificial neural network

To implement the European Union (EU)-Africa Green Energy Initiative in Cameroon to boost the renewable energy sector, we model the performance of a 500 W monocrystalline solar panel in major cities of Cameroon located in different climatic zones to select the best location for the installation of a solar farm. We also evaluate the contribution of seasonal and weather variability to the amount and stability of power generated by the panel using the artificial neural network (ANN). The ANN model was used to train and test the ERA5 hourly data for Bamenda. The model was then used to estimate Photovoltaic (PV) output in Douala, Yaounde, Ngaoundere, Garoua, and Maroua with a mean absolute error of 4.109 × 10−5, 4.699 × 10−5, 3.563 × 10−5, 3.106 × 10−5, and 3.083 × 10−5 kW, respectively. The results show that the ANN can capture the influence of weather variability on the generated output power. Cloud cover and rainfall are found to negatively affect the amount and stability of generated power in the lower latitude cities of Douala and Yaounde compared to the northern cities, with these effects being stronger in the rainy season than in the dry season. Garoua followed by Maroua are proving to be the best locations for installing a solar park in terms of the amount and stability of electricity generated throughout the year. The Cameroonian government, its EU partners, and other stakeholders involved in the development of solar energy in the country will be able to use the results of this study for better decision-making.

Smart Solar Photovoltaic Panel Dust Monitoring System Using Internet of Thing (IoT)

Photovoltaic technology has become one of the most popular ways of generating power from renewable energy sources. Solar panels are solely responsible for generating the maximum amount of electricity. One of the reasons to be rectified is the lack of equipment or systematic monitoring of solar panels. Due to their surface being covered with dust and not being cleaned for a long time, solar panels can get damaged and become unable to generate electricity. This project investigates the factors of dust that impact photovoltaic module performance and designs an intelligent monitoring system. The project aims to develop a monitoring system that allows users to use their smartphones to monitor the cleanliness of solar panels. This monitoring kit includes a dust sensor component as data input and a NodeMCU ESP32 microcontroller that receives, processes, and sends data to the Blynk application. The project enables the owners to monitor their solar panels in real-time. The results show that solar panel performance has decreased by 61% when the reading value of dust sensor 1 is 47%, dust sensor 2 is 30%, dust sensor 3 is 53%, and dust sensor 4 is 50%. The benefits of this project are that solar panel maintenance will be more systematic, ensuring solar panels can generate the maximum electricity, and they are not easily damaged, thus increasing the lifespan of solar panels.

IoT-Enabled energy conservation in residential Buildings: Machine learning models for analyzing annual solar power consumption

This study investigates enhancing the self-consumption of solar panels by integrating thermal and electrical energy storage using lithium-ion batteries in a duplex building over a year. Machine learning algorithms are applied to understand energy usage patterns. An IoT-connected model is introduced to reduce electricity expenses by boosting consumption with solar energy and extending solar panel lifespan through optimization. The model includes lithium-ion batteries for energy storage and a thermal cooling system to regulate panel temperatures. Data collected from sensors monitoring occupancy, temperature, humidity, energy, and thermostats are sent to a cloud-based platform via IoT. Results show peak monthly energy conservation measures of 41.6 kW and 28 kW during summer for the two residences. Winter witnesses a reduction of 13.9 % and 7 % in energy consumption, while autumn sees an 18 % decrease for one residence and stability for the other. Real-time IoT monitoring aids model training, with the Gaussian Probability model emerging as the most accurate. Optimization analysis underscores the thermal cooling system’s efficacy in improving solar panel performance and reducing electricity costs. Integration of machine learning and IoT curtails energy consumption to 22 kWh and 15.7 kWh for the respective residences, offering inhabitants free energy from renewable sources and ensuring swift recovery of solar panel installation costs.

Comprehensive study on zeolitepolyester composite coated sheet for eco-friendly solar panels for enhanced panel performance and reduced panel temperature

Solar energy is the most promising source for generating residential, commercial, and industrial electricity. However, solar panels should be eco-friendly to increase sustainability during manufacturing and recycling. This study investigates the potential of using natural fibre composites as eco-friendly alternatives to conventional polyethylene terephthalate (PET) back sheets in solar panels. Furthermore, it examines the performance of sisal fibres coated with zeolite-polyester resin. The chemical composition, structural integrity, and crystalline properties of the composites were evaluated through extensive microstructural analysis. The results from the experimental analysis revealed significant improvements in voltage (8%) and current (6%) for the coated sisal fibre panels compared to conventional panels. Power output increased by 12%, and overall efficiency improved from 9.75 to 10.8%. Solar panels with sisal fibre sheets exhibit adequate tensile strength and impact resistance and reduce operating temperature by 2–3 °C, ensuring stable operation and minimizing heat loss. Statistical analysis confirmed the reliability and significance of these results. The life cycle analysis demonstrated a 60% reduction in CO2 emissions and a 50% decrease in energy consumption during the production, utilization and disposal of sisal fibre sheets. These findings underscore the viability of natural fibre composites in enhancing the performance and sustainability of solar panels.

Design and implementation of a dual-axis sun tracker for an Arduino-based micro-controller photovoltaic system

This research investigates the optimization of solar panel performance by designing and implementing a low-cost Dual-Axis Sun Tracker (DAST) for an Arduino-based Microcontroller Photovoltaic System. The primary aim is to enhance solar energy extraction by precisely aligning the panel perpendicular to the sun's position, maximizing output voltage and current efficiency compared to fixed systems. The DAST employs two micro servo motors, SG90, controlled by a specialized chronological algorithm in offline mode, ensuring strategic alignment and scheduled adjustments. A comprehensive evaluation of the DAST's performance is conducted, contrasting it with a Fixed System to underscore the advantages of solar tracking. As a result, a DAST produces higher output than a fixed system by 0.896W during sunny days and 0.206W during cloudy days. Besides, the efficiency of PV panels in the DAST is 71.65%, and fixed is 49.66% during sunny days, while DAST is 22.96% and fixed is 17.91% during cloudy days.

Investigating the influence of nanofluid on photovoltaic-thermal systems concerning photovoltaic panel performance

Solar energy has much promise to replace the planet's finite supply of fossil fuels as it is sustainable and eco-friendly. Photovoltaic (PV) panels are one example of the proper technology that may transform solar energy into electrical energy. On the other hand, high temperatures may reduce photovoltaic cells' ability to produce power efficiently. As a result, a study was carried out to use the PV/T (Photovoltaic Thermal) collector system and evaluate the effects of CuO, TiO2, and Al2O3 nanofluids as cooling agents on the performance and operating temperature of PV panels. In Surakarta, Indonesia, an experimental study was conducted with nanoparticle concentrations of 0.2 vol% and a flow rate of 3 L/m at an intensity of radiation of 1000 W/m2. The results showed that, in comparison to uncooled PV panels, employing CuO, TiO2, and Al2O3 nanofluids in the PV/T system resulted in temperature reductions of 18.84 °C, 18.12 °C, and 17.62 °C, respectively. Moreover, measurements of the electrical efficiency produced by PV and PV/T panels using CuO, TiO2, and Al2O3 nanofluids showed that the respective values were 10.98%, 13.92%, 13.65%, and 13.39%. This study contributes to the development of solar energy technology by demonstrating the potential of nanofluid-based cooling devices to improve solar panel performance in hot conditions.

ANALISIS PERBANDINGAN KINERJA SISTEM PENDINGIN PV DENGAN CAIRAN AQUADES, AIR SUMUR, DAN TANPA PENDINGIN DENGAN MONITORING IOT

Solar Power Plants are an environmentally friendly energy source that utilizes sunlight energy which is converted into electricity. However, solar panels must maintain their performance. One of the impacts is that solar panels cannot work optimally if the temperature of the solar panels exceeds the safe temperature. This research examines the factors that cause the performance of solar panels to not work optimally, when the surface temperature of the solar panels is too hot and exceeds the safe temperature limit, then tools are needed to help the solar panels so that the working temperature is not too high. The technology used to control the temperature of solar panels is by utilizing IoT by maximizing the DS18B20 sensor and voltage sensor, to read the surface temperature of the panel, which will then wet the surface of the solar panel with distilled water and well water automatically if the surface temperature of the panel exceeds the safe temperature on the solar panel. And later you can monitor the temperature of the panel with a smartphone and also observe whether distilled water and well water can reduce the temperature on the surface of the panel. When collecting data, it can be compared to when without using cooling, the temperature on the panel is above 33 degrees. Meanwhile, when using a cooler the temperature obtained decreases. The well water cooler produces a temperature of 32 degrees, while the distilled water cooler produces a temperature of 30.5 degrees. It can be concluded that distilled water can reduce the temperature of the panel, so it can make the panel work optimally. So, in order to maximize the performance of the panels to utilize solar power, in this research, distilled water is very good for lowering the temperature on the surface of the panels.

Investigation on the operation performance of solar panels cooled by a natural draft cooling system

This paper is to investigate the feasibility of a natural draft cooling system for PV panel cooling in a typical hot and dry region (Hami, Xinjiang, China). A 3-D model of PV panels cooled by a natural draft cooling system is established using Fluent 2020R2 software, and the cooling effect of the natural draft cooling system on PV panels is simulated and analyzed based on the meteorological data of Hami, China. Simulation finds that the natural draft cooling system can reduce the average temperature of the PV panels. The minimum temperature drops of the PV panels in January (represents winter season), April (represents spring season), July (represents summer season) and October (represents autumn season) of the year 2020 are 1.7 °C, 3.1 °C, 6.1 °C and 2.5 °C, respectively; while the maximum temperature drops of the PV panels are 7.0 °C, 11.2 °C, 15.5 °C and 6.3 °C, respectively. Besides, the natural draft cooling system is also compared with the PV panels without cooling system in six typical days, and finds that the PV panel with a natural draft cooling system increases the photoelectric conversion efficiency by up to 1.41 % (i.e., from 13.71 % to 15.12 %) when compared with the PV panel without cooling. Therefore, the proposed natural draft cooling system can effectively reduce the temperature of PV panels in Hami, Xinjiang.

An Experimental Assessment of the Performance of Solar Panels Using Efficiency Enhancement Techniques in India's Tropical Region

<p style="text-align:justify"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="color:black">Due to its abundance and lack of pollution, solar energy will soon be the most popular source of electricity production. Numerous factors, including the temperature effect, solar irradiance effect, shadow effect, incident angle effect, dust accumulation on the solar cell, shunt resistance, types of solar cell materials, mechanical defects in the solar panel, spectral mismatch loss, cable loss, maintenance and cleaning cycle, load mismatching effect, etc., harm the efficiency of solar P.V. power generation systems. This study aims to quantify the impact of these factors and explore efficiency enhancement techniques to mitigate their effects. Among the various factors, solar irradiation and temperature significantly influence solar P.V. panel efficiency. This research investigates the effects of solar irradiation, incident angle, and temperature through simulations and experimental methods using two 100W solar panels. Efficiency enhancement techniques like sensor-based sun tracking and temperature cooling systems are incorporated with conventional solar P.V. power generation systems. The experimental results prove that the efficiency enhancement techniques improved the efficiency of solar panels by up to 12% compared to conventional solar panels.</span></span></span></span></span></p>

Economic Optimization and Performance Enhancement of Rooftop Solar Power Systems Using Concentrated Solar Technology and Advanced Materials

Abstract: This research investigates the economic optimization and performance enhancement of rooftop solar power systems through the integration of concentrated solar technology and advanced materials. The aim is to assess the viability and effectiveness of these innovations in both residential and commercial settings. By focusing on improving energy efficiency and reducing costs, the study provides a comprehensive economic perspective on the adoption of these advanced solar technologies. Key aspects of the research include the evaluation of lifecycle costs, energy yield, and payback periods, offering insights into the long-term financial benefits and feasibility of implementing such systems. The integration of concentrated solar technology enhances the intensity of solar energy captured, thereby significantly boosting the efficiency of rooftop solar panels. Advanced materials, such as perovskites, multi-junction cells, and nanomaterials, are examined for their potential to improve energy absorption, durability, and overall performance of solar panels. The research employs simulation software to model the performance of these optimized systems under various environmental conditions, providing a detailed analysis of their energy output and efficiency.