Performance Of Solar Energy Systems Research Articles

Performance Analysis of Solar Photovoltaic (PV)-Thermoelectric (TEG) Hybrid Energy System for Electricity Generation

The solar spectrum responsible for PV operation comprises the ultra-violent, visible and invisible radiation. The increase in cell temperature due to the invisible radiation in the solar spectrum reduces the solar PV performance. This research investigated the performance of solar PV-TEG hybrid energy system for energy optimization in comparison with simple solar PV system. The solar PV-TEG hybrid energy system is one on which thermoelectric generators (TEG) were couple with PV module for conversion of the excess heat energy due to rise in cell temperature to electrical energy. The output parameters of both systems such as; short circuit currents open circuit voltages and PV cell temperature were measured with respect to incident solar radiation. The power production and efficiencies for the simple PV and hybrid PV-TEG systems were evaluated and compared, The results revealed that at maximum value of 920 W/m2 average hourly sun radiations, 57.9 W useful PV power input was simultaneously utilized by the simple PV and hybrid PV-TEG systems. The operating temperatures of the simple PV and the hybrid PV-TEG systems varies from 64.5°C to 58.9°C which is 9.94%, reduction. The simple PV and hybrid PV-TEG short circuit currents and open circuit voltages varies with values (17.8 to 20.13 V) and (0.56 to 0.57 A), which have increased by 1.78% and 14.1% respectively. However, with respective values 10.14 W and 11.58 W of power outputs an increase of 14.5 % was realized. These enable enhancing respective 17.5 % and 20 % energy efficiencies. The overall power output and efficiency of the hybrid PV-TEG energy system reported were 11.67 W and 20.2% which showed increased by 15.09 % and 15.43% respectively compared to the simple PV system. Moreover, the statistical analysis conducted reported quality of the measured results and inequality of the respective pairs of output values with P-values of 0.000 in each case. Therefore, integrating PV module with TEG enables reduction .......

A universal tool for estimating monthly solar radiation on tilted surfaces from horizontal measurements: A machine learning approach

In this study, a universal method for estimating monthly solar radiation on tilted surfaces from horizontal measurements was developed and validated, targeting the optimal design and performance of solar energy systems. Using data from the Photovoltaic Geographical Information System (PVGIS) across 740 global locations, three machine learning models—linear regression, random forest, and k-nearest neighbors (KNN)—along with a multilayer perceptron deep learning model, were evaluated. The dataset spanned tilt angles from 0° to 90° at 1° increments and 11,511,773 rows with six input features for predicting irradiance on any tilt angle. The KNN model was selected for further testing analysis due to its superior accuracy, achieving the lowest root mean square error (RMSE) of 1.42 kWh/m2 and mean absolute error (MAE) of 0.7 kWh/m2 on the validation dataset. Testing was conducted with 16 years of data from five locations not included in the training dataset, obtained from PVGIS and Solcast. Results show that the KNN model consistently outperformed the traditional isotropic model in four out of five cities using PVGIS data and in two using Solcast data, especially excelling in regions with stable climates and consistent irradiance profiles. Moreover, better prediction performance was observed with PVGIS data compared to Solcast data across all five cities, with PVGIS data yielding an RMSE of 3.03 kWh/m2 and an MAE of 2.41 kWh/m2, while Solcast data exhibited a higher RMSE of 5.22 kWh/m2 and MAE of 4.14 kWh/m2. Finally, these results advocate for a shift towards data-driven methodologies highlighting their enhanced reliability.

Smart Solar PV Monitoring System Using IoT

In the era of modernization, the Internet of Things (IoT) emerges as a pivotal technology, particularly in enhancing the performance and monitoring of solar energy systems. By integrating IoT into solar plants, real-time supervision becomes feasible, allowing users to monitor crucial parameters such as current, voltage, and light intensity. This monitoring system addresses the challenges associated with remote location monitoring, making it especially relevant for off-grid solar home systems in remote areas, including coastal communities. The IoT-based monitoring system enables users to remotely track the performance of solar panels and receive alerts for abnormal conditions such as panel breakage or excessive dust accumulation. Through mobile connectivity, users can access real-time data and receive warning messages in case of anomalies, facilitating efficient fault detection and maintenance. The proposed solution aims to prototype this IoT-enabled monitoring system, offering a cost-effective and scalable approach to monitoring and maintaining solar plants in remote locations. Key Words: Internet of Things (IoT), Solar Energy, light, Smart Solar, Temperature, Remote areas.

Impact of dust particles on the output power of photovoltaic modules in Calabar, Cross River State

During the dry season, dust particles on photovoltaic modules (PV modules) are a regular occurrence in Calabar, Cross River State. This dusty condition or environment is caused by the migration of dust particles from the Sahara region via the east-west wind. The concentration of dust on PV modules has an impact on the overall performance of solar energy systems. This research looks into the effect of dust particles on the output power of solar modules. This investigation was carried out by subjecting 80-watt monocrystalline PV modules to an outdoor experiment and analyzing the changes in current and voltage from the control and dusty PV modules with a digital multimeter. Other environmental factors such as relative humidity, ambient temperature, and solar radiation were also measured with a hygrometer, thermometer, and solar power meter respectively. Appropriate formulars were employed to calculate output power and efficiency. Dust particles on the surface of PV modules dramatically reduces the quantity of sunlight that penetrate the panel by 21.10%, resulting in a drop in the system's overall output power. The study emphasizes on the importance of frequent cleaning and maintenance of PV systems.

Implementasi Sensor LDR Pada Prototipe Sistem Tracking Dual Axis Untuk Deteksi Arah Sinar Matahari Pada Sel Surya

Solar energy is a potential solution to meet the electricity needs of TNI AD posts and bases in border areas. However, conventional solar energy systems have limited efficiency because they can only absorb sunlight from one direction. The efficiency of solar energy systems can be improved by using a dual-axis tracking system equipped with a LDR sensor. The LDR sensor is used to detect the direction of sunlight. The change in the LDR sensor resistance due to the change in the intensity of light that hits it is then processed by a microcontroller to move the solar panel in the appropriate direction. Based on the results of testing, the dual-axis tracking system with an LDR sensor significantly increases the power generated compared to conventional solar energy systems. At the peak of performance during dynamic testing at 12:00 pm, the total power reached 2,196 W, while the static test reached a total power of 1,311 W. Therefore, the dual-axis tracking system with an LDR sensor can be a more optimal choice to improve the performance and efficiency of solar energy systems in generating electricity at TNI AD posts and bases in border areas.

Sensible heat thermal energy storage performance of mono and blended nanofluids in a free convective-radiation inclined system

The photothermal conversion performance (PCP) of a nanofluid-based solar energy system is investigated numerically and experimentally. The impacts of particle size, volume concentration, nanoparticle type, base fluid type and collector inclination angle on the PCP are investigated. It is observed that using nanoparticles improves the ability to absorb solar energy. Temperature gain is 2.2, 3.2, 3.8, 4.2, and 9 times better than pure water for water-based Al, Al2O3, Au, Cu, and Graphite mono nanofluids, respectively. Interestingly, blended nanofluids containing the same nanoparticles significantly argument the optical properties, with useful heat enhancement ranging from 62.8 (mono) to 194 kJ/kg (hybrid). However, the collector's inclination angle, which ranges from 0 to 60°, has a negative impact on the PCP by reducing the solar radiation absorption of the nanofluids due to a decrease in the radiation penetrating the collector. Furthermore, as nanoparticle size increases, so does the thermal performance of the nanofluid. An experimental investigation is carried out for pure water and nanofluid at various wind speeds and solar irradiation levels ranging from 1 to 4 m/s and 200–1000 W/m2, respectively, to validate the numerical results.

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.

Design and development of data acquisition system (DAS) for panel characterization in PV energy systems

Data acquisition systems are commonly used to monitor the power generation performance and efficiency of solar energy systems. The main purpose of this study is to monitor the PVs used by mounting on the outer surface of rapidly growing green buildings from a single point and to intervene when necessary. The proposed prototype aims to both characterize the panel performance and quickly detect the failed PV panel by using a single low-cost control box and cloud based open-source software. With this system, the separate current–voltage-power output parameters of all panels and characterization curves can be automatically obtained and at the same time, all environmental conditions (radiation, UV, temperature, wind speed and direction, pressure, humidity, rain, dust and PV panel temperature) affecting the PV modules can be simultaneously recorded.The software used in the developed system is open source, free of charge and user-friendly..

Advanced exergy analysis of a solar-driven cascade ejector system with heat storage

Solar-driven cascade ejector system can not only promote the utilization reliability of the solar energy, but also improve the performance of solar energy systems. This paper proposes a novel solar-driven cascade ejector system which integrates a solar ejector heat storage subsystem with R290 as refrigerant and a transcritical CO2 ejector-expansion refrigeration subsystem. The conventional and advanced exergy analysis methods are utilized to investigate the system’s exergetic performance, and explore the interactions among the system components and their improvement potential. The effects of compressor efficiency, area ratio and efficiency of ejectors on the exergetic performance of system and components are investigated. The analysis results show that the transcritical CO2 ejector-expansion refrigeration subsystem has higher exergy efficiency and its components have small exogenous exergy destruction. The endogenous exergy destruction and the avoided exergy destruction of cascade system account for 88.85% and 41.46%, respectively. Besides, 66.35% of exogenous exergy destruction of middle heat exchanger is caused by the compressor and CO2 ejector, and to improve the performance of CO2 ejector and CO2 evaporator would decrease the exergy destruction of compressor. CO2 ejector and R290 ejector have the superior improvement potential in each subsystem compared to other components. The rise of ejector area ratio would increase the endogenous exergy destruction of ejector. For the system with high-performance compressor, R290 ejector efficiency should be attracted more attention to reduce its avoided endogenous exergy destruction.

Effects of staggered dimple array under different flow conditions for enhancing cooling performance of solar systems

Enhancing cooling performance of internal channel with dimple array is promising technology for various solar energy systems. In this study, we investigate the use of dimple arrays within these coolant channels as a means of achieving higher cooling performance while minimizing pressure loss. Experiments were conducted to evaluate the effects of dimple arrays under various flow conditions (laminar, transition, and turbulent). Flow measurements revealed that the dimple array promotes secondary flows and turbulent intensity, particularly under transitional flow conditions. Among the tested flow conditions, the highest thermal performance ratio of 1.8 was achieved at ReH = 2000 and 3000 (transition flow cases), resulting in a 47% increase in heat transfer compared to the laminar flow case (ReH = 1000). These results suggest that dimple arrays with transitional flow conditions could be an effective means of improving the cooling performance of solar energy systems and ultimately contribute to reducing carbon dioxide emissions and preventing climate change. Further research is needed to optimize the dimple array design for specific flow conditions cooling applications for solar energy systems.

Computational study of Cattaneo–Christov heat flux on cylindrical surfaces using CNT hybrid nanofluids: A solar-powered ship implementation

This study examines the potential of using nanofluids in solar thermal energy systems. Nanofluids are known to exhibit high convection heat transfer coefficients, low specific heat, and density, making them ideal for improving the performance of solar thermal energy systems. However, this computational study investigates the application of Cattaneo-Christov heat flux on cylindrical surfaces using carbon nanotube (CNT) hybrid nanofluids, for use in a solar-powered ship. The work utilizes numerical simulations to analyze the heat transfer and fluid flow characteristics of the hybrid nanofluids. The research examines the use of single-walled and multi-walled carbon nanotubes (SWCNT and MWCNT) in engine oil (EO) as the working fluid. The Galerkin weighted residual method (GWRM) is utilized to solve the ordinary differential equations (ODEs) governing the system. The impact of various parameters, such as Cattaneo-Christov heat flux, solar thermal radiation, nonlinear stretching surface, slippery velocity, and porous media on the velocity equation, energy equation, and entropy generation are investigated and elaborated through detailed plots. The findings show that the MWCNT-SWCNT/EO hybrid nanofluid (HNF) exhibits maximum efficiency of around 2.4%, while the minimum efficiency is at 2.7%. This research provides valuable insights into the design and optimization of solar thermal systems for sustainable transportation.

EXPERIMENTAL ANALYSIS OF THE EFFECT OF NANOFLUID USE ON POWER AND EFFICIENCY ENHANCEMENT IN HEAT PIPE SOLAR COLLECTORS AND MODELING USING ARTIFICIAL NEURAL NETWORKS

Solar energy systems have significant advantages over traditional energy production methods, but improvements are needed to improve performance and efficiency. In this study, the effect of the use of nanofluids on power and efficiency values in a heat pipe solar collector was analyzed using experimental and artificial intelligence approaches. A heat pipe solar collector was fabricated and the effects of prepared water-based Al<sub>2</sub>O<sub>3</sub> and TiO<sub>2</sub> nanofluids on power and efficiency values were experimentally investigated. Using the obtained experimental data, an artificial neural network model has been developed to predict power and efficiency values. The values obtained from the network model were compared with the experimental data and the prediction performance of the network model was extensively examined using various performance parameters. The coefficient of performance value for the neural network model was calculated as 0.99332 and the mean squared error value was calculated as 2.77E-03. The study findings revealed that the use of nanofluids in the heat pipe solar collector improves the power and efficiency values. It has also been seen as a result of the study that the developed artificial neural network model can predict power and efficiency values with deviation rates lower than 0.48%.

ARDUINO BASED SMART SOLAR PHOTOVOLTAIC REMOTE MONITORING SYSTEM

The continuous monitoring system of the photo voltaic solar based energy model is currently highly crucial to monitor the overall performance and other related characteristics of energy solar energy systems from remote locations. For this, data loggers also play a vital role. These technologies are highly important to identify any malfunctions inherent in PV based solar energy systems. The proposed IoT based system is useful to obtain real time data from PV based solar energy systems from remote areas. An Arduino ATMEGA16 microcontroller was used to acquire real time data from the system, which is incorporated with the blynk app to transmit real time data to the destination through a webpage. The proposed system consists of a PV based solar system, Arduino controller board Wi-Fi module and the blynk app, in order obtain and transmit data from any remote location. We have developed a cost effective IoT based Smart monitoring system for PV based solar energy applications to monitor the various characteristics and performance of the system, as well as to carry out preventive maintenance and fault detection.

Fixed and adjusted optimal tilt angle of solar panels in three cities in Albania

In this paper, we are concerned about optimal tilt angle choice of solar panels installation in three cities in Albania. The optimal tilt angle of solar panels is a crucial parameter for a high performance of solar energy systems. Solar energy systems can be tracking systems or fixed systems, fixed tilt angle determination and adjustable tilt angle evaluation of solar panels is an important issue to solve. In this study, Bernard-Menguy-Schwartz (BMS) model is used to determine the yearly and seasonally optimum tilt angle of the solar panels in three cities in Albania. The determination of the optimum tilt angle is made considering the fact that the solar energy reaching the solar panels has maximum value for optimum tilt angle. The yearly optimum tilt angle of the solar panels for the cities: Tirana (41.3275° N, 19.8187° E), Kuksi (42.0807° N, 20.4143° E) and Vlora (40.4661° N, 19.4914° E) are respectively 38o, 39o and 37o. It should be noted that, the yearly optimal tilt angle of solar panels based on the above mathematical model is almost equal to the latitude of the location. A suggestion of seasonably adjustable tilt angle is made. In addition, seasonally or adjusted tilt angles of solar panels were compared with yearly optimal tilt angle.

A Closer Look at the Environmental Impact of Solar and Wind Energy.

Moving towards a sustainable society implies constant improvement in the way energy is supplied and consumed, with wider implementation of solar and wind energy facilities in stand‐alone or hybrid configurations. The goal of this work is to evaluate the lifecycle performance (construction and operation‐related impact) of large‐scale solar and wind energy systems and to compare it with conventional coal and natural gas fossil fuel plants under similar conditions. Environmental analyses of energy conversion systems today usually neglect the construction‐related environmental impact of fossil fuel plants, because it is significantly smaller than the impact related to the operation of the plant. However, the construction of large‐scale renewable plants implies the use of rare materials, transport‐related emissions, and other environmentally impactful activities. The plants evaluated here are configured and compared for similar emissions and similar power output. It is found that the life‐cycle environmental impact of the renewable plants could, in some specific cases, exceed that of the fossil fuel plants. Understanding the reasons behind this and the possible limitations of the different technologies can help plan for sustainable energy systems in the future. Finally, solutions to minimize the impact of renewable energy are proposed for more environmentally friendly implementation and future research.

A review on using thermoelectric cooling, heating, and electricity generators in solar energy applications

Parallel with the rapid advances of solar technologies as the very crucial components of future energy systems, much interest has also been observed in scientists and commercial companies for the further development and use of thermoelectric systems during recent years. This has led to an interesting field of research combining solar systems/technologies and thermoelectric systems for a variety of purposes. Thermoelectric cooling, heating, and power generators are here proposed in different ways to enhance the performance of solar energy systems. In thermoelectric heating applications, the hot side of the thermoelectric module contributes to increasing the temperature of the solar working fluid in e.g. the desalination, water or air heater systems, etc. For the thermoelectric cooling case, the cold side of the module plays a supplementary or main role in reducing the temperature of e.g. PV panels, the condensation area of desalination units, etc. A thermoelectric power generator may be used to convert the waste thermal energy or the main heat flow output of a solar system to electricity. A combination of heating, cooling, and/or electricity generating thermoelectric units in solar systems could also be frequently seen in the literature. This article, presenting a summary and discussion of previous articles published recently in the literature, aims to make a review and assessment of the methods and impacts of thermoelectric devices on the performance of solar systems in various designs, and thereby, giving readers a good insight into the state-of-the-art and the gaps.

Unique performance of a standalone solar photovoltaic energy system with SEPIC converter

Electricity production using Photovoltaic (PV) energy system is the cheapest among all existing means. The primary source of input to this system is sunlight, which is available plenty in nature. To transfer maximum power available from the PV source at any instant, the service of Maximum Power Point Tracking (MPPT) controllers are needed. Incorporation of the most suited MPPT controller in the system limits the supply of abnormal current, voltage and power to the load. Moreover, the nonlinear behavior of the Solar PV (SPV) module with respect to the output parameters like current, voltage, power depends mainly on climatic conditions like solar irradiation and air temperature of the location. To increase the voltage magnitude of a PV source within a certain limit, PV cells may be arranged in series. The role of a high gain converter in the system is to increase this voltage compatible with the utility. By increasing the duty cycle, high voltage gain can be expected from a Boost converter. The performance of the Boost converter with high gain is not appreciated as a solution for a practical solar PV system on account of the presence of ripples in input/inductor current. This in turn increases the conduction loss and affects the converter power efficiency. Targeting an improvement in the performance of the PV system, highlighting improved voltage gain and converter efficiency, high gain SEPIC converters are proposed and their performance in the system are analysed using various MPPT controllers. The quality of the power obtained from the energy system has a specific dependence on the type of controller and converter used [1]. There are various research work utilizing different controller techniques in search of efficient utilization of energy from a PV source [2,3]. This paper addresses an analysis on the ripples associated with input current, output voltage, output power and power efficiency of a PV energy system, employing a high gain DC-DC converter. Performance analysis is carried out using four different controllers and climatic conditions. A detailed simulation is done on the system and analysis is done based on the outcome from numerical experiments. Even though numerous research contributions are available in the field of solar energy extraction using different controller techniques, this paper specifically concentrates on a high gain converter which multiplies the input voltage to nearly 20 times at the output side. Here an elaborate analysis is done on the system with four different controller techniques in search of the most apt MPPT controller for the system.

Modified Seagull Optimization Algorithm based MPPT for augmented performance of Photovoltaic solar energy systems

The changing weather conditions and Partial Shading Situation (PSS) create numerous challenges in harvesting available maximum power from the solar Photovoltaic (PV) systems. The limitations of classical and bio-inspired optimization-based Maximum Power Point Tracking (MPPT) methods are incapable of extracting maximum power under PSS. Therefore, this paper presents a Modified Seagull Optimization Algorithm (MSOA) based MPPT approach by incorporating Levy Flight Mechanism (LFM) and the formula for heat exchange in Thermal Exchange Optimization (TEO) in the original Seagull Optimization Algorithm (SOA) for accurate tracking of Global Maximum Power Point (GMPP) under transient and steady state operating conditions. The MSOA increases the capability of optimization in finding the optimal value of boost DC-DC converter’s duty cycle, D, for operating at GMPP. The superiority of the presented MPPT approach is contrasted with SOA MPPT under uniform irradiation situation and partial shading situations using Matlab Simulink platform. With the presented MSOA MPPT, the settling time and percentage maximum overshoot are reduced by 0.92 times and 0.55 times in comparison to SOA MPPT with increased efficiency. The hardware results validated the simulation results proving the proposed MSOA MPPT as an efficient MPPT for solar PV systems.

Optimization and sizing of SPV/Wind hybrid renewable energy system: A techno-economic and social perspective

The present work intends to develop a rural hybrid renewable energy system and examines the techno-financial, and social viability of the same system for rural sites in northern India. This work analyses the performance of Solar Photovoltaic-Wind hybrid renewable energy system for a residential load demand of 51.54 kWh/day with 11.51 kW peak in standalone application. In this paper, a nine parameter based multi-target optimization and sizing scheme is executed by HOMER PRO software. This work sequentially addresses the techno-economic and socio-environmental indicators for optimization analysis. The specification of technical and financial fields are availed from the Indian local market. Seven feasible combinations of different resources are analysed for designing parameters and the optimized combination is selected by multi criteria analysis. The solar Photovoltaic-wind-diesel generator-battery storage is found best combination for consistent power supply. The net present cost and cost of energy are obtained as $ 0.179 and $31,439 respectively. Sensitivity analysis is performed for macro-economic factors and components cost to obtain better opportunity. Moreover, the technical and commercial viability of the proposed system is assured by robustness check for satisfying the consumer demand.

The effect of cross-section geometry on the performance of a solar nanofluid heater in a parabolic solar receiver: A comparison study

Backgroundsolar collectors play a considerable role in transferring the irradiative thermal energy to the working fluid flow. The geometrical manipulation can enhance the performance of solar energy systems and helps to improve solar energy extraction for going forward to the sustainable development of countries. Structural modifications along with choosing efficient geometry should be followed for solar collectors. To address this issue, the current numerical work compares three various cross-sections, potentially used in the solar collectors, as the rectangle, triangle, and trapezoid. Methodthe collector is filled with nanographene-water nanofluid. The numerical simulation for various Reynolds numbers, volume fractions of nanoparticles and cross-section shapes are performed and the changes in the hydraulic and thermal performance of the fluid flow are appraised. Findingsit is shown that the Nusselt number and pressure drop achieve a higher value by increasing the Reynolds number and the volume fraction of nanoparticles. The rectangular duct presents the best thermal behavior by smearing the hot zone on the absorber plate and the Nusselt number at least 72% higher than the other geometries, for Re = 18, 000. At the highest Reynolds number, the rectangular duct can mitigate 5% the pressure drop compared to the other geometries. The performance evaluation criteria for the rectangular duct is 14 and 9%, respectively, for the volume fraction of 1 and 2%, more than the triangular duct, having the least value of the performance.