Output Power Of Module Research Articles

Proposal design and thermodynamic optimization of an afterburning-type isothermal compressed air energy storage system integrated with molten salt thermal storage

The isothermal compressed air energy storage is a potential technique for large-scale energy storage. In this study, the molten salt thermal storage is integrated with the afterburning-type isothermal compressed air energy storage system, which uses liquid piston compression technique, to enhance the thermal performances. Thermodynamic models of the hybrid energy storage system were developed, and the genetic algorithm was used to optimize multiple parameters to enhance the energy efficiency. Four operation modes, i.e., the high, medium-high, medium-low, and the low output power modes were proposed, to enhance the operational flexibility of the hybrid energy storage system. When the system operates in the operation mode of medium-low output power, a portion of afterburning heat is stored in the molten salt and used in the operation mode of low output power. Results show that the roundtrip efficiency of high, medium-high, medium-low, and the low output power modes are 63.57 %, 59.33 %, 57.13 %, and 83.28 %, respectively. Exergy analysis was carried out to quantify the irreversibilities of key components. In the three modes of operation with afterburning, the combustion chamber has the highest portion of exergy loss, and in the operation mode without afterburning, the exergy loss of heat exchangers is higher than that of expanders.

Realization of a tracker for a parabolic cylindrical optical concentrator (application on a photovoltaic_thermal window

To produce more electrical energy with the same PV module, the output power of the PV module can be increased by increasing the solar radiation incident on the PV module, because the amount of electricity obtained from PV systems is directly proportional to the intensity of solar radiation projected onto the surface of the solar module. Implementing a solar concentration system can be a good solution to increase the efficiency of the PV module. This study examines the effectiveness of using a movable parabolic reflector in a window containing a photovoltaic array with a heat exchanger containing water circulating in a pipeline, to obtain a photovoltaic/thermal (PV/T) module. The results demonstrate that using a movable parabolic reflector with a cooling system to absorb excess heat in a building window could increase and improve the performance of the photovoltaic array and enable optimal use of solar panels with more energy produced.

Analysis of Control Strategy Based on P&G Algorithm for the Switching Component of Wireless Solar Power Transfer

Previous researchers have studied wireless power transfer (WPT), while the current study focused on converting DC voltage to AC voltage on the transmitting coil and transferring it to the receiving coil. Previous studies did not discuss the control technique to drive the switching component on the converter circuit, especially the change controller in DC voltage and current affecting the WPT performance. This paper discussed a control strategy based on the P&O algorithm for driving MOSFETs on the half H-bridge inverter of a wireless solar power transfer (WSPT) system. This system is constructed by PV modules as the main DC voltage source, a control strategy based on the perturb and observe (P&O) algorithm, a half H-bridge inverter, and a transmitting and receiving circuit. The WSPT system was modelled using MATLAB SIMULINK with various solar irradiance, and its performance was assessed. The results show that the change in the total output voltage and power of PV modules can be controlled by the P&O algorithm as the signal input of the PWM generator applied to drive the MOSFETs. There are no transient values of voltage waveform on the transmitting and receiving circuit when the transient total output voltage of PV modules occurs. It indicates that the P&O algorithm can effectively manage the PWM generator for its input signal, including the total output voltage and current produced by PV modules.

Comparative experimental analysis of monocrystalline and polycrystalline photovoltaic panel through hybrid phase change material

Photovoltaics thermal management is utmost necessary as continuous heating of the module decreases the performance. This paper presents a comparative study in terms of performance of two photovoltaic modules namely monocrystalline and polycrystalline using hybrid phase change materials (PCMs). The hybrid PCMs of RT31 and RT 44HC were prepared by method of stirring. The final prepared hybrid PCMs were poured at the back of panel and panel was covered. The purpose of these experiments is to improve the efficiency of polycrystalline and monocrystalline photovoltaic modules and comparing which technology is more efficient having hybrid PCM. The experiments were performed under the room conditions in Taxila, Pakistan. The output parameters of the photovoltaic module appeared to be strongly dependent upon the solar irradiance. The power output and efficiency of monocrystalline and polycrystalline photovoltaic module were measured. The monocrystalline panel maximum temperature was 36.59 °C as compared to polycrystalline panel which was 40.23 °C. As the radiation intensity increased till noon, electrical power was improved, and it was 6.52 W in monocrystalline panel as compared to polycrystalline panel which was 6.32 W. Electrical conversion efficiency was also observed, and it was maximum up to 11.84 % in monocrystalline panel. The higher performance was observed in case of monocrystalline panel having hybrid in terms of performance as compared to polycrystalline panel.

A Comparative Study of Artificial Neural Network Training Algorithms to Predict Photovoltaic Module Output Power

Introduction: Solar energy is a crucial component and contributes a large portion of renewable resources, the demand for which has recently increased. During previous years, it was difficult to predict the amount of energy obtained from photovoltaic systems. The angle of inclination of the solar panel, the amount of radiation, the speed of the wind, the amount of humidity, and the temperature of the weather are major factors that effectively affect the production of electrical energy. Scientists have used many strategies to predict the power generated by PV modules accurately, but each method has different pros and cons. Method: This study tested three training algorithms for artificial neural networks (ANN): scaled conjugate gradient (SCG), Levenberg Marquardt (LM), and Bayesian regularization (BR), determining which one performed best in terms of prediction speed and accuracy. Twenty-eight thousand two hundred ninety-six samples of experimental data for primary influencing environmental factors were fed into the artificial neural network, which consists of 15 hidden layers. Before training the network, we preprocessed the data to remove factors that have a secondary effect. Result: The analytical results showed that the artificial neural network trained according to the LM algorithm is the best in terms of accuracy and speed of predicting the resulting photovoltaic energy. Conclusion: The results showed that although the regression evaluation and MSE values for the LM, SCG, and BR algorithms are close (98.129%, 0.0622, 97.849%, 0.0587, 98.151%, and 0.0585, respectively), the LM training algorithm is the best in terms of speed of calculation and display of results.

Development of Steg with Concentrating System

Solar cells, also known as photovoltaic cells, convert light energy into electrical energy through the photovoltaic effect. Integrating a thermoelectric generator into a photovoltaic system enhances power output and efficiency by utilizing waste heat. Concentrators are employed to further improve solar panel efficiency. This study aims to develop a solar thermoelectric generator (STEG) with a concentrating system that includes a heat sink cooling system, thermoelectric generator, and concentrator. Three conditions will be tested to observe the power output of each solar module: PV standalone, STEG with a heat sink, and STEG with a heat sink and concentrator. The efficiency appears to improve from 9.21% for PV standalone to 10.11% for STEG with a heat sink and 10.23% for STEG with a concentrating system. The results of these studies aim to establish a benchmark for enhancing the efficiency of future STEG systems.

Study of ash deposition characteristics of photovoltaic arrays taking into account the effect of photovoltaic module spacing and its effect on output characteristics: Indoor experiment

The problem of ash deposition on the surface of photovoltaic (PV) arrays during actual operation seriously affects their power generation efficiency. Because traditional research does not consider the PV module spacing, it cannot reflect the actual PV array ash deposition problem. This study explored the influence of PV module spacing, combined with different tilt angles and inlet wind speeds, on the characteristics of ash deposition and power output of the PV array through indoor experiments. The study designed a set of experimental equipment for simulating the formation process of ash deposition on the surface of PV arrays and set up an experimental platform to measure the influence of ash deposition on the maximum output power (Pmax), open-circuit voltage (Uoc), and short-circuit current (Isc) of the PV modules. The findings indicate that as the tilt angle increases, the PV array experiences reduced ash deposition and increased output power. Conversely, higher inlet wind speeds lead to greater ash deposition and decreased output power. Moreover, widening the spacing between PV modules results in increased ash deposition and reduced output power. Notably, within the PV array, the front PV module accumulates more ash than the rear module, with larger particle sizes. These research findings have significant implications for optimizing PV array design and enhancing power generation efficiency.

Photovoltaic modules fault detection, power output, and parameter estimation: A deep learning approach based on electroluminescence images

Accurately detecting faults in photovoltaic modules/cells and estimating their effective power output and parameters of the equivalent circuit representation of photovoltaic modules is becoming increasingly critical for both the reliability of associated systems and the efficiency of electricity production from renewable energy sources. Existing studies often work with datasets containing photovoltaic cells that exhibit one fault at a time, leading to the classification of photovoltaic cells with multiple faults as “mixed” faults. Moreover, factors such as cell alignment and specific fault types, collectively called “cell level features”, are not considered in current studies estimating the power output of a photovoltaic module. Therefore, this paper focuses on a comprehensive deep-learning pipeline to separately detect three types of faults in photovoltaic modules/cells using electroluminescence images. Furthermore, it addresses the estimation of the output power of photovoltaic modules and the series resistance of their equivalent circuit, considering the cell-level characteristics extracted from the electroluminescence images. The proposed model demonstrates its ability to detect “black core”, “crack”, and “edge” faults with global accuracies of 0.93, 0.868, and 0.95, respectively. Furthermore, the proposed model estimates the power output of photovoltaic modules with a normalized mean absolute error of 0.03547 and a normalized root mean squared error of 0.04892. This outperforms the base model that relies solely on non-pre-processed detected faults and significantly larger models adept at extracting features from the electroluminescence images. Moreover, the VGG16-based model estimates the series resistance in the equivalent circuit representation of photovoltaic modules with a normalized mean absolute error of 0.04472 and a normalized root mean squared error of 0.0622.

Analysis of dust deposition law at the micro level and its impact on the annual performance of photovoltaic modules

Solar photovoltaic (PV) power generation is a promising clean energy technology, but dust affects its performance. This study, conducted in nine Chinese cities with varying climates and geographies, analyzed the physicochemical properties of dust and its deposition patterns at different inclination angles using microanalysis. Inclination angles were classified into three categories, and a dust density prediction (DDP) model was developed for each. The study also quantified the effect of different dust densities on PV module temperature in a controlled environment and created a PV module output power prediction (POPP) model to assess dust accumulation impacts on PV efficiency. It was found that dust insulation could increase PV module output power by 1.64 %–1.79 % at dust densities of 5–10 g/m2 and radiation levels of 400–800 W/m2. Combining the DDP and POPP models, a PV efficiency loss rate model was proposed to evaluate annual average PV efficiency and power generation losses at different tilting angles. Results showed an annual average PV efficiency loss of over 7 % at a 15° inclination angle and about 4.5 % at a 90° inclination angle. This study provides a framework for evaluating PV module mounting angles and cleaning intervals, optimizing solar energy systems for improved efficiency and returns.

Application of artificial neuronal networks for the prediction of monthly photovoltaic power using tilt angle as input in Rabat city, Morocco

The use of artificial neural networks (ANNs) is attracting interest in the field of renewable energies, particularly for predicting operational parameters of photovoltaic (PV) systems. Thus, the present study aims to develop an ANN model for predicting the output power of a photovoltaic module. In this context, two ANN models with two and three hidden layers, respectively, were trained and validated to predict the average monthly photovoltaic power Pp produced by a 0. 325 kW photovoltaic module by varying the tilt angle from 0° to 90° of Rabat city, Morocco. The training data used in this study include longitude, latitude, calculated photovoltaic power, tilt angle, and months as inputs, while the predicted Pp is the target. These two proposed models are trained using the Levenberg-Marquardt (LM) optimiser. Furthermore, to confirm the reliability of each of these two proposed ANN models, existing photovoltaic power data were used to validate the two proposed models by predicting the average monthly power of the photovoltaic module. For the tilt angle of 34°, which corresponds to the latitude of the city of Rabat, the ANN model with three hidden layers performed better with an R2 value of 0.9997.

Simulation on water photovoltaic heat exchange mechanism and influence on water quality, a case on the middle route of South-to-North Water Diversion project

Water photovoltaic (WPV) power stations have developed rapidly in recent years around the world. However, the thermal performance, power generation characteristics and the influence on water temperature and water quality of WPV power station need to be further studied. At present, there is no systematic and complete numerical method that can simulate the impact of solar panel on water environment. In this paper, a joint calculation method of WPV heat exchange model and 1-D hydrodynamic and water quality model is proposed. Taking Hebei section of the middle route of the South-to-North Water Diversion project as an example, the solar panel temperature, electrical efficiency, water temperature and water quality changes are analyzed. The results show that, compared with the static water condition, the solar panel temperature and air temperature below the panel decrease under flowing water condition. And the photoelectric efficiency is improved. After covering the WPV, the air temperature below the panel is significantly affected by the solar panel temperature. When moving from south to north along the middle route of the South-to-North Water Diversion project, the photoelectric conversion efficiency of WPV gradually increases, but module output power per square meter drops by 1.75 W/m2. The short-wave radiation flux reduction factor of WPV should be less than 20 % for ensuring the stability of water quality and the large discharge will weaken the short-wave radiation flux reduction influences on water quality.

Long-Term Solar Power Generation Forecasting at Eastern West Large Scale Solar (LSS) Farm using Random Forest Regression (RFR) Method

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

Research on heat recovery technology of liquid metal heat dissipation system

In the fields of battery packs, data centers, and chips, the inevitable waste heat threatens the performance and life of devices. To solve this problem, this research developed a self-driven cooling system based on heat recovery. The system uses liquid metal gallium as the cooling medium, uses waste heat to excite energy, and supplies power to the drive pump through the thermoelectric module to complete the cooling cycle. The cooling effect of the system is analyzed in detail through experimentation and simulation. At the same time, the critical modules of the system are optimized, and the topology circuits, including maximum power point tracking (MPPT), boost, and temperature monitoring, are designed and simulated. The temperature changes of the simulated heat source under different flow rates are further discussed. The research shows that the optimized topology circuit can improve the output power of the thermoelectric module by 13 %. Using waste heat, the system can output 5v-50 mA electric energy at a temperature difference of 30 K. The flow rate of liquid metal gallium can reach 100 mL/min, the pressure head is 250 Pa, and the temperature of the simulated heat source can be reduced by 4.5 K. In addition, the reasonable selection of flow rate can not only effectively reduce the device temperature but also reduce the power consumption. Compared with no self-drive, the heat dissipation efficiency is improved by 73.8 %. The system’s heat recovery and self-driving characteristics significantly improve the heat dissipation performance, which provides a significant reference value for the design of self-driving heat dissipation systems in engineering applications.

Edge passivation: Considerable improvement in photovoltaic performance of perovskite/silicon tandem solar cells

Edge recombination is considered hard to avoid entirely in silicon (Si) solar cells as well as Si-base solar devices, hindering their future commercialization. However, such an important issue in perovskite/silicon (PK/Si) tandem solar cells has not attracted much attention. Herein, a low-temperature, non-vacuum liquid-based edge passivation strategy (LEPS) to improve the power conversion efficiency (PCE) of PK/Si tandem solar cells is proposed. The minority carrier lifetime (τeff) of the PK/Si tandem sample with 495.8 μs significantly enhances to 739.7 μs after passivating the Si sub-cell edge recombination. The open circuit voltage (VOC) of the PK/Si tandem solar cell increases by up to +3.8%abs from the initial state after LEPS treatment due to edge passivation, leading to the PCE of the PK/Si tandem solar cell increases by up to +1.2%abs. Finally, a monolithic PK/Si tandem cell with a PCE of 29.48% was achieved by further utilizing the LEPS, which opened up a simple and effective avenue for enhancing the PCE of PK/Si tandem solar cells and further promoting a higher photovoltaic output power of tandem modules.

Active cooling of a photovoltaic module in hot-ambient temperatures: theory versus experiment

Abstract The performance improvement of a PV-module is investigated theoretically and experimentally in a long-term research-plan via module cooling by different approaches including passive, active, and evaporative cooling as well as water cooling for the same module. In the present paper, the investigation is conducted to decide on the suitability of active-cooling of the module in hot-ambient temperatures. A module without cooling is used as a base case for comparison against cooled modules with and without fins attached to the module’s rear-surface and extended down in an air-cooling duct underneath the module. At first, a theoretical study of heat transfer through the module is conducted to investigate how the calculated cell temperature and module output power are influenced by the air velocity from a blower, ambient temperature and solar irradiation. The results showed a decrease of cell temperature by about 7–10 °C with a subsequent increase of electrical efficiency. The cell temperature decreases significantly with the increase of duct height and with the increase of the number and length of fins, the same as in passive cooling. The cell temperature decreases by more than 3 °C at duct height of 0.2 m. The calculated values of cell temperature, open-circuit voltage and short-circuit current of the module with and without active cooling agreed reasonably with the present measured values over the day hours of two successive days in summer season. At air velocity of 1.5 m/s, the increase of electrical efficiency by active cooling was found 0.67–0.80 %. Further increase of air-flow velocity or duct-height in active cooling seeking higher efficiency is not recommended due to increase of consumed electric power by air-blower and limited decrease of cell temperature. This concludes that air cooling is not effective in regions of hot ambient temperatures. For a non-cooled module, the cell temperature is related to the ambient temperature in terms of the solar radiation and NOCT, the datasheet value of normal-operating-cell-temperature. The relationship is modified in the present paper to account for air-flow through the duct seeking its extension for application to air-cooled modules.

A fuzzy classification method based on rules learning for shaded and unshaded hotspot faults on photovoltaic modules

Hotspots (HS) reduce the output power (P) of photovoltaic modules (PVMs) and sometimes lead to fires. However, it is difficult to effectively classify and detect HS faults. In this paper, we focus on the classification and detection of Shaded HS and Unshaded HS. HS faults not only occur due to non-uniform illumination and parameter mismatch but due to internal defects as well, such as; low resistance defects of a cell. The Shaded HS can be repaired sometimes by water washing, such as PVMs in the desert, while the Unshaded HS can only be repaired by replacing them. Therefore, to classify and detect such HS faults, we propose a fuzzy classification method based on data-driven rules learning. Firstly, the characteristic performances of Shaded and Unshaded HS PVMs are compared with the performances of normal PVMs. Secondly, the percentage reduction algorithm is presented for extracting three fault indicators to identify such HS faults. Then, a classification method is proposed using a technique called the Mamdani-type Fuzzy logic system, in which the rules are obtained by a process called the “Data-Driven Rule Generation Method”. Finally, a low-resistance HS model is built using Matlab Simulink. The simulation and experiment results show that the proposed method has more classification accuracy for Shaded and Unshaded HS faults.

Design and optimal coolant flow rate analysis of an annular water-cooled automotive exhaust-based thermoelectric generator

This paper introduces a design of an annular water-cooled automotive exhaust-based thermoelectric generator (TEG). A comprehensive numerical model of TEG system is established to investigate the relationship between automotive exhaust temperature and coolant flow rate. Through numerical simulations, it is demonstrated that the implementation of this device can effectively enhance the temperature uniformity at the hot side of the heat exchanger, consequently increasing the TEG's power output. After introducing the annular water-cooled radiator, the temperature uniformity of the thermoelectric module increased by 18.8%. By utilizing the numerical simulation outcomes alongside empirical formulas, calculations for the pump power consumption, maximum output power, and maximum net output power of the thermoelectric module (TEM) are conducted. The maximum net output power can be increased by 3.30%–53.24% by controlling the coolant flow rate. As a result, a relationship curve depicting the average temperature difference between the hot and cold ends of the TEM, in relation to the optimum coolant flow rate, is obtained. This relationship curve serves as a valuable tool for adjusting the radiator's coolant flow rate and augmenting the TEG's net output power, ultimately achieving the objective of heightening the power generation efficiency of the TEG.

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

This study proposes a novel honey bee dancing (HBD) maximum power point tracking (MPPT) algorithm inspired by the foraging behavior of honey bees. When a bee finds nectar, it returns to the honeycomb and dances to inform others about the location of the nectar. Other bees then fly towards the location and gather the nectar. The proposed HBD algorithm uses five bees searching for the nectar who communicate with each other about the location and the quantity of nectar by dancing. Finally, the five bees found the location of the most nectar which is represented by the maximum power point. The proposed HBD algorithm applies to uniform irradiance condition (UIC) and partial shaded condition (PSC). It is then compared with the PV panel output power and load relationship (OPLR) algorithm and perturb and observe (P&O) algorithm. Experimental verification has been performed under both the UIC and PSC. At the UIC's irradiance level of 200 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 120 W, 118 W, and 94.5 W, respectively, with efficiencies of 99 %, 97 %, and 78 %. Additionally, under the PSC with an irradiance level of 600 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 218 W, 189.2 W, and 74.8 W, respectively, with efficiencies of 99 %, 86 %, and 34 %, and convergence times of 4.7 ms, 6.5 ms, and 6 ms, respectively. It is evident from the results that the solar MPPT performance of the proposed HBD algorithm is better than the OPLR algorithm and P&O algorithm. This method ingeniously combines the foraging behavior of bees with solar power generation to produce the maximum natural power. This approach does not require the development of photovoltaic (PV) panel specification data, complex calculations, and additional temperature meters and heliographs. It is highly efficient and has significant economic benefits.

Shooting Three Birds with One Stone: Device Construction and Thermal Management for Simultaneous Photothermal Conversion Water Evaporation, Thermoelectric Generation and Photocatalytic Degradation

AbstractIt's a promising way to address the global freshwater crisis and energy shortage through solar‐driven devices with evaporate, degrade, and generate electricity at the same time. Low‐efficient light‐thermal conversions for multi‐usage limit application. This paper presents a low‐cost melamine foam/TiO2/carbon black/bismuth (MF/TiO2/CB/Bi) composite with excellent activity to simultaneously process photothermal water evaporation, pollutant degradation, and thermoelectric output, namely‚ shooting three birds with one stone. Under 1 sunlight light intensity, the water evaporation rate reach 4.235 kg m−2 h−1, and the maximum electric power output of a single module is 0.733 W m−2. Meanwhile, Rhodamine B (RhB) solution with 4.0 × 10−4 mol L−1 can be almost degraded within 60 min. Excellent performance benefits from the enhanced adsorption by chemicals, 3D structure for accepting more incident light, and hydrophilic group reducing evaporation enthalpy. Combined with the simulation and experiment, the relationship between heat distribution and thicknesses is studied. When less than 1.98 cm, the lateral temperature is higher than the ambient temperature, thus additional energy cannot be obtained. This work provides a facile and novel method for the construction of photothermal conversion materials with multi‐functional, as a new and useful attempt to solve the shortage of fresh water and electric energy, as well as environmental pollution.

Simulation and validation of the performance of a polycrystalline photovoltaic module

The performance of a solar module is described by its Current-Voltage (I-V) and Power-Voltage (P-V) curves, it is therefore necessary to generate these curves in order to validate the parameters of the module namely Short Circuit Current, Open Circuit Voltage, Maximum Power, Current and Voltage at Maximum Power Points, Fill Factor, and Efficiency. This study highlights two methods of generating I-V and P-V curves from a 30W sunshine (AP-PM-30) module. The methods include a computer simulation using MATLAB/Simulink software and an experimental procedure at different values of Irradiance and cell temperature. Findings from this study show that an increase in irradiance at constant cell temperature of the solar module results to significant increase in the current produced but slight increase in voltage. Consequently, the power output of the solar module also increases, while increase in cell temperature at constant irradiance decreases the voltage but slightly increases the current produced by the module but power output decreases. Therefore, these explain the influence of irradiance and cell temperature on the performance of a photovoltaic module.