Solar Panel Power Research Articles

Design and Implementation of MPPT Solar System Based On the ANN & ANFIS

The project works with maximum power point tracking (MPPT) using the Perturb and Observe (P&O) algorithm. The P&O algorithm is implemented using the controller make up of logic components such as voltage follower, voltage inverter, differentiators, comparators, and X-OR gates. The solar energy harvested from solar panel is fed into the MPPT system to acquire maximum power output at the load. Typical solar panel works with low efficiency such < 15% thus, maximizing from the solar panel energy is a necessity to achieve optimum performance of solar energy. A simple MPPT system is built to maximize the power of solar panel. Key Word: Energy; Tracking; Solar Cells; Power; Temparature.

Studi Perencanaan Penerangan Jalan Umum Panel Surya di Kelurahan Gading Kasri Kecamatan Klojen

Malang City is a city that has an increasing need for electrical energy in the technological era, which today is very much needed in fulfilling daily life, both for household, social and industrial needs. One of them is the supply of electrical energy for Public Street Lighting (PJU). If you only rely on the supply of electrical energy from grid, this is a heavy burden that must be borne by the Malang City Regional Budget to pay the electric energy consumption bill for the PJU. This study aims to determine the comparison of the use of conventional PJU with solar powered PJU (PJUTS) in the area of Gading Kasri Village, Klojen District, Malang City both in terms of technical installations which include solar panels, batteries, and charger controllers as well as from an economic point of view. That shines for 5 hours every day, the solar panel power specifications are 240 Wp with 37 points of light. Budget calculations using the ACS (Annual cost system) which is obtained in the 16th year the difference from the initial investment costs and revenue from sales met at BEP (break event point) amounting to Rp. 256,761,376.00.

Potential Application of Carbon Quantum Dots (CQD) Synthesis from Rice Husk Waste Composite as Advanced Solar Cells to Increase Photon Energy Absorption in Maximizing Solar Panels Power Output Production

The increase in national energy consumption has forced us to intensify renewable energy utilization. Solar panel is one optional answer for that purpose. Unfortunately, the product resistance and photon absorption from silicon-based solar cells are still not good enough. Carbon quantum dots (CQD) could be an alternative advanced material that can be utilized to maximize the performance of solar panels. Therefore, researchers are interested in finding out how to manufacture CQD from the synthesis of rice husk waste composite as an advanced solar cell and analyze its potential to maximize solar panel photon energy absorption. Researchers use mixed study that applies literature review methods (as part of the descriptive research aspect) and causal comparative research methods (as part of the quantitative aspect). Based on previous research, rice husk waste was pre-processed by washing using de-ionized water (DI water) to remove residue, then baked and blended into powder, and cleaned using HCl as impurities remover. The hydrothermal process was carried out at 190 oC for 12 hours to synthesize CQD which functioned by amino and carboxyl. For further purification, CQD was dialyzed against DI water in cellulose. Super-dense material due to quantum level compression, makes CQD have a higher resistance when compared to silicon as solar cells. In addition, the absorption of photon energy that can be done by CQD solar cells has a higher percentage when compared to conventional solar cells. However, in this paper, the researchers did not make CQD and only analyzed through modeling which became the research gap in this paper.

ANALISIS KERJA LVD (LOW VOLTAGE DISCONNECT) MULTSISEM PADA AKUMULATOR 12 VOLT PADA PANEL SURYA

Solar Power Plant (PLTS) is an alternative energy generator that is widely used at this time, where the energy source will never run out. To support the performance of the PLTS system, an accumulator is needed that functions as an electric charge store with a capacity of 12 Volts, 105 Ah. with Low Voltage Disconnect multisystem 12 Volt as a protector. The purpose of this study is to analyze the work of the accumulator on the 12 Volt multisystem LVD protector system when charging and discharging in a solar panel power plant. The method used starts from the process of filling and emptying the solar panel power plant as well as data collection and evaluation. The experimental results obtained have been designed and are able to analyze the work of the accumulator against the multi-system LVD protector system when charging and discharging solar panel power plants with varying capacities.

Effect of Inductor for Designing an Inverter to Maximize Solar Panel Power Output

The current methods to maximize solar cell efficiency have proven too complex for low-power solar applications. In addition, traditional methods, such as changing the orientation of a solar panel, can be too difficult for large photovoltaic arrays. Therefore, in this work, various analyses will be done with the critical components of a solar PV circuit to estimate its maximum power point (MPP). The effect of inductance for designing a solar cell system for maximized power by controlling the DC/DC Boost controller.

Short-term prediction of PV output based on weather classification and SSA-ELM

In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types.

Rancang Bangun Maximum Power Point Tracking pada Panel Surya dengan Metode Incremental Conductance Menggunakan Zeta Konverter

Maximum Power Point Tracking (MPPT) is a method of maintaining the maximum output power of solar panels when the power of the solar panel changes over time due to the influence of the load or intensity of sunlight. This research proposed designing MPPT on photovoltaic using the Incremental Conductance method. The incremental Conductance method controls the duty cycle when the voltage and current change values ?I/?V>-I/V. This system uses one solar panel with a power capacity of 100 Wp, a ZETA converter as a DC-DC converter to lower the voltage, and used a load 12V/5W DC lamp. From the tests that have been carried out, the tracking time for MPPT Incremental Conductance is 4,5 seconds with a maximum power of 63,9015 Watt with irradiation of sunlight 813 W/m2. The average power without MPPT is 51,215 Watt, and by using MPPT Incremental Conductance, the average power is 57,274 Watt. So, there is an increase in power when using the MPPT Incremental Conductance control.

Analisis Kebutuhan Sel Tenaga Surya sebagai Sumber Tenaga Pompa Air Sumur untuk Irigasi di Wilayah Sladi, Kapanewon Ponjong, Gunung Kidul

This study raises the theme of analyzing the needs of solar cells as a source of power for well water pumps for irrigation in the Sladi, Kapanewon Ponjong, Gunung Kidul Regency. This research was conducted to analyze the daily electricity load, solar panel power and the number and power of the batteries needed. By using the method of field studies and interviews as well as data in the research area having agricultural land dominated by dry land, or rainfed, makes the land have drought problems. Therefore, PT. Virama Karya Persero is developing the village by drilling a drilled well with a solar cell-powered water pump as a source of irrigation water. In determining the capacity of solar panels and the number of batteries needed, it includes the daily electricity load found at the research location. Based on the calculation results, the water pump used has an electricity requirement of 30,000 watts per day requiring 15 solar panels with a power of 410 WP and 42 batteries with a capacity of 60 Ah 12 V as many as 42 batteries.

Forecasting the Solar Panels Power Output Based on Air Pollution and Weather in the Gulf Countries by Using Machine Learning

Solar panels' power output is greatly impacted by environmental factors such as weather patterns and air pollution. A solar panel's ability to generate power is limited by unfavorable weather conditions and pollution in the air. To ensure that solar panels are installed correctly and perform optimally, it is particularly important to understand their power output prior to their installation. A method for predicting solar panel power output is presented in this paper based on weather and air pollution characteristics. In order to develop machine learning models, three types of features are taken into account, including the weather, air pollution, and the combination of the two. The data was collected from the Gulf Countries (Saudi Arabia, the United Arab Emirates, Kuwait, Bahrain, Qatar and Oman) between 2018 and 2020. Experiments conducted on solar panels indicate that weather and air pollution can be decisive factors in predicting power output.

Output Power Prediction of Solar Photovoltaic Panel Using Machine Learning Approach

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its efficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, relative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms. The outcomes of this study showed that the multiple linear regression algorithm provides better performance with the result of mean absolute error, mean squared error, coefficient of determination and accuracy of 0.04505, 0.00431, 0.9981 and 0.99997 respectively, whereas the support vector machine regression had the worst prediction performance. Moreover, the predicted responses are in great understanding with the actual values indicating that the purposed machine learning algorithms are quite appropriate for predicting the output power of solar photovoltaic panels under different environmental conditions.

Perancangan Dual-Axis Solar Tracker untuk PLTS dengan Analisis Pengaruh Jumlah Sensor dan Tracking Delay

One of the problems with solar power plants is the angle of inclination at the time of installation. Errors in determining the angle would make solar power plants do not produce power optimally. To maximize the power generated by solar power plants, the tilt angle of the solar panel must be adjusted so that it faces directly towards the sun. This research designs a dual-axis solar tracker using a microcontroller, LDR sensor, and servo motor. In addition, the influence of the number of sensors and tracking delay is analyzed to determine the output power of solar panels with different configurations. Testing from 7:00 am - 5:00 pm, where there are 2 days of testing, 2 days of tracking delay testing and 3 days of testing 3 solar panels installed, first dual-axis solar tracker, the rest with 0° and 30° tilt angles. The results of the dual-axis solar tracker design on the x-axis can rotate from 0o to 20o, while the y-axis can rotate from 35° to -17°. Testing using 4 LDR sensors affects the angle change of the solar panel. Tests show tracking delays every 30 minutes and 1 hour have no significant effect. The power generated at a tracking delay of every 30 minutes is greater, but the power consumed is also greater. During the test, it was found that the 1-hour tracking delay produced 1.76% more net power. The last test of dual-axis solar tracker and solar power plant with tilt angles of 0° and 30°. During the test it was found that the dual-axis solar tracker produced 16.89% more power than the 30° tilt angle and 11.42% more than the 0° angle.

Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches.

In this paper, the impact of dust deposition on solar photovoltaic (PV) panels was examined, using experimental and machine learning (ML) approaches for different sizes of dust pollutants. The experimental investigation was performed using five different sizes of dust pollutants with a deposition density of 33.48 g/m2 on the panel surface. It has been noted that the zero-resistance current of the PV panel is reduced by up to 49.01% due to the presence of small-size particles and 15.68% for large-size (ranging from 600 µ to 850 µ). In addition, a significant reduction of nearly 40% in sunlight penetration into the PV panel surface was observed due to the deposition of a smaller size of dust pollutants compared to the larger size. Subsequently, different ML regression models, namely support vector machine (SVMR), multiple linear (MLR) and Gaussian (GR), were considered and compared to predict the output power of solar PV panels under the varied size of dust deposition. The outcomes of the ML approach showed that the SVMR algorithms provide optimal performance with MAE, MSE and R2 values of 0.1589, 0.0328 and 0.9919, respectively; while GR had the worst performance. The predicted output power values are in good agreement with the experimental values, showing that the proposed ML approaches are suitable for predicting the output power in any harsh and dusty environment.

Experimental investigation for an innovative hybrid photovoltaic/Thermal (PV/T) solar system

An innovative hybrid photovoltaic/Thermal (PV/T) solar system can simultaneously generate both electrical and thermal energy from solar energy. The solar PV panels in the (PV/T) solar system will generate electricity/power efficiently when the loop thermosyphon system (including loop thermosyphons, a heat exchanger, two solenoid valves, an accumulator, etc.) generates thermal energy by absorbing heat from it (solar PV panels). In addition, a decrease in the operating temperature of solar PV panels can increase the electrical efficiency of solar PV panels. In other words, extracting heat from solar PV panels can increase the electrical efficiency of the solar PV panels. In this research, the loop thermosyphon system was used in the innovative hybrid PV/T solar system, and the passive and loop thermosyphon system was used to extract heat from solar PV panels then transfer that thermal energy/heat to water in the shell and tube heat exchanger via latent heat of refrigerant. Consequently, the thermal energy from (warm) water could be used for washing hands, swimming pools, and so on. Furthermore, the loop thermosyphon system was designed without extra pumping power, which is the most crucial advantage of this method. And the loop thermosyphons were attached to the back side of solar PV panels. Finally, solar PV panels’ generated power and electrical efficiency with a loop thermosyphon system are investigated and compared with ordinary solar PV panels without extra cooling through a real-time field measurement/experiment method. The experiment results reveal that (when cooling occurred) the generated power of solar PV panels with the loop thermosyphon system reduced “up to a maximum of about 17%” lower the average operating temperature compared to ordinary solar PV panels; meanwhile, the solar PV panels have loop thermosyphon system increased “up to a maximum of about 7.3%” higher than ordinary solar PV panels during water flow rate into heat exchanger was 0.2 kg/s. The electrical efficiency showed improvement after using/combining the loop thermosyphon system.

Design and Programming of a Microcontroller Based on a Solar Tracking System

This paper is regarding the design and program of a Microcontroller Arduino Uno board using Arduino software to work as a photo-sensor (Active) Single Axis Solar Tracker System (SASTS). A solar panel, two photo-resistors which are also known as Light-Dependent Resistors (LDRs) on two sides (north/south) of the photovoltaic (PV,) and a servo motor are connected to the Uno board, which is previously running a code that is prepared by Arduino Integrated Development Environment (IDE) then it works as a tracking system. Here, the LDRs send the signal of the presence or absence of the light to the board and based on that signal the Uno reflects a new signal to the servo motor to rotate and find the light source. Lastly, the photo sensor single-axis tracker is made. While the system tries to move the panel face to the sun and change the irradiance intensity, in the meantime it starts a search to find the angle of the highest irradiance. Based on the results that are extracted from the data, the tracker system significantly boosts the output power ratio of the solar panel. Through using the Micro-controller Uno board, LDRs, servo motor, and specially designed mechanical base, the tracking system is constructed, determined from acquired data the influence of the STS on increasing the solar panel power ratio is tremendously obvious. Significantly, the tracker system rises the power ratio of the PV system. Significantly, the tracker system raises the power ratio of the PV. As a result, the tracker system remarkably boosts the output power ratio of the solar panel and increases by up to 32.18%.

Feasibility Study on the Influence of Data Partition Strategies on Ensemble Deep Learning: The Case of Forecasting Power Generation in South Korea

Ensemble deep learning methods have demonstrated significant improvements in forecasting the solar panel power generation using historical time-series data. Although many studies have used ensemble deep learning methods with various data partitioning strategies, most have only focused on improving the predictive methods by associating several different models or combining hyperparameters and interactions. In this study, we contend that we can enhance the precision of power generation forecasting by identifying a suitable data partition strategy and establishing the ideal number of partitions and subset sizes. Thus, we propose a feasibility study of the influence of data partition strategies on ensemble deep learning. We selected five time-series data partitioning strategies—window, shuffle, pyramid, vertical, and seasonal—that allow us to identify different characteristics and features in the time-series data. We conducted various experiments on two sources of solar panel datasets collected in Seoul and Gyeongju, South Korea. Additionally, LSTM-based bagging ensemble models were applied to combine the advantages of several single LSTM models. The experimental results reveal that the data partition strategies positively influence the forecasting of power generation. Specifically, the results demonstrate that ensemble models with data partition strategies outperform single LSTM models by approximately 4–11% in terms of the coefficient of determination (R2) score.

Potential of wind generators as additional source of energy in hybrid renewable energy systems located in Baltic region

Study of the joint daily variability of wind speed and solar activity in the region of Gulf of Finland and its impact on renewable energy sources. The influence of external factors on the variability of total output power of ground-based wind turbines and solar photovoltaic panels is investigated. To a small extent, wind turbines are able to complement the generation of renewable energy sources: 15% –30% wind turbines and 70% –85% solar photovoltaics is preferable ratio of renewable energy sources. Comparing two similar climate regions in different scenarios lead economic feasibility strongly corelate with fuel prices. Standalone diesel generators can be quite effective at first, but in the long-term it is not viable solution due to fuel costs. Combined wind-PV renewable energy system is more profitable, but it has higher initial cost.

PEMANFAATAN REFLEKTOR UNTUK PENINGKATAN DAYA LUARAN PANEL SURYA

The Government of Indonesia has committed to support the development of solar power plant, while the efficiency of solar panels still around 5-16%. There are several methods to increase the output power of solar panel, but mostly consume a big portion of energy from solar power plant. The objective is to identify an off-grid solar power plant optimization method with smaller power consumption. The proposed optimization method is to add a reflector that can be positioned to direct sunlight so that it always hits the solar panel, so that the solar panel is able to produce electrical power optimally. This method can save the use of electrical power, because the weight of the reflector is much lighter than solar panels. The position of the reflector can be adjusted automatically based on feedback from the monitoring system. Off Grid solar system consisting of solar panels, batteries, solar charge controller (SCC), inverters, safety against overloads, short circuits and lightning strikes and mounted reflectors that can be adjusted in position, so that a lot of sunlight hits the solar panels so that the solar panel output power increase. In choosing the type of reflector, several things need to be considered, namely weight, refractive index, size, shading effect and the reflector's resistance to wind, impact and shock. Data retrieval is carried out automatically with a monitoring system to measure and store data when testing solar power system without reflectors and with reflectors in various positions. Data retrieval is based on IEC Standard 61724, while data processing is based on IEA-PVPS T2-01, 2000. Installing reflectors on solar panels is proven to increase the output power of solar panels, where aluminum reflectors and flat mirrors produce the greatest output power at an angle of 83°. Flat mirror reflectors produce a greater increase in solar panel output power compared to flat aluminum reflectors

Design of novel energy harvesting device based on water flow manipulation

Water power is abundant to meet the energy needs of the world. To exploit these plentiful natural resources, a novel energy harvesting device has been designed based on the water field manipulation theory. The flow field can be redirected, and the kinetic energy can be harvested flexibly and efficiently. The proposed energy concentrator exhibits promising prospects to harvest low-speed water flow energy at a highly effective level of up to 4–7 times the power of solar panels and wind turbines. While this study is at an early stage, it can prompt the exploitation of water flow energy that is not directly available due to the low flow rates.

Maximum power point tracking based on self-adaptive neural network combined with incremental conductance

ABSTRACT This article proposes a hybrid method that combines the self-adaptive neural network (SANN) and incremental conductance (IC) algorithm for maximum power point tracking (MPPT) in a photovoltaic (PV) system. The main objective of the proposed algorithm is to extract the maximum power of solar panels quickly and faster than the recent artificial neural network (ANN)-IC MPPT algorithm. Compared to the previous ANN-IC algorithm, a minimum number of data sets were used to obtain the GMP without compromising accuracy. Furthermore, comparison demonstrates that the suggested SANN-IC provides high efficiency, reliability, rapid tracking, and less response time tracking of GMP than w.r.t. all state-of-the-art techniques and ANN-IC method. The simulation is done with the help of MATLAB/SIMULINK. This is the first article about the performance of the hybrid MPPT technique tested by a sun simulator. The benefit of this hybrid method is that it reduces the search space of ANN, and hence, convergence speed is significantly increased. Training time and computing power are optimized by altering the number of epochs and requiring less memory. The experimental result showed the best convergence time and less oscillation than the ANN-IC method. Hence, the overall performance of SANN-IC in a steady-state condition with a partial shading condition meets the motive of the work.

A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm


 
 
 
 Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.