scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Penerapan Teknologi Pembangkit Listrik Tenaga Surya (PLTS) Untuk Mendukung Terwujudnya Kemandirian Energi Listrik Di Pesantren Raudhatul Ishlah, Tangerang Selatan

TERANG ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 200-210
Author(s):  
Sugeng Purwanto
Keyword(s):  
Power Generation ◽  
Program Evaluation ◽  
Power Plant ◽  
Community Service ◽  
Solar Power ◽  
Solar Power Plant ◽  
Solar Panels ◽  
Service Program ◽  
Solar Power Generation ◽  
Site Survey

This community service program is a development and continuation of previous program activities that take the theme of Solar Power Plant Design Training in Madrasah Aliyah Darussalam Parung Bogor with a focus on designing simple solar power plant for catfish ponds that use battery , DC lights, and 5 WP solar panels. The focus on this community service program is training on the application of appropriate technology for solar power generation with a household group R-1 / TR 2200 VA that uses solar panels, batteries, and controller. This program activity is divided into several stages, including site survey, socialization, solar power generation, and testing of solar power plant component, training, system installation, component assembly, and program evaluation. The first stage is a site survey conducted to obtain preliminary information about electricity usage patterns in the Raudhatul Ishlah. The second stage is the socialization of the program to the students and the community. The next stage is the solar power generation component testing to find out the quality and the identification of its components. Then the fourth stage is the introduction of its technology to give participants a greater understanding. The fifth stage is the assembly and installation of solar power generation components. The last stage is program evaluation using the interview and filling out the questionnaire. This program is held at Raudhatul Ishlah. This activity is the application technology which is very useful in the provision of electricity independently to support various activities in Islamic boarding schools and communities.  Keywords: solar power generation, hybrid power generation, load supply regulation, automatic transfer switch, photovoltaic   ABSTRAK  Program pengabdian kepada masyarakat (PKM) ini merupakan pengembangan dan kelanjutan dari kegiatan PKM sebelumnya yang mengambil tema tentang Pelatihan Rancang Bangun Pembangkit Listrik Tenaga Surya di Madrasah Aliyah Darussalam Parung Bogor dengan fokus kepada perancangan PLTS sederhana untuk kolam benur lele yang menggunakan komponen baterai, lampu DC dan panel surya 5 WP. Sedangkan fokus pada program PKM ini berupa pelatihan penerapan teknologi tepat guna pembangkit listrik tenaga surya (PLTS) dengan skala rumah tangga golongan R-1/TR 2200 VA yang menggunakan beberapa peralatan dengan teknologi tepat guna seperti panel surya, baterai dan controller (ATS – Automatic Transfer Switch). Kegiatan PKM ini terbagi dalam beberapa tahapan, meliputi: survei lokasi, sosialisasi, pengujian komponen PLTS, pelatihan, perakitan dan instalasi sistem serta evaluasi program. Tahap pertama adalah survei lokasi yang dilakukan untuk mendapatkan informasi awal mengenai pola pemakaian listrik di Pesantren Raudhatul Ishlah. Tahap kedua adalah sosialisasi kegiatan PKM kepada para santri dan masyarakat. Tahap selanjutnya adalah tahap pengujian komponen yang bertujuan untuk mengetahui kualitas dari PLTS, serta identifikasi komponen PLTS. Kemudian tahap keempat yaitu pengenalan teknologi PLTS untuk memberi pemahaman lebih mengenai teknologi PLTS. Tahap kelima adalah perakitan dan pemasangan (install) komponen PLTS secara keseluruhan. Tahap terakhir dalam kegiatan PKM ini adalah evaluasi program dengan menggunakan metode interview dan pengisian kuisioner. Kegiatan PKM ini dipusatkan di Pondok Pesantren Raudhatul Ishlah, Kelurahan Serua, Tengerang Selatan. Kegiatan ini merupakan penerapan teknologi PLTS yang sangat berguna dalam pengadaan listrik secara mandiri untuk mendukung berbagai aktivitas di dalam pondok pesantren dan masyarakat.  Kata kunci: pembangkit listrik tenaga surya, pembangkit listrik tenaga hibrid, pengaturan suplai beban, automatic transfer switch, photovoltaic.

scholarly journals Design Implication of a Distribution Transformer in Solar Power Plants Based on Its Harmonic Profile

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1362
Author(s):  
Iván Rafael Macías Ruiz ◽  
Luis Alonso Trujillo Guajardo ◽  
Luis Humberto Rodríguez Alfaro ◽  
Fernando Salinas Salinas ◽  
Johnny Rodríguez Maldonado ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Solar Power ◽  
Operating Conditions ◽  
Design Parameters ◽  
Design Calculation ◽  
Solar Power Plant ◽  
Distribution Transformer ◽  
Solar Power Generation ◽  
Harmonic Content

This article presents a comparative analysis for the design considerations for a solar power generation transformer. One of the main existing problems in transformer manufacturing is in the renewable energy field, specifically the solar power generation, where the transformer connected to the inverter is operated under a certain harmonic content and operating conditions. The operating conditions of the transformer connected to the inverter are particularly unknown for each solar power plant; thus, the transformer will be subject to a particular harmonic content, which is defined by the inverter of the solar power plant. First, the fundamental calculations for solar power plant transformer and the proposed methodology for the design calculation of the distribution pad-mounted three phase transformer are presented. Then, a design study case is described where a distribution transformer and an inverter of a particular solar power plant are used for the analysis. Next, the transformer under analysis is modeled using finite element analysis in ANSYS Maxwell® software, where the transformer will be designed for a non-harmonic and harmonic content application. Lastly, the main design parameters, flux density, the core losses and the winding excitation voltage of the transformer are calculated and presented in results and discussion section.

scholarly journals Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

2021 ◽  
Vol 309 ◽  
pp. 01163
Author(s):  
K. Anuradha ◽  
Deekshitha Erlapally ◽  
G. Karuna ◽  
V. Srilakshmi ◽  
K. Adilakshmi
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Random Forest ◽  
Regression Models ◽  
Solar Power ◽  
Machine Learning Techniques ◽  
Pv Systems ◽  
Solar Power Generation ◽  
Learning Techniques ◽  
Solar Power Forecasting

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

scholarly journals Analysis of correlation of climate factors affecting solar power generation

2018 ◽  
Vol 7 (3.3) ◽  
pp. 354
Author(s):  
Bok Jong Yoo ◽  
Chan Bae Park ◽  
Ju Lee
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Solar Radiation ◽  
Solar Power ◽  
Meteorological Data ◽  
Prediction Algorithm ◽  
Accurate Estimation ◽  
High Positive Correlation ◽  
Solar Power Generation ◽  
Positive Correlation Coefficient

Background/Objectives: In designing the solar power generation, feasibility review and power generation volume prediction during guarantee phase after the completion are very important.Methods/Statistical analysis: The study compares the actual power generation volume obtained from solar power generation monitoring system and estimated volume calculated using overseas meteorological data from Meteonorm 7.1 and NASA-SSE and Korean data from the Korea Meteorological Administration, in order to understand their accuracy. The calculation using KMA data, with the highest prediction value, was used to analyze the correlation among solar radiation, temperature, and solar power generation volume.Findings: Previous solar power generation volume prediction was conducted only with solar radiation value, which caused errors between the actual and predicted solar power generation volume. The study found that the power generation volume and solar radiation have a high positive correlation coefficient of 0.8131 for Songam Power Plant. For correlation between power generation volume and temperature, the coefficient for Songam was 0.2843 and 0.4616 for Jipyeong Power Plant, showing lower influence than that of solar radiation. In sum, solar radiation influences the solar power generation volume more than temperature, but the current study indicates that both solar radiation and temperature must be considered for an accurate prediction of solar power generation volume.Improvements/Applications: Research to develop solar power generation volume prediction algorithm that takes into account both solar radiation and temperature must be conducted to expand the application of solar power generation system with more accurate estimation of power generation volume.  

scholarly journals ANALYSIS OF FACTORS FOR FORECASTING ELECTRIC POWER GENERATION BY SOLAR POWER PLANTS

2020 ◽  
Author(s):  
D. Matushkin ◽  
А. Bosak ◽  
L. Kulakovskyi
Keyword(s):  
Mathematical Model ◽  
Power Generation ◽  
Power Plant ◽  
Electric Power ◽  
Electricity Generation ◽  
Solar Power ◽  
Target Function ◽  
Solar Power Plant ◽  
Electric Power Generation ◽  
The Mathematical Model

The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.

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