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Author: search.filters.author.Sriprapha, KobsakStart date: 2020

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    Enhancing PV system modeling accuracy with the irradiance intensity selection technique
    (2026-06-01)
    Songtrai, Sasiwimon
    ;
    Chinnavornrungsee, Perawut
    ;
    Sriprapha, Kobsak
    ;
    Kobayashi, Tomonao
    ;
    Niemcharoen, Surasak
    This study is a method to improve the accuracy of photovoltaic (PV) power modeling by the irradiance intensity segmentation. The developed model was compared with 2 years of data from PV system in Thailand and the original 1D5P and weight function models to determine accuracy. The results show that by applying the irradiance intensity selection technique with a 1D5P equivalent circuit model improved the accuracy of the proposed model. The proposed method achieved a significantly lower %root mean square error (%RMSE) compared with other models yielding %RMSE values of 0.26% under clear-sky and 2.80% under cloudy conditions. Seasonal evaluation further demonstrated improved prediction accuracy, with the lowest deviation observed during the rainy period, attributed to reduced dust accumulation. A 2 years comparison confirmed the proposed model exhibited the lowest deviation of 0.69%, outperforming conventional PV simulation programs. These findings indicate that irradiance segmentation enhances forecasting performance and provides accurate PV output estimation under real environmental conditions.
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    Impact of soot nanoparticle size and quantity on four-ball steel wear characteristics using EDS, XRD and electron microscopy image analysis
    (2022-01-01)
    Karin, Preechar
    ;
    Chammana, Pattara
    ;
    Oungpakornkaew, Pitchaporn
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    Rungsritanapaisan, Panyakorn
    ;
    Amornprapa, Warawut
    The effect of soot contamination on the tribological performance of engine oil was investigated. Carbon black is introduced to simplify diesel engine soot contamination. Besides, the tribological performance issue is verified by a four-ball tribometer. The steel ball worn surfaces were studied by Scanning Electron Microscopy (SEM), Optical Microscope (OM) and Energy Dispersive X-ray spectroscopy (EDX). In addition, Transmission Electron Microscopy (TEM) was used to investigate the morphology and nanostructure of soot and carbon black. According to the four ball test results, the average wear scar diameter of steel ball tested with engine oil blended with N220, N330, N550 and N660 by 1% by weight is larger than that of pure engine oil by 26%, 38%, 41%, and 39%, respectively. The wear scar diameter tends to increase after blended larger size of carbon black particle. The steel balls tested with formulated engine oil without soot contamination and with soot contamination by 0.5 wt%, 1 wt%, and 2 wt% have average wear scar diameters of 621, 567, 784 and 894 nm, respectively. On the other hand, wear scar roughness of steel balls tested with formulated engine oil without soot contamination and with soot contamination by 0.5 wt%, 1 wt%, and 2 wt% were 2.28, 0.25, 1.49 and 1.76 μm, respectively. Consequently, quantity of the soot nanoparticle approximately 0.5% by mass in engine oil significantly plays an important role in steel ball wear scar diameter and surface roughness reduction. Moreover, the agglomerated soot which is larger than the oil film thickness might block the lubricant from entering the contact. It leads to the breakdown of the oil film thickness resulting in increasing adhesive wear on the worn surface.
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    A method to improve the accuracy of simulation models: A case study on photovoltaic system modelling
    (2021-01-02)
    Bupi, Aekkawat
    ;
    Kittisontirak, Songkiate
    ;
    Chinnavornrungsee, Perawut
    ;
    Songtrai, Sasiwimon
    ;
    Manosukritkul, Phassapon
    This research presents a method to improve data accuracy for the more efficient data management of the studied applications. The data accuracy was improved using the preciseness function learning model (PFL model). It contains a database in which the amount of data is more or less dependent on all of the possible behavior of the studied application. The proposed model improves data with functions obtained by optimizing curves to represent the data at each point, which estimate the database’s diffusion behavior, and functions can be built around all of the various forms of databases. The proposed model always updates its database after processing. It has been learning to optimize the processing precision. In order to verify the precision of the proposed model through its application to a PV system simulation model, the process’s database should contain at least one year. This is because the overall behavior of the PV power output in Thailand depends on the seasonal weather; Thailand has three seasons in a period of one year. The testing was performed by comparing the PV power output. The simulation results with the actual measurement data (12 MW PV system) can be divided into two conditions: the daily comparison and the seasonal PV power output. As a result, the proposed model can accurately simulate the PV power output despite the sudden daily climate change. The average nRMSE (normalized RMSE) of the proposed model is very low (1.23%), and ranges from 0.30% to 2.26%. Therefore, it has been proven that this model is very accurate.
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    Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model
    (2020-01-01)
    Dawan, Promphak
    ;
    Sriprapha, Kobsak
    ;
    Kittisontirak, Songkiate
    ;
    Boonraksa, Terapong
    ;
    Junhuathon, Nitikorn
    The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.