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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
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Kittisontirak, Songkiate
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Chinnavornrungsee, Perawut
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Songtrai, Sasiwimon
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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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Sensing layer combination of vertically aligned ZnO nanorods and graphene oxide for ultrahigh sensitivity IDE capacitive humidity sensor
(2020-06-01)
Pongampai, Satana
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Pengpad, Puttapon
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Meananeatra, Rattanawan
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Chaisriratanakul, Woraphan
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Poyai, Amporn
An interdigitated electrode (IDE) capacitive humidity sensor fabricated on a silicon substrate was used to investigate sensing materials, which proved to be an ultrahigh-sensitivity humidity sensor. A sensing layer combination (SLC) between vertically aligned ZnO nanorods and optimal graphene oxide (GO) was prepared on the device and was tested as a humidity sensor. X-ray diffractometry (XRD) exhibited crystallized wurtzite structure of ZnO nanorods and transmission electron microscope (TEM) shown perfectly indexed hexagonal wurtzite ZnO structure dots position correspondence. A scanning electron microscope (SEM) was used to analyze ZnO nanorods/GO morphologies. Furthermore, Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) clearly exhibited GO presence and hydrophilic functional groups (carboxyl, epoxy, and hydroxyl), respectively. The SLC prominently demonstrated ultrahigh sensitivity (up to 196.95% or 1.97 times from commercial sensor; HS1101, Humirel) and linear responses behavior with 0.96 for coefficient of determination. The device sensitivity obviously improved as steps of 40, 50, 60, 70, 80, and 90% RH at values of 1.09, 1.41, 1.51, 1.65, 1.80, and 1.91 times, respectively. The device also exhibited fast response (25 s) and short recovery times (17 s). Its hysteresis (6.58%) manifestly improved to 1.84 times. Moreover, repeatability and long-term ability of the device demonstrated high accuracy (range ±0.37pF) and durability. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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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
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Sriprapha, Kobsak
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Kittisontirak, Songkiate
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Boonraksa, Terapong
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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.
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Contactless silicon-based multi-dimensional Hall sensor with simultaneous magnetic sensing and omni-rotational angle measurement
(2019-01-01)
Kaewjumras, Yongyut
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Prabket, Jirawat
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Titiroongruang, Wisut
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Niemcharoen, Surasak
This experimental research proposes a contactless silicon-based two-dimensional (2D) Hall sensor capable of simultaneous parallel- and perpendicular-directional magnetic sensing, with a 360° angle measurement. The Hall sensor was of non-symmetrical five-ohmic contact configuration (C1 – C5). In the study, experiments were carried out in three stages. In the first-stage experiment, the current (I) and voltage (V) of the 2D Hall sensor were determined under three schemes: schemes A (C1&C2), B (C2&C5), and C (C3&C4). In the second-stage experiment, the parallel and perpendicular absolute sensitivities of the 2D sensor were examined. Considering the discrepancy between the parallel and perpendicular absolute sensitivities, signal conditioning circuitry was incorporated into the sensor system to compensate, and the rotational angles measured in the final-stage experiment. The results revealed that the I-V curves were dominantly linear, corresponding to Ohm’s law. However, the parallel and perpendicular absolute sensitivities were low and unequal. Thus, signal conditioning circuitry was incorporated into the system to address the discrepancy and improve the performance. Importantly, the 2D Hall sensor exhibited a mere ±3<sup>o</sup> discrepancy between the measured and reference rotational angles, given the magnetic flux density of 1000 G, with the hysteresis error of 2.8%. In essence, the proposed contactless silicon-based 2D Hall sensor possesses high potential for high-precision industrial applications.
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The effects of gamma irradiation on threshold voltage and channel mobility models of PMOS
(2018-07-02)
Kerdpradist, Amonrat
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Titiroongruang, Wisut
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Atiwongsangthong, Narin
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Muanghlua, Rangson
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Ruangphanit, Anucha
This paper presents the effects of gamma irradiation on threshold voltage and mobility models of PMOS. The devices with a gate oxide thickness of 15 nm fabricated in an 0.8-micron CMOS technology were measured and tested. The electrical properties were collected pre-irradiation and after the exposure by a<sup>60</sup>Co gamma-ray source in the dose range of 1 to 10 kGy, transient dose = 290.68 Gy/hr. The results show that the threshold voltage of big MOS at zero substrate bias (VT0) increased approximately 28%, but the low field surface mobility (UO) and the maximum transconductance parameter (K0) decreased in the same value by 7.5% and the THETA (? ) parameter decreased by 28%. All parameters caused the saturation drain current of big MOS to decrease 16%. For short channel effects, the saturation drain current decreased more. Finally, the short channel model parameter should be investigated and discussed in future.
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Influence of thickness of p-type diamond Hall sensors synthesized by HFCVD on their response sensitivity to magnetic field
(2018-07-02)
Panyalert, Wasin
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Siriwongrungson, Vilailuck
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Suwanna, Prapakron
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Titiroongruang, Wisut
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Niemcharoen, Surasak
This article presents the study of the influence of thickness of p-type diamond Hall sensors, which were synthesized by HFCVD, on their response sensitivity to magnetic field. Boron was added in ethyl alcohol as the reactant to obtain p-type diamond film at B/C ratio of 10,000 ppm. The synthesis durations of 6, 12 and 36 hours at the substrate temperature of 750°C were selected to achieve the required diamond film thicknesses of 50, 100 and 150 µm, respectively. The synthetic diamond films were confirmed to be diamond using Raman spectroscopy and the cross sections were analyzed using SEM (Scanning Electron Microscopy). Then, simple p-type diamond Hall sensors were fabricated using four silver electrodes adhered on the diamond film. The ohmic of the electrodes were confirmed by the measurement of electrical properties of electrodes and magnetic field response at three different diamond thicknesses. The response sensitivity to magnetic field were 12, 9 and 6 µV/Gauss, respectively. When the thickness of the p-type diamond Hall sensor increased, the response sensitivity to magnetic field decreased.
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Performance of the Module Temperature Model in Forecasting the Power Output of Photovoltaic Systems
(2018-07-02)
Dawan, Promphak
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Worranetsuttikul, Kaweepoj
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Kittisontirak, Songkiate
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Sriprapha, Kobsak
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Titiroongruang, Wisut
This paper is the study the performance of the temperature model in forecasting the power output of photovoltaic systems. The prominent point of the solar power forecasting model was to use only one input parameter (solar irradiance). Solar irradiance was injected into the temperature model using mathematic equations. The output of temperature model and the solar irradiance was used by the solar power forecasting model for forecasting the power output of the photovoltaic systems. The results shows that the effectiveness of the temperature model had a discrepancy at 4.03%, which is acceptable.
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Comparison of Performance of 1D5P with 2 Input and 1 Input Model in Forecasting of Power Output for Photovoltaic Systems
(2018-07-02)
Dawan, Promphak
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Kittisontirak, Songkiate
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Sriprapha, Kobsak
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Muanglua, Rangson
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Titiroongruang, Wisut
This paper presents the comparison on forecasting performance of power output for photovoltaic system type 1D \pmb{5}\mathbf{P}, which used 1 input parameter being solar irradiance and 2 input parameters consisting of solar irradiance and the temperature model. The result shows that the forecasting of power output for photovoltaic system type 1D5P, which used 1 input parameter, provided root-mean-square deviation (RMSE) at 0.04 and for 2 input parameters provided RMSE at 0.05.
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The Effect of Gamma Irradiation on Threshold Voltage and Channel Mobility Degradation of NMOS
(2018-07-02)
Kerdpradist, Amonrat
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Ruangphanit, Anucha
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Titiroongruang, Wisut
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Muanghlua, Rangson
This paper presents the effect of Gamma Irradiated on threshold voltage, surface mobility and transconductance on N-channel MOSFET devices at a gate oxide thickness of 15 nanometers, which is fabricated at Thai Microelectronics Center by 0.8-micron CMOS technology. Then gamma irradiation (Co-60) at a total dose varied from 1 to 10 \mathbf{kGy}. By performing five rounds to observe changes in threshold voltage when the devices were exposed to gamma rays for measurement to extraction threshold Voltage (V<inf>TH</inf>) into the surface mobility (Uo) from I<inf>DS</inf>Vs V<inf>Gs</inf> curves in the saturation region. As a result, the threshold Voltage reduces at highest dose by approximately 38% compared with conventional N-channel MOSFET, for a parameter extraction in the level 3 model for MOSFET SPICE parameters is presented.
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The specific golf swing patterns using K-means clustering with the two-sided confidence interval
(2017-11-03)
Tangwongcharoen, Wisan
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Titiroongruang, Wisut
Golf is a popular sport for exercise or socializing. It affects an increasing number of patients. Because of these reasons the researchers decided to focus on this problem. We presented the analysis golf swing using K-Means Clustering with Two-Sided Confidence Intervals and the Closest Pair of Points Problem. The raw data were clustered by K-Means Clustering. The boundaries of subgroups processed by K-Means Clustering were calculated to represent the data of the normal and abnormal golfers, which we use as the diagnose patterns. The compared data is the comparison of the diagnose patterns of both normal and abnormal patterns. From the experimental results, the percentage of similar pattern is shown. Therefore, this algorithm can help the doctor to predict the injuries trend of golf players.

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