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Author: search.filters.author.Dawan, Promphak

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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.
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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
    ;
    Kittisontirak, Songkiate
    ;
    Sriprapha, Kobsak
    ;
    Muanglua, Rangson
    ;
    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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    Performance of the Module Temperature Model in Forecasting the Power Output of Photovoltaic Systems
    (2018-07-02)
    Dawan, Promphak
    ;
    Worranetsuttikul, Kaweepoj
    ;
    Kittisontirak, Songkiate
    ;
    Sriprapha, Kobsak
    ;
    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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    Leakage current measurement with the bench test in watchdog timer power down mode for microcontroller device
    (2017-11-03)
    Dechmunee, Parin
    ;
    Dawan, Promphak
    ;
    Titiroongruang, Wisut
    ;
    Atiwongsangthong, Narin
    This paper discusses the bench test for failure analysis. For identify the location of a defect on die circuit in microcontroller device. Bench (ipd-wdt) test method is very helpful. When curve tracer cannot detect the electrical leakage current in the form of I-V curve characteristic. In this article we discuss the bench test in watchdog timer power down (Ipd-wdt) mode with the closing all of frequency function and programming control watchdog timer function by C language. The program will be reference by datasheet then set to measure electrical current leakage. Then put into the OBIRCH or Ion Emission process for find defect location on die circuit. To identify the possibility that the type of defect.
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    A novel power output model for photovoltaic system
    (2017-10-19)
    Kittisontirak, Songkiate
    ;
    Dawan, Promphak
    ;
    Atiwongsangthong, Narin
    ;
    Titiroongruang, Wisut
    ;
    Chinnavornrungsee, Perawut
    This paper proposed the novel concept model for forecasting the PV power output. The model was used two meteorology to input model which are solar irradiance and module temperature. This model was improve the accuracy of model from simplified model is 1D5P by using the weight function. The proposed model was verified by comparison with measured data. The results showed that the model has high accuracy. The RMSE ranges from 0.02 to 0.07 and average RMSE is 0.04.