Jiriwibhakorn, Somchat

Preferred name

Alternative Name

Jiriwibhakorn, S.

Jiriwibhakorn, Somchart

Main Affiliation

School of Engineering

somchat.ji@kmitl.ac.th

Now showing 1 - 10 of 17

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Optimal power flow problem solved by using Distributed Sobol particle swarm optimization
(2010-07-30)
Wannakarn, P.
;
Khamsawang, S.
;
Pothiya, S.
;
Jiriwibhakorn, S.
The Distributed Sobol particle swarm optimization (DSPSO) algorithm was studies for solving optimal power flow problem (OPF), in this paper. In the proposed method, swarm size of the particles is separated in multi-groups and searching procedure is divided according with the swarm group. Reducing search space and high cost elimination are concluded in the DSPSO. The DSPSO was tested for solving two sizes of the OPF problem, six bus test system and IEEE-30 bus test system respectively. The numerical results obtain from the DSPSO were compare with many optimization methods, namely bee colony algorithm (BA), differential evolution algorithm (DE), genetic algorithm (GA), particle swarm optimization (PSO) and tabu search algorithm (TSA). The results show that the proposed method had faster convergence and better solution than the rest methods.
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Effective horizon detection on complex seas using back propagation neural network
(2019-07-01)
Kumeechai, Pisanu
;
Jiriwibhakorn, Somchat
Object detection is one of the main features of surface vehicles that do not require a driver (USV). Research on the detection of the horizon is one of the basic factors in conducting obstructions. Precisely detection helps to improve the efficiency and accuracy of object detection. Because it will eliminate most irrelevant areas. In this paper, there are four different algorithms that apply to three image sequences that have been taken in the open sea. This paper focuses on the accuracy rate and efficiency of horizon detection. By testing four different algorithms, including Hough transform, least squares, RANSAC and neural networks to separate the horizon from the image. Experimental results show that artificial neural network methods use more time but accuracy better than others.
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Real-time critical clearing time estimation by considering contingency conditions
(2018-07-02)
Phootrakornchai, Witsawa
;
Jiriwibhakorn, Somchat
This paper presents an approach called adaptive neuro-fuzzy inference system for the transient stability assessment by considering contingency conditions of networks. The contingency condition herein means a case of transmission outage and network configuration change. In addition, in this study all significant dynamic parameters of a power system (e.g. machine models, excitation systems, turbine governors, and load characteristic etc.) are considered for the estimation. We use the critical clearing time for the transient stability index. The 9-bus IEEE is applied for the power dynamic simulation. Finally, this study shows that the adaptive neuro-fuzzy inference system can be applied with the real-time critical clearing time estimation subject to contingency conditions and some parameters affecting the system's dynamic behavior are taken into account.
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Assessment of wind power generation
(2018-08-13)
Wannakam, Khanittha
;
Jiriwibhakorn, Somchat
Wind power generation is an unstable source of renewable energy. Electricity depends on the weather at the installation location of the wind turbine. This paper presents the prediction of wind power by using the Weibull distribution and Adaptive Neuro-Fuzzy Inference System. The lowest error rates from the Adaptive Neuro-Fuzzy Inference System were 2.1912% and 4.5678%. This is the error value of the training data and the test data respectively.
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Evaluation of generation system reliability using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNS)
(2018-01-01)
Wannakam, Khanittha
;
Jiriwibhakorn, Somchat
This paper presents an evaluation of the reliability index of power generation systems using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) to compare the results obtained from the basic method of probability. The reliability index used in this study is the Expected Energy Not Supplied (EENS) index, which is used in planning to increase the installed capacity for the adequate demand for electricity. The ANFIS and ANNs techniques will learn the relationship between the priority level, the installed capacity and the force outage rate (FOR) of the generator, which significantly affect the EENS index. The results indicated that the ANNs techniques have the best predictive performance. The best accuracy of the training data was 1.2488% and the testing data was 2.3963%, calculated using a Mean Absolute Percentage Error (MAPE). Furthermore, the ANNs took more time to learn faster than the ANFIS.
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Reliability indices estimation in PEA distribution system using ANNs
(2017-05-10)
Jiriwibhakorn, Somchat
;
Kaewmanee, Keattisak
The purpose of this paper is to present the methodology of SAIFI, SAIDI and CAIDI estimation of Provincial Electricity Authority (PEA) distribution network in Thailand using Artificial Neural Networks (ANNs). The data used in this study was obtained from the Reliability Program of PEA. The feeder data from Singburi substation was used as the samples of ANNs in this research from January 2010 to October 2012. The three inputs of ANNs for reliability indices (SAIFI, SAIDI and CAIDI) consist of the number of customers behind the protective equipment, the interruption frequency of protective equipment per month and the total time interruption per month. ANNs gave the outputs (SAIFI, SAIDI and CAIDI) faster with accurate results.
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Application data for electricity load forecasting models
(2019-07-01)
Katruksa, Sooppasek
;
Jiriwibhakorn, Somchat
This paper used artificial neural networks (ANN) in medium-term energy forecasting for the Metropolitan Electricity Authority (MEA) area of Bangkok, Thailand. This method could improve the electricity load efficiency of the MEA. Moreover, The combined ANN with the GIS in next paper have a key role in the decision-making for investment in new substation and power system planning for maintenance and operation. The input data were clustered by K-means algorithms before training by forecasting the models. In this research, the energy forecasting models were the ANN (2 hiddens 4 hiddens). The prediction was based on the MEA's electrical energy history (six months; three months) and the gross domestic product (GDP). The results appeared to indicate that the prediction of ANN 4 hiddens (Classified Data Input) is more accurate.
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Online critical clearing time estimation using an adaptive neuro-fuzzy inference system (ANFIS)
(2015-05-17)
Phootrakornchai, Witsawa
;
Jiriwibhakorn, Somchat
This paper describes an approach using an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of online critical clearing time (CCT). The ANFIS can integrate neural networks and fuzzy logic principles, and has a potential to combine the advantages of both in a single framework. In this paper, the ANFIS is applied for the prediction of CCT by varying load levels and fault locations in buses and transmission lines. The IEEE 39-bus system and 9-bus western system coordinating council are tested and implemented in this study. All machines of the IEEE 39-bus system are considered as the classical model without considering any generator's exciters. While three machines in the 9-bus western system coordinating council are considered as detailed models, forth-order differential equation is described for all machines by considering the excitation system controller. CCT values obtained by the time domain simulation method using step-by-step calculation are used as the benchmark. The power world version 17 is used for transient simulation, and the ANFIS is implemented using MATLAB version 2014B. The results obtained from the ANFIS approach are quite satisfied with high accurate solutions and much lower computation time. Finally, the graphical user interface in MATLAB is applied for the online CCT estimation of two test power systems by using appropriate ANFIS models obtained from simulations.
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Flexibility and Frequency Security Enhancement to Generation Expansion Planning Framework
(2019-05-15)
Komasatid, K.
;
Jiriwibhakorn, S.
The traditional generation expansion planning (GEP) framework focuses on system adequacy where generation portfolio can cover the growing peak demand, taking into account the uncertainty. The need for low-carbon society, makes renewable portfolio (solar, wind, hydro, biomass, etc.) grow rapidly in many countries. Unfortunately, variable renewables (VR), i.e. wind, solar and small hydro, are changing conventional power system planning philosophy. On one side, dispatch characteristics require high flexible capacities to respond with the changes of system net load. The other side is that, frequency dynamics may deteriorate because the conventional generation is replaced by near-zero inertia generation and non-governor control. To encounter these problems, this study enhances flexibility and frequency security assessment into traditional GEP framework. The proposed framework is demonstrated on the modified IEEE RTS-1996, considering only high penetration of solar photovoltaics. Furthermore, simulation results signify that the optimal plan satisfies both flexibility and frequency security point of view.
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Correlation between predicted and tested temperature values of dry type transformer
(2017-11-03)
Teechuen, Phairote
;
Jiriwibhakorn, Somchat
The performance or output current of a transformer is subject to the load that it is connected to, which may be full, empty, or unusually excessive. Sometimes an overload may be unavoidable, such as in the case of a full load or an above normal load under which the transformer may carry an excessively high current and may result in a huge loss in the form of excessive heat. This will undoubtedly affect the functioning of the transformer, that is, the insulator that protects the coil may be deteriorated and the life of the transformer shortened. This article presents an analysis of the temperature change pattern of 1 MVA, 1,600 KVA cast resin transformer under a normal condition with the following objectives: to analyze the relationship between temperatures change of the low voltage and high voltage coils within predicted ranges of temperature; to predict the tolerances of the transformer against the load increase while extending the transformer life; and to predict the ability of the transformer to withstand heat due to temperature rising with a view to designing a high performance transformer. The method used in the test was by means of short-circuiting the low voltage coil while allowing the current flow through the high voltage coil at a rated load. This process highlights the rising temperature of the transformer as the prime factor in its performance.

Jiriwibhakorn, Somchat

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