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 39

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Network observability determination using artificial neural networks
(2005-12-01)
Tanprasert, Pornthep
;
Jiriwibhakorn, Somchat
This paper purposes a method for the determination of network observability of the Provincial Electricity Authority (PEA)'s power system in Thailand using the artificial neural networks (ANNs). The network observability problem related to the power system configuration or network topology, called the topological observability, is studied to solve the topological observability problem. The artificial neural networks (ANNs) based on back propagation learning are used as a tool to solve this problem.
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PEA distribution reliability (SAIFI, SAIDI) determination using artificial neural networks
(2005-12-01)
Kaewmanee, Keattisak
;
Jiriwibhakorn, Somchat
This paper purposes the methodology of SAIFI and SAIDI determination of Provincial Electricity Authority (PEA) distribution network in Thailand using Artificial Neural Networks (ANNs). Data used in this study was obtained from the Reliability Program[1]. The data of feeders 2 and 7 of Pattananikom substation was used as the examples in this research from January to July 2004. The three inputs of ANNs for reliability indices (SAIFI, SAIDI) consist of the number of customers behind the protective equipment, interruption frequency of protective equipment per month and total time interruption per month. Referring to the results obtained after inputting ANNs and SAIFI to be trained in neural networks, the mean absolute percentage error (mape) of feeders 2 and 7 are 0.0042%, 0.0168% respectively, while inputting SAIDI into the network, the mean absolute percentage error (mape) of feeders 2 and 7 are 0.6377%, 3.2942% respectively.
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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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Time Series-Based Fault Detection and Classification in IEEE 9-Bus Transmission Lines Using Deep Learning
(2025-01-01)
Jiriwibhakorn, Somchat
;
Kanwal, Shazia
Transmission line faults present a significant threat to the stability of power systems, potentially causing widespread outages. Timely detection of these faults is essential to prevent substantial disruptions in the power supply. This paper explores a time series-based deep learning technique for fault detection and classification in the IEEE 9-bus system. Post asymmetrical fault current and voltage time series data have been used to train a convolutional neural network (CNN), representing normal and faulty conditions, with convolutional and ReLU layers. A fully connected layer is used to detect features without missing critical information of the signal, achieving MSE as zero for fault detection and 0.0149 for fault classification. This demonstrates the effectiveness of CNNs for real-time fault detection and classification in complex power grids. The robustness of the CNN model indicates its potential for deployment in practical applications, enhancing the reliability and resilience of the transmission network. Using deep learning techniques opens opportunities for further improvements in fault detection and location strategies within the power grid.
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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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Critical generator and maximum power limit determination using neural networks
(2002-12-01)
Jiriwibhakorn, Somchat
The critical clearing time (CCT) often determines the maximum power output of a critical generator (CG) in a multi-machine network if transient stability is to be maintained under fault conditions. Using a time domain simulation method the CG can be identified and its maximum power limit (MPL) established. This paper proposes a novel method of using a neural network to predict the MPL of the CG. Both weighted and weightless neural networks are used and compared using Sobol sequences (Sob) to select the training patterns. The methods are tested on a 4 machine, 11 bus and a 10 machine, 39 bus New England system under variation of loading, fault location and network structures. It is shown that the permissible MPL of the CG can be established to within 5% of those obtained by time simulation.
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Artificial Intelligence based Faults Identification, Classification, and Localization Techniques in Transmission Lines-A Review
(2023-12-01)
Kanwal, Shazia
;
Jiriwibhakorn, Somchat
An overview of the many methods used for fault detection, classification and location in the power system, particularly in transmission lines, is provided in this review, it also includes an experimental result of adaptive neuro-fuzzy inference system -based fault detection , fault classification and fault location. Being in operation outdoor environment, transmission lines are more vulnerable to various faults which may lead to system collapse in severe cases. Therefore, to ensure the reliable and safe operation of power system it is imperative to critically monitor the faults in transmission lines. In this regard, researchers around the globe have developed several techniques and constantly putting efforts to further improve the protection efficacy. The brief yet thorough analysis and comparison of the artificial intelligence-based techniques, hybrid methodologies and most recent approaches in the context of power system faults have been discussed and presented. In addition, the research work and the experimental results of an adaptive neuro-fuzzy inference system-based techniques have also been discussed for IEEE-9 bus system. The mean square error for testing data of ANFIS-based fault detection, classification, is zero and for fault location Mean square error is 5.32km. This piece of work could be helpful in the development of a comprehensive understanding of various artificial intelligence-based techniques within the realm of fault detection, classification and localization in transmission lines.
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Advanced Short-Term Wind Power Forecasting Based on Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network
(2025-01-01)
Huang, Zhibin
;
Jiriwibhakorn, Somchat
Accurate short-term wind power forecasting plays a critical role in maintaining grid stability and enhancing the efficient utilization of renewable energy, particularly as wind energy continues to contribute increasingly to global electricity generation. This study explores and analyzes two forecasting approaches—Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), aiming to improve predictive accuracy. Both models utilize identical historical wind farm datasets and are trained, tested, and validated using the MATLAB R2023b platform. The research findings demonstrate that both ANN and ANFIS are well-suited for short-term wind power forecasting; however, ANFIS exhibits superior predictive accuracy compared to ANN. Specifically, the coefficient of determination (R<sup>2</sup>) values for ANN and ANFIS are 0.973 and 0.985, respectively. In terms of Root Mean Square Error (RMSE), ANN records 7.82e-03 during training and 7.44e-03 during testing, whereas ANFIS achieves a significantly lower 2.14e-03 in both phases. These results indicate that both models demonstrate a strong fit to actual data, with R² values approaching 1, validating their reliability for short-term forecasting. Furthermore, ANFIS proves to be more effective in handling data nonlinearity and uncertainty, consistently yielding lower RMSE values in both the training and testing phases. Despite achieving higher predictive accuracy, ANFIS requires a longer computational time. While this study confirms ANFIS's superior performance in short-term wind power forecasting, its advantage over ANN is not guaranteed in all scenarios, as the effectiveness of the model remains dependent on the complexity of input data and the choice of training function.

Jiriwibhakorn, Somchat

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