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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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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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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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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Comparison of YOLOv8 Models for Aircraft Detection in Airport Apron Using Digital Image Processing
(2024-06-01)
Wiangkam, Nitipoom
;
Jiriwibhakorn, Somchat
Airport safety can be improved by efficient air traffic management, which monitors aircraft parking spots and controls their movement into them more efficiently. In addition to emergency situations, it can assist in accurately inspecting airport areas. Management costs can be reduced by reducing the workload of personnel in controlling and helping manage air and ground traffic. This research focuses on comparing the performance of various YOLOv8 models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8nl, and YOLOv8nx) in an automated aircraft detection system using digital image processing techniques. The methodology involves collecting a dataset of 1,000 airport apron images with parked aircraft, dividing them into 900 training images and 100 testing images. The YOLOv8 models are trained on the training dataset, and their performance is evaluated on the testing dataset using confusion matrices. Experimental results reveal that YOLOv8nx achieves the highest average aircraft detection performance, with a precision of 0.94, recall of 0.74, and f1-score of 0.83. Additionally, YOLOv8n demonstrates the highest processing speed at 0.95 milliseconds. Consequently, YOLOv8n is suitable for applications requiring high-speed processing, while YOLOv8nx is ideal for tasks demanding the utmost performance efficiency.
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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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Critical Clearing Time Determination of EGAT System Using Artificial Neural Networks
(2003-12-01)
Pothisarn, Chaichan
;
Jiriwibhakorn, Somchat
Currently, with the continuous interconnection and size increasing, the power systems become larger and more complex. Therefore, the study of transient stability for protection system design and planning is more difficult and takes more time due to system size and complexity. This paper proposes an application of Artificial Neural Networks (ANNs) in transient stability study, with fast access to the answer of the power system stability. ANNs was used to determine the critical clearing time (CCT) of the Electricity Generating Authority of Thailand (EGAT) system. The results show that designed ANNs can estimate the CCT correctly.
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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.

Jiriwibhakorn, Somchat

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