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 13

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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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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
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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.
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Comparison of YOLOv8 Models for Aircraft Detection in Airport Apron Using Digital Image Processing
(2024-06-01)
Wiangkam, Nitipoom
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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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Design of DC/AC Unidirectional Inverter Based on DSP with Second-Order Switching Sequence Control in Photovoltaic DC Nano-Grids
(2025-01-01)
Bi, Chenxing
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Jiriwibhakorn, Somchat
Conventional inverters predominantly utilize Pulse-Width Modulation (PWM) control, operating in an intermediate state between the averaged and actual switching models. However, this approach often results in suboptimal output power quality and limited dynamic performance, which can compromise the operational stability of AC load systems. To address these limitations, this paper proposes a second-order Switching Sequence Control (SSC) strategy based on sliding mode theory, offering discrete-time control with enhanced robustness and precision. A comprehensive mathematical model of a single-phase LC inverter is developed and discretized. The SSC control algorithm is implemented using a TMS320F28335 Digital Signal Processor (DSP), with code generated via MATLAB/Simulink to streamline development and ensure real-time execution. Extensive simulations under various conditions demonstrate that the proposed inverter achieves excellent tracking accuracy, strong dynamic response, and reduced Total Harmonic Distortion (THD) is only 0.48%. Compared with conventional PWM, Selective Harmonic Elimination (SHE), and FPGA-based control methods, the SSC strategy significantly improves waveform fidelity and transient performance. These results confirm its suitability for high-performance applications in photovoltaic DC nanogrids, smart energy systems, and critical load support scenarios.
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Critical Clearing Time Prediction for Power Transmission Using an Adaptive Neuro-Fuzzy Inference System
(2023-01-01)
Jiriwibhakorn, Somchat
An adaptive neuro-fuzzy inference system (ANFIS) is a hybrid algorithm composed of fuzzy logic and an artificial neural network. It takes advantage of fuzzy logic and artificial neural networks to solve complex problems. For power transmission, several dynamic parameters are ignored for conventional transient stability assessment due to the complexity of the equations, and due to the long computational time required. Certainly, it is very difficult to do the real-time assessment of large power systems by considering dynamic impacts in detail. In this paper, an approach is required to increase the accuracy of results. Herein, a method, namely ANFIS, was found to overcome the limitations. All significant effects of dynamics can be taken into account; not only the machine model but also the turbine governor model, automatic voltage regulator (AVR) model, and load characteristic model are carefully considered. In addition, the model used for each generator unit is varied to achieve real conditions. The ANFIS output is the critical clearing time (CCT). CCT values are very important to be correctly predicted for the stability of power systems after clearing the faults. When the faults are cleared by opening the circuit breakers within the CCT values, the power systems are still stable. If the faults are cleared after the CCT values, the systems become unstable. The modified IEEE 9-bus and IEEE 39-bus systems are applied in the implementation of this study. The locations of faults and the levels of loads (except the power system topology changes) are varied for each simulation. The results from the ANFIS application indicate that ANFIS (considering the dynamic effects of the machine models, AVR system, turbine governors, and load characteristics) can predict CCT values with high accuracy. Moreover, when ANFIS results are compared to the artificial neural network (ANN) results, which are generally used, it can be seen that ANFIS results are better and take a lower time for training and testing processes than ANN. ANFIS can be adapted, improved, and implemented in real practice.
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Evaluation of the Power Demand for Economic Load Dispatch Problem Using Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network
(2024-01-01)
Jiriwibhakorn, Somchat
;
Wongwut, Kamolwan
The evaluation of power demand is fundamental in the Economic Load Dispatch problem, ensuring that the generated power meets the needs of consumers reliably and efficiently in planning system operations. This paper presented two approaches using an Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Artificial Neural Network (ANN) to evaluate the power demand. The modified IEEE 57-Bus system is considered the thermal units that incorporate renewables. The ANFIS and ANN are implemented using MATLAB online version R2023b. The results show that the ANN and ANFIS techniques are suitable for evaluating power demand. A comparison of both methods indicates that ANFIS is relatively superior to the ANNs techniques, considering the coefficient of determination of the ANNs and ANFIS were equal. The accuracy of its results in terms of prediction RMSE for the ANN and ANFIS of 10.147e-05 and 5.2177e-05 for the training and 14.639e-05 and 5.2177e-05 for the testing, respectively. Finally, the prediction accuracy of the ANFIS can be observed to be higher than that of the ANN, but the ANFIS takes longer to process. ANFIS is the method that can be appropriately applied to evaluate the power demand in this research. However, it could not guarantee for other research topics that ANFIS would be better than ANN for the RMSE. It depends on input and output data complexity and the training function type.
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Comparative Analysis of ANN and ANFIS-Based MPPT for Photovoltaic System
(2025-01-01)
Bi, Chenxing
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Jiriwibhakorn, Somchat
Eco-friendly and sustainable energy sources, such as solar and wind power, have rapidly integrated into the grid in recent years. Solar power generation is influenced by factors such as radiation intensity and temperature. However, the intermittent nature of solar energy poses challenges for consistent power generation. Intelligent techniques are essential for maximizing power extraction. This study focuses on developing Artificial Intelligence-based Maximum Power Point Tracking (MPPT) methods in solar photovoltaic (PV) systems. Two AI techniques were implemented, and their performance was compared to achieve more efficient results. Real-time data from micro-nano grids in China were utilized to design a feed-forward neural network and an adaptive neural network-based MPPT system. These systems aim to optimize solar energy harvesting and enable integration into nano-grid systems, offering affordable and eco-friendly energy solutions for families in Thailand. The algorithms were developed using MATLAB/Simulink, and both techniques demonstrated excellent performance. Notably, the ANFIS-based MPPT system outperformed, achieving a tracking efficiency of 93% in PV systems.
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A Novel Enhancement of the Classical Lambda Iteration Method for Solving Economic Load Dispatch Problem of Cost Function With Integrated Renewables
(2025-01-01)
Jiriwibhakorn, Somchat
;
Wongwut, Kamolwan
This paper presents a novel enhancement to the classical lambda iteration method for solving the economic load dispatch problem under modern power system conditions. The traditional LI method works well for simple cost functions, but it struggles with the complexities caused by valve-point effects and the growing use of renewable energy sources. Unlike metaheuristic methods that can be flexible but usually require a lot of computing power and are sensitive to how their settings are adjusted, the new λ-algorithm keeps the straightforward and predictable features of the original method while making it suitable for complicated situations involving non-convex costs and renewable energy sources. The algorithm incorporates mechanisms to handle non-smooth cost functions and account for variability from wind and solar generation. Its effectiveness is tested using simulations on four standard systems: an IEEE 57-bus system without renewable energy that cost $288,576; a modified IEEE 57-bus system with renewable energy that cost $283,774; a 38-generator system with a smooth cost function that cost $9,417,174.09 and CPU time of 1.3708 seconds; and a 40-generator system with valve-point effects that cost $121,714.07 and CPU time of 235.05 seconds. In all situations, the developed λ-algorithm provides affordable solutions while greatly cutting down on computing time, showing that it could be a useful, easy-to-use, and adaptable option for today’s ELD problems.
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Fault Detection in Transmission Lines Using CNN
(2024-01-01)
Kanwal, Shazia
;
Jiriwibhakorn, Somchat
Transmission line faults pose a significant risk to power systems, potentially leading to widespread outages. Detecting these faults using advanced algorithms is crucial for preventing major disruptions in power supply. In this paper, we work on a fault detection technique for the IEEE 9-bus system based on deep learning. By training a Convolutional Neural Network (CNN) on features extracted from both normal and faulty conditions, we achieve an accuracy of 86%. This high accuracy underscores the potential of CNNs for real-world implementation in fault detection systems. The robustness of the CNN approach suggests its viability for deployment in complex, real-time systems, offering improved reliability and resilience against transmission line faults. Additionally, utilizing deep learning techniques opens avenues for further refinement and optimization of fault detection strategies in the power grid.

Jiriwibhakorn, Somchat

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