Kaitwanidvilai, Somyot

Preferred name

Alternative Name

Kaitwanidvilai, S.

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School of Engineering

somyot.ka@kmitl.ac.th

Now showing 1 - 10 of 110

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A new directional relay algorithm for the protection of transmission network systems using discrete wavelet transforms
(2010-12-01)
Ngaopitakkul, A.
;
Kaitwanidvilai, S.
This paper proposes a novel directional relay algorithm to protect transmission network systems with an application of discrete wavelet transform (DWT). The fault signals are simulated using PSCAD/EMTDC. The coefficients of the positive sequence current obtained from each bus are compared in order to identify the direction of fault signals. The coefficient ratio between buses that the fault occurred is calculated so that the proper protective relay sequence can be selected. The result is shown that the algorithm is capable of performing the fault detection as well as arranging the protective relay sequence, with accurate results. ©2010 IEEE.
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Robust voltage stabilization in an isolated wind-diesel power system using pso based-fixed structure H∞ loop shaping control
(2009-07-30)
Vachyirasricirikul, Sitthidet
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Ngamroo, Issarachai
;
Kaitwanidvilai, Somyot
It is well known that the power system controller designed by H∞ control is complicated, high order and impractical. In power system applications, practical structures such as proportional integral derivative (PID) etc., are widely used, because of their simple structure, less number of tuning parameters and low-order. However, tuning of controller parameters to achieve a good performance and robustness is based on designer's experiences. To overcome this problem, this paper proposes a fixed structure robust H∞ loop shaping control to design Static Var Compensator (SVC) and Automatic Voltage Regulator (AVR) for robust stabilization of voltage fluctuation in an isolated wind-diesel hybrid power system. The structure of the robust controller of SVC and AVR is specified by a PID controller. In the system modeling, a normalized coprime factorization is applied to represent possible unstructured uncertainties in the power system such as variation of system parameters, generating and loading conditions etc. Based on the H∞ loop shaping, the performance and robust stability conditions are formulated as the optimization problem. The particle swarm optimization is applied to solve for PID control parameters of SVC and AVR simultaneously. Simulation studies confirm the control effect and robustness of the proposed control. © 2009 The Institute of Electrical Engineers of Japan.
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Robust control design for three-phase power inverters using genetic algorithm
(2011-08-12)
Nimpitiwan, N.
;
Kaitwanidvilai, S.
This paper proposes a new technique to design a fixed-structure robust controller for grid connected inverter systems. The proposed technique applies the Genetic Algorithm to evaluate the optimal controller parameters. The integral squared error (ISE) of the controlled system is minimized and the robust performance (RP) of the system is satisfied. In the proposed design, the structure of controller is specified as a decentralized PI controller which is preferred for practical implementations. Simulation results show that the proposed technique is promising. © 2011 IEEE.
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Robust fixed-structure cascade controller for a quadratic boost converter
(2010-12-01)
Srithongchai, Pitsanu
;
Kaitwanidvilai, Somyot
In this paper, a new technique for designing a robust cascade controller for a quadratic boost converter is proposed. A performance index in H infinity loop shaping control, stability margin, is adopted as the objective function in our optimization control problem; GA is used to solve this problem to evaluate the optimal controller. Conditions of cascade control are adopted as constraints in our optimization problem. In addition, pre-compensator weight which is normally difficult to be selected is simultaneously determined with the controller. Comparative study with the conventional H infinity loop shaping is presented. Finally, simulation results verify the effectiveness of the proposed technique.
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Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach
(2011-01-01)
Kaitwanidvilai, Somyot
;
Olranthichachat, Piyapong
In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance. © 2010 Elsevier Ltd. All rights reserved.
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The impact of deadtime effect on unwanted harmonics conducted emission of PWM inverters
(2000-01-01)
Kaitwanidvilai, S.
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Khan-Ngern, W.
;
Panarut, M.
This paper presents an analysis of unwanted harmonics spectra impacted by deadtime effect which is the one of the conducted emission sources at the load terminal of PWM inverters. The unwanted voltage from this impact is shown that it is depend upon not only load current direction, deadtime interval, DC link voltage but also amplitude and phasor of the load. The analysis with Fourier transform theory is clearly shown this phenomena and the simulated and measured results for harmonics injection PWM and discontinuous PWM with deadtime of 2% and 4% of switching time are presented in this paper. The deadtime compensated show the improvement for PWM inverter performance. The results show closed agreement between both of results with a good support from the analysis and simulation.
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Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems
(2025-06-01)
Yanyong, Sarucha
;
Kaitwanidvilai, Somyot
This paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. Additionally, the system demonstrates the ability to adapt to environmental changes, maintaining high-quality performance despite variations. A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. The hybrid algorithm also reduces overshoot and undershoot, resulting in faster system responses. Simulation and experimental results demonstrate that the proposed technique outperforms conventional force control systems in terms of learning ability and overall performance.
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Harmonic reduction technique in PWM AC voltage controller using Particle Swarm Optimization and artificial neural network
(2010-12-01)
Piyarungsan, Pairoj
;
Kaitwanidvilai, Somyot
This paper proposes a novel harmonic reduction technique for designing a Pulse Width Modulation (PWM) AC voltage controller. In the proposed technique, Total Current Harmonic Distortion (THD<inf>i</inf>), subjected to be minimized, is formulated in a cost function in an optimization problem; the optimal turn on and turn off angles in PWM waveform for a given output voltage are evaluated by Particle Swarm Optimization (PSO) technique. To apply our proposed technique for all output voltages, artificial neural network (ANN) is investigated to approximate the switching angles from sets of optimal angles evolved by PSO. Simulation results show that the proposed technique is suited for designing and gains a better performance compared to the conventional technique.
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Dual-stage Classification Framework for Detecting Rare or Unseen Patterns Based on Novelty Detection and Supervised Learning
(2024-01-01)
Rungtalay, Natthakritta
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Kaitwanidvilai, Somyot
In this article, we propose a dual-stage classification framework designed for identifying rare or unseen patterns in the hard disk drive (HDD) industrial test process. The proposed framework integrates novelty detection and supervised learning methodologies to effectively address the challenges associated with imbalanced datasets and the detection of infrequent or unseen patterns within continuously changing environments. By employing novelty detection as the first-stage classifier followed by supervised learning as the second-stage classifier, the proposed method demonstrates an increased capacity to adapt to fluctuating environments, consequently enhancing the overall accuracy of process classification in practical manufacturing settings. To strengthen the robustness of novelty detection methods, an ensemble model technique is employed. Notably, the accuracy of the novelty detection methods in the first stage can be further enhanced with the incorporation of supervised learning techniques, particularly when a sufficiently large number of labeled samples are amassed. The proposed method consistently maintains accuracy, even in the face of changing environments, as it demonstrates the ability to adapt to data drift without necessitating the acquisition of new labeled data in the initial stage. This adaptability makes it particularly well suited for managing imbalanced datasets, rendering it highly practical for industrial applications. In a comprehensive case study conducted within the HDD industry, the framework exhibits immediate adaptability to rapidly changing environments while preserving high accuracy. This highlights the practical effectiveness of the proposed dual-stage classification framework in addressing the unique challenges posed by industrial scenarios.
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PSO-Optimized Deep Learning for Ultra-Precise Corrosion Detection on HDD Read/Write Heads
(2025-01-01)
Punyammaree, Chaiwat
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Kaitwanidvilai, Somyot
This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. A U-Net model optimized via Particle Swarm Optimization (PSO) is developed to enhance segmentation performance by automatically tuning hyperparameters. The methodology includes optimized SEM image acquisition, preprocessing (patch-based subdivision and expert annotation), PSO-driven hyperparameter selection, and post-processing with thresholding and connected component analysis for pit counting. Experimental results demonstrate that the PSO-optimized U-Net significantly outperforms standard U-Net, SegNet, and LinkNet models, achieving an F1-score of 79.60%, an IoU of 86.51%, and an accuracy of 99.77%. Additionally, the proposed method achieves 86.9% counting accuracy, surpassing human experts (72.7%) while processing images 15 times faster (180 seconds vs. 2700 seconds per image). These findings highlight the potential of PSO-optimized deep learning for improving HDD quality control by providing an accurate, efficient, and standardized solution for corrosion pit detection, ultimately reducing the risk of HDD failure and data loss.

Kaitwanidvilai, Somyot

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