• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.297-304
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• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2840-2851
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• 2022

Electric boilers (EBs) are the backup steam source for the auxiliary steam system of high-temperature gas-cooled reactor nuclear power plants. When the plant is in normal operations, the EB is always in hot standby status. However, the current hot standby operation strategy has problems of slow response, high power consumption, and long operation time. To solve these problems, this study focuses on the optimization of hot standby operations for the EB system. First, mathematical models of an electrode immersion EB and its accompanying deaerator were established. Then, a control simulation platform of the EB system was developed in MATLAB/Simulink implementing the established mathematical models and corresponding control systems. Finally, two optimization strategies for the EB hot standby operation were proposed, followed by dynamic simulations of the EB system transient from hot standby to normal operations. The results indicate that the proposed optimization strategies can significantly speed up the transient response of the EB system from hot standby to normal operations and reduce the power consumption in hot standby operations, improving the dynamic performance and economy of the system.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.433-449
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• 2021

In this study, a newly enhanced Fluid-Structure Interaction (FSI) model which incorporates mooring lines was used to simulate a floating structure. The model has two parts: a Computational Fluid Dynamics (CFD) model and a mooring model. The open-source CFD OpenFOAM® v1712 toolbox was used in the present study, and the convergence criteria and relaxation method were added to the computational procedure used for the OpenFOAM multiphase flow solver, interDyMFoam. A newly enhanced, tightly coupled solver, CoupledinterDyMFoam, was used to decrease the artificial added mass effect, and the results were validated through a series of benchmark cases. The mooring model, based on the finite element method, was established in MATLAB® and was validated against a benchmark analytical elastic catenary solution and numerical results. Finally, a model which simulates a floating structure with mooring lines was successfully constructed by connecting the mooring model to CoupledinterDyMFoam.

• Current Optics and Photonics
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• v.5 no.6
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• pp.652-659
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• 2021

The theoretical analysis and optimization of extrinsic Fabry-Perot interferometer (EFPI) opticalfiber humidity sensors are deeply investigated. For a typical dual-cavity structure composed of an optical fiber and a humidity-sensitive membrane (HSM), the changes in refractive index (RI) and initial length are discussed for polymer materials and porous oxide materials when relative humidity (RH) increases. The typical interference spectrum is simulated at different RH using MATLAB. The spectral change caused by changing HSM RI and initial length are simulated simutineously, showing different influences on humidity response. To deeply investigate the influence on RH sensitivity, the typical response sensitivity curves for different HSM lengths and air-cavity lengths are simulated. The results show that the HSM is the vital factor. Short HSM length can improve the sensitivity, but for HSM RI and length the influences on sensitivity are opposite, because of the opposite spectral-shift trend. Deep discussion and an optimization method are provided to solve this problem. According to analysis, an opaque HSM is helpful to improve sensitivity. Furthermore, if using an opaque HSM, a short air cavity and long HSM length can improve the sensor's sensitivity These results provide deep understanding and some ideas for designing and optimizing highly sensitive EFPI fiber humidity sensors.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.355-363
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• 2022

This paper presents the modeling for islanded hybrid AC/DC microgrid and the verification of the proposed supervisory controller for energy management for this microgrid. The supervisory controller allows the microgrid system to operate in different power flows through the proposed control algorithm, it has several roles in the management of the energy flow between the different components of the microgrid for reliable operation. The proposed microgrid has both essential objectives such as the maximum use of renewable energies resources and the reduction of multiple conversion processes in an individual AC or DC microgrids. The microgrid system considered for this study has a solar photovoltaic (PV), a wind turbine (WT), a battery (BT), and a AC/DC loads. A small islanded hybrid AC/DC microgrid has been modeled and simulated using the MATLAB-Simulink. The simulation results show that the system can maintain stable operation under the proposed supervisory controller when the microgrid is switched from one operating mode of energy flow to another.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.1-12
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• 2022

In order to overcome the power fluctuation issues in photovoltaic (PV) smart grid-connected systems and the inverter nonlinearity model problem, an adaptive backstepping command-filter and a double second order generalized Integrators (DSOGI) controller are designed in order to tune the AC current and the DC-link voltage from the DC side. Firstly, we propose to present the filter mathematical model throughout the PV system, at that juncture the backstepping control law is applied in order to control it, Moreover the command filter is bounded to the controller aiming to exclude the backstepping controller differential increase. Additionally, The adaptive law uses Lyapunov stability criterion. Its task is to estimate the uncertain parameters in the smart grid-connected inverter. A DSOGI is added to stabilize the grid currents and eliminate undesirable harmonics meanwhile feeding maximum power generated from PV to the point of common coupling (PCC). Then, guaranteeing a dynamic effective response even under very unbalanced loads and/or intermittent climate changes. Finally, the simulation results will be established using MATLAB/SIMULINK proving that the presented approach can control surely the smart grid-connected system.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.239-247
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• 2022

The existing studies on the localization in the GNSS (Global Navigation Satellite System) denied environment usually exploit low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit) sensors to replace the GNSS signals. However, the navigation system still requires GNSS signals for the normal environment. This paper presents an integrated GNSS/INS (Inertial Navigation System) navigation system which combines GNSS and multiple IMU sensors using extended Kalman filter in partially GNSS-denied environments. The position and velocity of the INS and GNSS are used as the inputs to the integrated navigation system. The Mahalanobis distance is used for novelty detection to detect the outlier of GNSS measurements. When the abnormality is detected in GNSS signals, GNSS data is excluded from the fusion process. The performance of the proposed method is evaluated using MATLAB/Simulink. The simulation results show that the proposed algorithm can achieve a higher degree of positioning accuracy in the partially GNSS-denied environment.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.26-38
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• 2022

This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).