• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1346-1354
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• 2018

Quality of the power system depends upon the reliability of its components such as transformer, transmission lines, insulators, circuit breakers and isolators. The transient voltage due to internal or external reasons may affect the insulation level of the components. The insulation level of these components must be tested against these conditions. Different studies, testing of different electrical components against high voltage impulses and different industrial applications rely on the international manufactures for pulsed power generation and testing, that is quite expensive and large in size. In this paper a model of impulse voltage generator with capacitive load of pin type insulator is studied by simulation method and by an experimental setup. A ten stage high voltage impulse generator (HVIG) is designed and implemented for different applications. In this proposed model, the cost has been reduced by using small and cheap capacitors as an alternative for large and expensive ones while achieving the same effectiveness. Effect of the distributed capacitance in each stage is analyzed to prove the effectiveness of the model. Different values of front and tail resistances have been used to get IEC standard waveforms. Results reveal the effectiveness at reduced cost of the proposed model.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1889-1900
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• 2018

Harmonic current mitigation is vital in power distribution networks owing to the inflow of nonlinear loads, distributed generation, and renewable energy sources. The active power filter (APF) is the current electrical equipment that can dynamically compensate for harmonic distortion and eliminate asymmetrical loads. The compensation performance of an APF largely depends on the control strategy applied to the voltage source inverter (VSI). Model predictive control (MPC) has been demonstrated to be one of the effective control approaches to providing fast dynamic responses. This approach covers different types of power converters due to its several advantages, such as flexible control scheme and simple inclusion of nonlinearities and constraints within the controller design. In this study, a finite control set-MPC technique is proposed for the control of VSIs. Unlike conventional control methods, the proposed technique uses a discrete time model of the shunt APF to predict the future behavior of harmonic currents and determine the cost function so as to optimize current errors through the selection of appropriate switching states. The viability of this strategy in terms of harmonic mitigation is verified in MATLAB/Simulink. Experimental results show that MPC performs well in terms of reduced total harmonic distortion and is effective in APFs.

• Structural Engineering and Mechanics
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• v.72 no.1
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• pp.31-41
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• 2019

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.

• International Journal of Reliability and Applications
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• v.17 no.2
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• pp.137-147
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• 2016

The engine mount of a car subjected to a pre-load related to the weight of the engine, and acts to insulate the vibration coming from the engine by moving on large or small displacement depending on the driving condition of the car. The vibration insulation of the engine mount is an effect obtained by dissipating the mechanical energy into heat by the viscosity characteristic of the rubber and the microscopic behavior of the additive carbon black. Therefore, dynamic stiffness from the intrinsic properties of rubber filled with carbon black at the design stage is an important design consideration. In this paper, we introduced a hyper-elastic, visco-elastic and elasto-plastic model to predict the dynamic characteristics of rubber, and developed a fitting program to determine the material model parameters using MATLAB. The dynamic characteristics analysis of the rubber insulator of the ACTIVE MOUNT was carried out by using MSC.MARC nonlinear structural analysis software, which provides the dynamic characteristics material model. The analysis results were compared with the dynamic characteristics test results of the rubber insulator, which is one of the active mount components, and the analysis results were confirmed to be valid.

• Structural Engineering and Mechanics
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• v.40 no.2
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• pp.257-277
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• 2011

In this paper the geometrically nonlinear continuum plate finite element model, hitherto not reported in the literature, is developed using the total Lagrange formulation. With the layerwise displacement field of Reddy, nonlinear Green-Lagrange small strain large displacements relations (in the von Karman sense) and linear elastic orthotropic material properties for each lamina, the 3D elasticity equations are reduced to 2D problem and the nonlinear equilibrium integral form is obtained. By performing the linearization on nonlinear integral form and then the discretization on linearized integral form, tangent stiffness matrix is obtained with less manipulation and in more consistent form, compared to the one obtained using laminated element approach. Symmetric tangent stiffness matrixes, together with internal force vector are then utilized in Newton Raphson's method for the numerical solution of nonlinear incremental finite element equilibrium equations. Despite of its complex layer dependent numerical nature, the present model has no shear locking problems, compared to ESL (Equivalent Single Layer) models, or aspect ratio problems, as the 3D finite element may have when analyzing thin plate behavior. The originally coded MATLAB computer program for the finite element solution is used to verify the accuracy of the numerical model, by calculating nonlinear response of plates with different mechanical properties, which are isotropic, orthotropic and anisotropic (cross ply and angle ply), different plate thickness, different boundary conditions and different load direction (unloading/loading). The obtained results are compared with available results from the literature and the linear solutions from the author's previous papers.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.2
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• pp.127-134
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• 2021

This study presents an overhang-type rail traction system using dual brushless AC (BLAC) motors with hall sensors. For an accurate position and moving length control of the designed rail traction system, instantaneous position controller using speed reference model and modified disturbance observer for BLAC motor with hall sensor are proposed. The presented speed reference model is designed to satisfy the required performance of 200 mm/s with proper acceleration and deceleration slopes to reduce mechanical vibration. Through the instantaneous speed reference model, instantaneous position and speed errors can be compensated together. Furthermore, the modified disturbance observer for BLAC motors with low-resolution hall sensors can improve the torque and speed control performance. The proposed disturbance observer is based on an actual motor speed. However, the feedback speed information of the hall sensor is not enough for use in the low-speed region. The practical adopted disturbance observer uses an activation speed band to the actual torque controller of the designed rail traction system. The proposed position control scheme is verified by the MATLAB-Simulink model and a practical manufactured traction system. In the computer simulation and experiments, the proposed position control scheme shows advanced control performance.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Journal of Power Electronics
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• v.19 no.2
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• pp.529-539
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• 2019

The modular multilevel converter (MMC) has become a promising topology for high-voltage direct current (HVDC) transmission systems. To control a MMC system properly, the ac-side current, circulating current and submodule (SM) capacitor voltage are taken into consideration. This paper proposes a low-computation indirect model predictive control (IMPC) strategy that takes advantages of the conventional MPC and has no weighting factors. The cost function and duty cycle are introduced to minimize the tracking error of the ac-side current and to eliminate the circulating current. An optimized merge sort (OMS) algorithm is applied to keep the SM capacitor voltages balanced. The proposed IMPC strategy effectively reduces the controller complexity and computational burden. In this paper, a discrete-time mathematical model of a MMC system is developed and the duty ratio of switching state is designed. In addition, a simulation of an eleven-level MMC system based on MATLAB/Simulink and a five-level experimental setup are built to evaluate the feasibility and performance of the proposed low-computation IMPC strategy.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• ETRI Journal
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• v.45 no.6
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• pp.1079-1089
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• 2023

Stroke is one of the leading causes of long-term disability worldwide, placing huge burdens on individuals and society. Further, automatic human activity recognition is a challenging task that is vital to the future of healthcare and physical therapy. Using a baseline long short-term memory recurrent neural network, this study provides a novel dataset of stretching, upward stretching, flinging motions, hand-to-mouth movements, swiping gestures, and pouring motions for improved model training and testing of stroke-affected patients. A MATLAB application is used to output textual and audible prediction results. A wearable sensor with a triaxial accelerometer is used to collect preprocessed real-time data. The model is trained with features extracted from the actual patient to recognize new actions, and the recognition accuracy provided by multiple datasets is compared based on the same baseline model. When training and testing using the new dataset, the baseline model shows recognition accuracy that is 11% higher than the Activity Daily Living dataset, 22% higher than the Activity Recognition Single Chest-Mounted Accelerometer dataset, and 10% higher than another real-world dataset.