• Korea Trade Review
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• v.46 no.2
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• pp.281-299
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• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.165-175
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• 2015

Energy storage system has been widely applied in power distribution sectors as well as in renewable energy sources to ensure uninterruptible power supply. This paper presents a model predictive algorithm to control a bidirectional AC-DC converter, which is used in an energy storage system for power transferring between the three-phase AC voltage supply and energy storage devices. This model predictive control (MPC) algorithm utilizes the discrete behavior of the converter and predicts the future variables of the system by defining cost functions for all possible switching states. Subsequently, the switching state that corresponds to the minimum cost function is selected for the next sampling period for firing the switches of the AC-DC converter. The proposed model predictive control scheme of the AC-DC converter allows bidirectional power flow with instantaneous mode change capability and fast dynamic response. The performance of the MPC controlled bidirectional AC-DC converter is simulated with MATLAB/Simulink(R) and further verified with 3.0kW experimental prototypes. Both the simulation and experimental results show that, the AC-DC converter is operated with unity power factor, acceptable THD (3.3% during rectifier mode and 3.5% during inverter mode) level of AC current and very low DC voltage ripple. Moreover, an efficiency comparison is performed between the proposed MPC and conventional VOC-based PWM controller of the bidirectional AC-DC converter which ensures the effectiveness of MPC controller.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2058-2060
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• 2002

The demand for safety and reliability of Nuclear Power Plants (NPPs) has been constantly increasing and economical operation is also an important issue. Developing and adopting predictive maintenance technology for the major systems or equipment is considered as one way to achieve these goals. This paper suggests the predictive maintenance methodology that can be applied to NPPs and describes a sample application of the Rod Control System (RCS) to verify the effectiveness of the methodology. It is expected that the same methodology can be adopted for other systems of NPPs and general industry fields when its effectiveness is verified.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.6
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• pp.403-407
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• 2018

This study proposes a predictive direct power control method in a modular multilevel converter (MMC) high-voltage direct-current (HVDC) system. The conventional proportional integral (PI)-based control method uses a cascaded connection and requires an optimal gain selection procedure and additional decoupling scheme. However, the proposed control method has a simple structure for active/reactive power control due to the direct power control scheme and exhibits a fast dynamic response by predicting the future status of system variables and considering time delay. The effectiveness of the proposed method is verified by simulation results.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.5
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• pp.497-502
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• 2010

Many forces are pressuring utilities to reduce operating and maintenance costs without cutting back on reliability or availability. Many utility managers are re-evaluating maintenance strategies to meet these demands. To utilities how to reduce maintenance costs and extent the effective operating life of equipment, predictive maintenance technique can be adapted. Predictive maintenance has three types program which arc in-house program, engineering company program and mixed program. We can approach successful predictive maintenance program with "smart trust" concept.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.17-52
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• 2024

In this paper, the authors explore the modeling and control of a point absorber wave energy converter, which is connected to the electric grid via a power converter that is based on a linear permanent magnet synchronous generator (LPMSG). The device utilizes a buoyant mechanism to convert the energy of ocean waves into electrical power, and the LPMSG-based power converter is utilized to change the variable frequency and voltage output from the wave energy converter to a fixed frequency and voltage suitable for the electric grid. The article concentrates on the creation of a predictive control system that regulates the speed, voltage, and current of the LPMSG, and the modeling of the system to simulate its behavior and optimize its design. The predictive model control is created to guarantee maximum energy output and stable grid connection, using Matlab Simulink to validate the proposed strategy, including control side generator and predictive current grid-side converter loops.

• Journal of Power Electronics
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• v.18 no.2
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• pp.573-587
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• 2018

Recently evolved power electronics' based domestic/residential appliances have begun to behave as single phase non-linear loads. Performing as voltage/current harmonic sources, those loads when connected to a three phase distribution network contaminate the line current with harmonics in addition to creating a neutral wire current increase. In this paper, an enhanced performance three phase four leg shunt active power filter (SAPF) controller is presented as a solution for this problem. The presented control strategy incorporates a hybrid predictive fuzzy-logic based technique. The predictive part is responsible for the SAPF compensating current generation while the DC-link voltage control is performed by a fuzzy logic technique. Simulations at various loading conditions are carried out to validate the effectiveness of the proposed technique. In addition, an experimental test rig is implemented for practical validation of the of the enhanced performance of the proposed technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6B
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• pp.641-647
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• 2009

This paper proposes an improved predictive dynamic power management (DPM) scheme and a task scheduling algorithm to reduce unnecessary power consumption in embedded systems. The proposed algorithm performs pre-scheduling to minimize unnecessary power consumption. The proposed predictive DPM utilizes a scheduling library provided by the system to reduce computation overhead. Experimental results show that the proposed algorithm can reduce power consumption by 22.3% on the average comparing with the LLF algorithm for DPM-enable system scheduling.

• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.134-140
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• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.