• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.3
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• pp.49-58
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• 2000

This paper deals with a systematic approach to neural network controller design for static VAR compensator (SVC) using a learning algorithm of error back propagation that accepts error and change of error as inputs, the momentum learning technique is used for reduction of learning time, to improve system stability. A SVC, one of the Flexible AC Transmission System(FACTS), constructed by a fixed capacitor(FC) and a thyristor controlled reactor(TCR), is designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage.TO verify the robustness of the proposed method, we considered the dynamic response of generator rotor angle deviation, angular velocity deviation and generator terminal voltage by applying a power fluctuation and rotor angle fluctuation in initial point when heavy load and normal load. Thus, we prove the usefulness of proposed method to improve the stability of single machine-infinite bus with SVC system.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.349-356
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• 2018

In this study, the synchronous control system is designed to restrain the speed difference generated between two propellers, namely, synchronous error in a dual electric propulsion system of unmanned surface vehicle, fish finder boat, etc. The control system based on coupling structure is composed of pre-filters and speed controllers for each propulsion system and a synchronous controller cross-coupled between two propulsion systems. The pre-filter and speed controller are designed in order that the propulsion system may follow the speed reference without overshoot and input saturation. And the synchronous controller is designed in consideration of damping and quickness of the synchronous controller system after analyzing effect of the skew disturbance and mismatched dynamic characteristics on synchronous error. Finally, the simulation results show that the designed control system is effective for elimination of synchronous error.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.7
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• pp.371-380
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• 2003

In this paper, a new design method of H$H_\infty$-Qn PSS using genetic algorithm(GA) is proposed to efficiently damp low frequency oscillations despite the uncertainties and various disturbances of power systems. The selection method of evaluation function is proposed for selecting the robust PSS parameters. All QFT boundaries are satisfied automatically and H$H_\infty$-norm is minimized simultaneously without trial and error procedure. The eigenvalues and the damping ratio of dominant oscillation mode are investigated to evaluate performance of designed controller for one machine infinite bus system. A disturbance attenuation performance is investigated through singular value bode diagram of the system. Dynamic characteristics are considered to verify robustness of the proposed PSS by means of nonlinear simulations under various disturbances for various operating conditions. The results show that the proposed PSS is more robust than conventional PSS.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2043-2051
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• 2006

This paper considered identification of vibration characteristics of flexible structure with vector channel periodic lattice filter. We present an algorithm for AR coefficients for the vector-channel lattice filters, and characteristic equation and transfer function are derived from these coefficients. Vibration lattice filter is then constructed from the vector channel lattice filter, and performance of this vibration filter is tested with a test signal which is a combination of many sine waves to compare the performance of scalar and vector channel lattice. Also it is applied to the cantilever data to identify properties of the system, such as natural frequencies and damping ratios, to show its performance.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• Journal of Korean Society of Rural Planning
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• v.12 no.3 s.32
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• pp.39-42
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• 2006

The purpose of this study is to develop rural population model adapting cohort survival method with sift-share effects. Administrative district in this study is below Myun: about 2,000 population. Population data of rural area in 1990, 1995, and 2000 by age cohort were selected for applying developed model. Damping coefficient from population data was calculated as 7% and results applying this coefficient in rural population data below the error from 12% to 1.06%. In detail, most of cohorts fitted with developed model except from 15 to 29 age groups. Application result of small population area; DaesulMyun revealed that main factor of population change is not natural change but migration.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.443-450
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• 2009

This paper deals with the design of decentralized controller for load-frequency control of interconnected power systems with superconducting magnetic energy storage units and Governor Dead Band Nonlinearity using Multi-Objective Evolutionary Algorithm. The superconducting magnetic energy storage unit exhibits favourable damping effects by suppressing the frequency oscillations as well as stabilizing the inter-area oscillations effectively. The proposed control strategy is mainly based on a compromise between Integral Squared Error and Maximum Stability Margin criteria. Analysis on a two-area interconnected thermal power system reveals that the proposed controller improves the dynamic performance of the system and guarantees good closed-loop stability even in the presence of nonlinearities and with parameter changes.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.