• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.10
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• pp.616-618
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• 2014

In this paper, we designed the accelerated life testing(ALT) and presented the lifetime prediction method of the RF SAW duplexer. We determined RF input power as an accelerated stress when designing an accelerating life testing and defined the lifetime of the duplexer as the period during which the insertion loss increased by 0.5[dB]. Lifetime prediction results of duplexer was estimated for 82,900hours at an ambient temperature of $85^{\circ}C$ and RF input power of 30[dBm].

• Journal of Power System Engineering
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• v.16 no.5
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• pp.55-62
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• 2012

Centrifugal compressors are widely used and each operating condition is different. However, it cannot be manufactured according to the every operating condition. In the this study, performance of compressor was evaluated with various rotational speeds of impeller and various stagger angles of diffuser in order to apply a typical model widely. A centrifugal compressor was designed and manufactured based on the design point. On this machines, an experiment was conducted and the performance was predicted at off-design point. The performance prediction was validated with the experimental result and the numerical result. Although the isentropic efficiency on the prediction was slightly lower than that on the experimental result due to the heat loss in the experiment, the pressure ratio was predicted well and also the predicted results were matched well with the numerical results. When the rotational speed of the impeller and the stagger angle of the diffuser were changed together, the compressor can be worked in the high efficiency region and avoided operating in the stall region.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Journal of Power Electronics
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• v.16 no.1
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• pp.319-328
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• 2016

In this study, we first briefly introduce the effect of circulating current control on the modulation signal of a modular multilevel converter (MMC). The maximum modulation index is also theoretically derived. According to the optimal modulation index analysis and the model in the continuous domain, different DC-side output impedance equivalent models of MMC with/without compensating component are derived. The DC-side impedance of MMC inverter station can be regarded as a series xR + yL + zC branch in both cases. The compensating component of the maximum modulation index is also related to the DC equivalent impedance with circulating current control. The frequency characteristic of impedance for MMC, which is observed from its DC side, is analyzed. Finally, this study investigates the prediction of the DC voltage ripple transfer between two-terminal MMC high-voltage direct current systems under unbalanced conditions. The rationality and accuracy of the impedance model are verified through MATLAB/Simulink simulations and experimental results.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3892-3901
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• 2021

The pressure drop of a moisture separator in a steam generator is the important design parameter to ensure the successful performance of a nuclear power plant. The moisture separators have a wide range of operating conditions based on the arrangement of them. The prediction of the pressure drop in a moisture separator is challenging due to the complexity of the multi-dimensional two-phase vortex flow. In this study, the moisture separator test facility using the air/water two-phase flow was used to predict the pressure drop of a moisture separator in a Korean OPR-1000 reactor. The prototypical steam/water two-phase flow conditions in a steam generator were simulated as air/water two-phase flow conditions by preserving the centrifugal force and vapor quality. A series of experiments were carried out to investigate the effect of hydraulic characteristics such as the quality and liquid mass flux on the two-phase pressure drop. A new prediction model based on the scaling law was suggested and validated experimentally using the full and half scale of separators. The suggested prediction model showed good agreement with the steam/water experimental results, and it can be extended to predict the steam/water two-phase pressure drop for moisture separators.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.134-137
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• 2001

In this paper on-line model is derived from investigating via series of finite element process simulation. Some variables that little affect on non-dimensional parameters. ie. forward slip and torque factor. is extracted from composing on-line model Especially, this research focused on deriving on-line model which exactly predict roll force and roll power in the roughing mill process under small shape factor and small reduction ratio. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model

• Environmental Engineering Research
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• v.21 no.4
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• pp.355-364
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• 2016

Beijing, as a cradle of modern industry and the third largest metropolitan area in China, faces more responsibilities to adjust industrial structure and mitigate carbon emissions. The purpose of this study is aimed at predicting and comparing industrial carbon emissions of Beijing in ten scenarios under different policy focus, and then providing emission-cutting recommendations. In views of various scenarios issues, system dynamics has been applied to predict and simulate. To begin with, the model has been established following the step of causal loop diagram and stock flow diagram. This paper decomposes scenarios factors into energy structure, high energy consumption enterprises and growth rate of industrial output. The prediction and scenario simulation results shows that energy structure, carbon intensity and heavy energy consumption enterprises are key factors, and multiple factors has more significant impact on industrial carbon emissions. Hence, some recommendations about low-carbon mode of Beijing industrial carbon emission have been proposed according to simulation results.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1085-1086
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• 2008

An error prediction system (EPS) for a power distribution system can predict an out-of-order state based on gathered data from the system. This paper describes a software structure of an EPS that is equipped with various sensors. The software analyzes the gathered data from sensors and predict error symptoms using statistical methods. The EPS system is installed on a real power distribution system.