• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.265-274
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• 2022

This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.27-30
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• 2005

Because of unreplenishable power resources, reducing node energy consumption to extend network lifetime is an important requirement in wireless sensor networks. In addition both path length and path cost are important metrics affecting sensor lifetime. We propose a dynamic clustering scheme based on location in wireless sensor networks. Our scheme can localize the effects of route failures, reduce control traffic overhead, and thus enhance the reachability to the destination. We have evaluated the performance of our clustering scheme through a simulation and analysis. We provide simulation results showing a good performance in terms of approximation ratios.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.422-429
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• 2013

Advances in wireless sensor network (WSN) technology have enabled small and low-cost sensors with the capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. In the WSN, the sensor nodes have a limited transmission range and their processing and storage capabilities as well as their energy resources are limited. A triple umpiring system has already been proved for its better performance in WSNs. The clustering technique is effective in prolonging the lifetime of the WSN. In this study, we have modified the ad-hoc on demand distance vector routing by incorporating signal-to-noise ratio (SNR) based dynamic clustering. The proposed scheme, which is an efficient and secure routing protocol for wireless sensor networks through SNR-based dynamic clustering (ESRPSDC) mechanisms, can partition the nodes into clusters and select the cluster head (CH) among the nodes based on the energy, and non CH nodes join with a specific CH based on the SNR values. Error recovery has been implemented during the inter-cluster routing in order to avoid end-to-end error recovery. Security has been achieved by isolating the malicious nodes using sink-based routing pattern analysis. Extensive investigation studies using a global mobile simulator have shown that this hybrid ESRP significantly improves the energy efficiency and packet reception rate as compared with the SNR unaware routing algorithms such as the low energy aware adaptive clustering hierarchy and power efficient gathering in sensor information systems.

• Journal of IKEEE
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• v.23 no.4
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• pp.1373-1380
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• 2019

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.505-513
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• 2006

One of the major reasons for recent blackout, like August 14, 2003 blackout in the US and Canada has been insufficient voltage/reactive power support. For the stable reactive power management, a new approach for the voltage monitoring and control structure is required in the market environment. This paper proposes the effective localized-voltage control scheme using the information from pilot buses at each zone. In this paper, the steady state voltage monitoring and control (SSVMC) is adopted and illustrated for the voltage control scheme during steady state because it is thought as the systemic algorithm to explain voltage profile phenomenon before and after contingencies. And the concept of electrical distance is applied to simultaneously achieve both clustering the voltage control zone, and selecting the pilot bus as the representative node at each control zone. Applying SSVMC based on the structure with clustering and pilot bus enables system operators to monitor and understand the system condition much more easily, to monitor and control the voltage in real-time more manageably, and to respond quickly to a disturbance. The proposed voltage control scheme has been tested on the IEEE 14-bus system with the numerical analysis to examine the system reliability and structure efficiency.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.41.1-41.1
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• 2018

We constrain cosmological parameters by combining three different power spectra measured from galaxy clustering, galaxy-galaxy lensing, and cosmic shear using the Deep Lens Survey (DLS). Two lens bins (centered at z~0.27 and 0.54) and two source bins (centered at z~0.64, and 1.1) containing more than one million galaxies are selected to measure the power spectra. We re-calibrate the initial photo-z estimation of the lens bins by matching with SHELS and PRIMUS and confirm its fidelity by measuring a cross-correlation between the bins. We also check the reliability of the lensing signals through the null tests, lens-source flipping and cross shear measurement. Residual systematic errors from photometric redshift and shear calibration uncertainties are marginalized over in the nested sampling during our parameter constraint process. For the flat LCDM model, we determine S_8=sigma_8(Omega_m/0.3)^0.5=0.832+-0.028, which is in great agreement with the Planck data. We also verify that the two independent constraints from the cosmic shear and the galaxy clustering+galaxy-galaxy lensing measurements are consistent with each other. To address baryonic feedback effects on small scales, we marginalize over a baryonic feedback parameter, which we are able to constrain with the DLS data alone and more tightly when combined with Planck data. The constrained value hints at the possibility that the AGN feedback in the current OWLS simulations might not be strong enough.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.79.2-79.2
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• 2019

Based on three types of 2-point statistics (galaxy clustering, galaxy-galaxy lensing, and cosmic shear power spectra) from the Deep Lens Survey (DLS), we constrain cosmology and baryonic feedback. The DLS is a deep survey, so-called a precursor to LSST, reaching down to ~27th magnitude in BVRz' over 20 deg2. To measure the three power spectra, we choose two lens galaxy populations centered at z ~0.27 and 0.54 and two source galaxy populations centered at z ~0.64 and 1.1, with more than 1 million galaxies. We perform a number of consistency tests to confirm the reliability of the measurements. We calibrated photo-z estimation of the lens galaxies and validated the result with galaxy cross-correlation measurement. The B-mode signals, indicative of potential systematics, are found to be consistent with zero. The two cosmological results independently obtained from the cosmic shear and the galaxy clustering + galaxy-galaxy lensing measurements agree well with each other. Also, we verify that cosmological results between bright and faint sources are consistent. While there exist some weak lensing surveys showing a tension with Planck, the DLS constraint on S8 agrees nicely with the Planck result. Using the HMcode approach derived from the OWLS simulation, we constrain the strength of baryonic feedback. The DLS results hint at the possibility that the actual AGN feedback may be stronger than the one implemented in the current state-of-the-art simulations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.841-849
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• 1992

In power system operation, the stability of synchronous machine has been recognized one of the most important things. AESOPS program developed by EPRI in U.S.A. is a frequency domain analysis program in power system stability and it computes the electro-mechanical oscillation mode. This paper presents how to analyze the power system small signal stability problem efficiently by uusing the AESOPS program and analyze the various factors affecting the damping characteristics of these oscillations in KEPCO power system of 1986 with pumped-storage plant. To reduce the computing time and efforts, selecting the poorly-damped oscillation mode and clustering technique have been used. The characteristics of load, the amount of power flow on the transmission line and the gain of exciter have a significant effects on the damping of the system while the governing system has only a minor one. With the Power System Stabilizers, the stability of the power system has been improved.