• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.69-71
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• 1994

The objective of this paper is to present a systematic approach to detect and classify automatically Power Quality Disturbances by applying the recent advances in digital signal processing techniques including wavelet theory and neural networks. To demonstrate the validity of the derived result, computer simulation results are included.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.619-622
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• 2001

In this paper, an efficient approach to classification of transient and harmonic disturbances in power systems is proposed. First, the Stop-and-Go CA CFAR Detector is utilized to detect a disturbance from the power signals which are mixed with other disturbances and noise. Then, (i) Wigner Distribution, SVD(Singular Value Decomposition) and Fisher´s Criterion (ii) DWT and Fisher´s Criterion, are applied to extract an efficient feature vector. For the classification procedure, a combined neural network classifier is proposed to classify each corresponding disturbance class. Finally, the 10 class data simulated by Matlab power system blockset are used to demonstrate the performance of the proposed classification system.

• Journal of Power Electronics
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• v.9 no.3
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• pp.386-395
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• 2009

In this paper, a new control algorithm for the dynamic voltage restorer (DVR) is proposed to regulate the load terminal voltage during various power quality problems that include sag, swell, harmonics and unbalance in the voltage at the point of common coupling (PCC). The proposed control strategy is an Adaline (Adaptive linear element) Artificial Neural Network (ANN) and is used to control a capacitor supported DVR for power quality improvement. A capacitor supported DVR does not need any active power during steady state because the voltage injected is in quadrature with the feeder current. The control of the DVR is implemented through derived reference load terminal voltages. The proposed control strategy is validated through extensive simulation studies using the MATLAB software with its Simulink and SimPower System (SPS) toolboxes. The DVR is found suitable to support its dc bus voltage through the control under various disturbances.

• Journal of Power Electronics
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• v.6 no.4
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• pp.307-314
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• 2006

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.774-775
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• 2011

Recently, by increasing of devices which are sensitive to power quality, microprocessor and power electronics, the deterioration of power quality is accelerated. Accordingly, various methods for analysis, detection, compensation and countermeasure on power quality are proposed. For this, the study of distinction of various disturbances and fault which is occurred in power system is necessary to prior. Therefore, in this paper, the classification algorithm among steady state and single line to ground fault which is commonly occurred fault is proposed using S-Transform.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.858-861
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• 2004

This paper presents a wavelet-based neural network technology for the detection and classification of the various types of power quality disturbances. Power quality phenomena are short-time problems and of many varieties. Particularly, the transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of WN (PQ-DAS) and some case studies are described.

• Journal of the Korean Solar Energy Society
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• v.32 no.5
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• pp.1-10
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• 2012

This paper proposes a modeling and controller design approach for a wind-diesel hybrid system including dump load. Wind turbine depends on nature such as wind speed. It causes power fluctuations of wind turbine. Excessive power fluctuation at stand-alone power grid is even worse than large-scale power grid. The proposed control scheme for power quality is fuzzy PI controller. This controller has advantages of PI and fuzzy controller. The proposed model is carried out by using Matlab/Simulink simulation program. In the simulation study, the proposed controller is compared with a conventional PI controller. Simulation results show that the proposed controller is more effective against disturbances caused by wind speed and load variation than the PI controller, and thus it contributes to a better quality wind-diesel hybrid power system.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1099-1101
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• 2007

In this paper, a study has been performed for development of dynamic voltage restorer(DVR) system using EDLC (Electric Double Layer Capacitor) to solve voltage sags which are considered the dominant disturbances affecting power quality. With the prolific use of semiconductor devices in electrical equipment, modern-day loads are becoming increasingly sensitive to the sags and the disturbances prove to be costly to industries. As a technology-driven custom power installation, the Dynamic Voltage Restorer (DVR) is recognized to be most effective equipment that can be used to counter the voltage sag problem. Hence, this paper proposes the high-performance optimal design of On-Line type DVR system which can efficiently compensate the instantaneous voltage sag and instantaneous interruption of the utility voltage source.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.670-676
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• 2008

While the matrix converter has many advantages that include bi-directional power flow, a size reduction, a long lifetime, and sinusoidal input currents, it is vulnerable to the input voltage disturbances, because it directly exchanges the input voltage to the output voltage. So, in this paper, a critical evaluation of the effect of various abnormal voltage conditions like unbalanced power supply, balanced non-sinusoidal power supply, input voltage sags and short time blackout of power supply on matrix converter fed induction motor drives is presented. The operation under various abnormal conditions has been analyzed. For this, a 230V, 250VA three phase to three phase matrix converter (MC) fed induction motor drive prototype is implemented using DSP based controller and tests have been carried out to evaluate and improve the stability of system under typical abnormal conditions. Digital storage oscilloscope & power quality analyzer are used for experimental observations.

• Women's Health Nursing
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• v.28 no.4
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• pp.296-306
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• 2022

Purpose: This study investigated the effects of peripheral neuropathy symptoms, self-care ability, and disturbances to daily life on quality of life (QoL) among gynecological cancer patients undergoing chemotherapy. Methods: The participants included 144 patients with gynecological cancer undergoing anticancer chemotherapy at a tertiary hospital in Seoul, South Korea, from December 1, 2021 to January 28, 2022. Convenience sampling was used to recruit patients who had received 4 or more cycles of chemotherapy using a paclitaxel-platinum regimen, and a self-reported questionnaire was used to collect data. Descriptive statistics, the t-test, analysis of variance, Scheffé test, Pearson correlation coefficients, and multiple regression analysis were performed. Results: Most of the participants had ovarian cancer (70.1%) or endometrial cancer (14.6%), and the most common number of treatment cycles was 6 to 10 (29.2%). The mean QoL (60.83±19.89) was greater than the midpoint. The regression model analyzing the patients' QoL was statistically significant (F=15.38, p<.001) with an explanatory power of 56.7%. Self-care ability (β=.39, p<.001), disturbances to daily life (β=-.38, p<.001), the duration of peripheral neuropathy symptoms (β=2.14, p=.034), and regular exercise (β=-2.12, p=.036) were found to significantly affect QoL. Conclusion: Efforts to improve the self-care ability of gynecological cancer patients who have experienced peripheral neuropathy after receiving chemotherapy and mitigate disturbances to their daily life can improve their QoL. Healthcare professionals should identify peripheral neuropathy symptoms and examine the effects of the symptoms on patients' daily lives. Improving the self-care ability of patients and alleviating their limitations in daily life may improve QoL.