• Proceedings of the KIEE Conference
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• 2005.07e
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• pp.94-96
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• 2005

Power Quality(PQ) problems are various owing to a wide variety of causes so detection and classification of many kinds of PQ problems are awkward. Almost all studies about it were about getting good results by Neural Networks(NN) which get input features from as random variables, FFT and wavelet transform. However they are discontented with results because it is very difficult to classify all PQ items. A study about feature extraction becomes needed. Thus, this paper suggests effective way of using principle Component Analysis (PCA) for PQ Problem classification. PCA found more effective features among all features so it will help us to get more good result of classification.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.190-194
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• 2002

본 논문에서는 전력품질 분석을 위한 시스템을 설계하였고 평균치를 이용한 기법 중 하나인 Sampling Timer Rate에 의한 방법으로 PQ(Power Quality) 현상을 측정을 하였다. 또한 Internet을 이용하여 전용 Monitoring Software를 통해 원격지에 있는 Field System의 PQ진단을 실시하였다.

• 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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• 2006.07a
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• pp.167-168
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• 2006

This paper proposes a new fault distance calculation method in the power quality (PQ) monitoring system. The proposed method calculates the fault impedance and the fault distance based on the measurement data of the PQ monitors and the information of the topology of the distribution systems. By using the iterative calculation method, the proposed method can find more exact location of the PQ events than the existing methods. The proposed method is applied to the IEEE 34 bus test feeders by using the PSCAD/EMTDC$^{TM}$.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1030-1032
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• 1998

In this paper, an efficient feature vector extraction method and MLP neural network are utilized to automatically detect and classify power quality disturbances, where the proposed classification procedure consists of the following three parts: i.e., (i) PQ disturbance detection using discrete wavelet transform. (ii) feature vector extraction from the detected disturbance. using several methods, such as FFT, DWT, Fisher's criterion. etc.. and (iii) classification of the corresponding type of each PQ disturbance by recognizing the pattern of the extracted feature vector. To demonstrate the performance and, applicability of the proposed classification algorithm. some test results obtained by analyzing 10-class PQ disturbances are also provided.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.671-674
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• 1997

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FIT, DWT(Discrete Wavelet Transform), and Fisher's criterion are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 10-class power quality disturbances are also provided.

• Journal of Electrical Engineering and Technology
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• v.1 no.2
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• pp.170-176
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• 2006

This paper proposes a topological locating algorithm to determine the location of the power quality event source. This algorithm makes use of the information on the topology of the monitored network and on the direction of PQ events. As a result, the bus incidence matrix is modified using monitor location and the direction matrix is constructed. With this information, the algorithm determines the suspected locations of the PQ events. To reduce suspicious areas, it utilizes event cause and related equipment. In case of line fault event, it calculates the distance from the monitor to the location of event source. The overall algorithm is applied to the IEEE test feeder and accurately identifies the event source location.

• Proceedings of the KIEE Conference
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• 2007.10c
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• pp.172-174
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• 2007

An electronic watt-hour meter with high-precision measurement technology can provide many valuable metering data of a real-time system measurements, such as per-phase voltage, ampere, active power, reactive power, apparent power, power factor, and system frequency. Also many of accumulated metering data such as active energy, reactive energy, apparent energy, and load profile can be gettable from an electronic watt-hour meter[1]. This paper presents an approach of the small-sized AMR (Automatic Meter Reading) that provides customers with a very valuable electrical service. This AMR service transmits lots of a valuable metering data by using ZigBee communication module, so that users resided in their premises can use the information to audit a power quality and improve their electrical conditions by using the PQ monitoring device equipped with ZigBee receiver. This PQ monitoring device shows metering data on LCD and transmits to the PC through an internal network. Also, the device can keep the valuable meter data into a built-in non-volatile memory. The final goal of this paper is to better understand the power quality of electrical systems and offer the power qualify information for the convenience of all power consumers.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.404-406
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• 1996

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FFT, DWT(Discrete Wavelet Transform), and data compression are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 7-class power quality disturbances generated by the EMTP are also provided.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.396-398
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• 2006

Predictive maintenance is not an unfamiliar concept because it has been used to predict the failures of electrical equipment such as transformers, motors and so on. By thoroughly monitoring the status of individual equipment and tracing how the various characteristics change over time, we can be aware of its exact condition and prevent the impending failure by taking appropriate actions. In this paper, I will extend the concept of predictive maintenance for individual electrical equipment to the power distribution system and show how to use the data obtained from power quality monitors to improve the power system.