Seong-Hee Park

Comparison of Classification Rate for PD Sources using Different Classification Schemes

Insulation failure in an electrical utility depends on the continuous stress imposed upon it. Monitoring of the insulation condition is a significant issue for safe operation of the electrical power system. In this paper, comparison of recognition rate variable classification scheme of PD (partial discharge) sources that occur within an electrical utility are studied. To acquire PD data, five defective models are made, that is, air discharge, void discharge and three types of treeinging discharge. Furthermore, these statistical distributions are applied to classify PD sources as the input data for the classification tools. ANFIS shows the highest rate, the value of which is 99% and PCA-LDA and ANFIS are superior to BP in regards to other matters.

Characteristics of Disk-type Linear Ultrasonic Motor for Application to x-y Stage

In this paper, a disk-type ultrasonic motor using a combination of radial and bending vibration modes is newly designed and fabricated. The characteristics of the test motor are also measured. By means of traveling elastic wave induced at the surface of circumference of the elastic disk, a steel bar in contact with the surface of circumference of the elastic disk bonded onto the piezoelectric ceramic disks is driven in both directions by changing the sine and cosine voltage inputs. The stator of the motor is composed of two sheets of piezoelectric ceramic disks to bond onto both surfaces of an elastic disk, respectively. As a result, the diameter of the elastic body is increased and the resonant frequency is decreased. The resonant frequency of the stator is about 92 ㎑, which is composed with piezoelectric ceramic disks of 28 ㎜ in diameter and 2 ㎜ in thickness, and an elastic body of 32 ㎜ in diameter and 2 ㎜ in thickness. A driving voltage of 20 Vpp produces 200 rpm with a torque of 1 Nm and an efficiency of about 10 %.

Classification of defects and evaluation of electrical tree degradation in cable insulation using pattern recognition method and weibull process of partial discharge

The purpose of this paper is to recognize partial discharge (PD) sources and evaluate of electrical treeing degradation for cable insulation. To acquire PD data, three defective tree models were made. And the data are shown by the phase-resolved partial discharge method (PRPD). As a result of PRPD, tree discharge sources have their own characteristics. If other defects (void, metal particle) exist at internal power cable, their characteristics are shown very differently. This result is related to the time of breakdown and this is important point of cable diagnosis. To apply PD data classification, methods of different type are selected. Those are, multi layer perceptions (MLP-BP), adaptive neuro-fuzzy inference system (ANFIS) and principle component analysis-linear inference system(PCA-LDA). As a result, ANFIS shows the highest rate which value is 100 %. Finally, we performed classification of tree progress using ANFIS and that result is 99 %. To evaluate degradation of electrical tree, weibull distribution was used. The time of each degradation stage (initiation, middle, breakdown) was measured to classify electrical tree degradation with each model by. Using the result, parameters are presumed by the each model and stage and it is possible to calculate time difference of each degradation stage and estimate the lifetime.

Characteristics of π-shaped Ultrasonic Motor

In this paper, the design and characteristics of a Π-shaped ultrasonic motor that is applicable to optical zoom operation of a lens system for mobile phones are investigated. Its design and simulation of performances are carried out by FEM (finite element method) commercial software. As a simulation result, by applying voltage with single phase, a combined vibration is produced at the surface of a stator arm. A prototype of the motor is fabricated and its outer size is 8*4*2 ㎣ including the cylindrical steel rod of 2 ㎜ in diameter as the rotor. The motor exhibits a maximum speed of 500 rpm and a power consumption of 0.3 W when driven at 20 Vpp and 64 ㎑.

Comparison of recognition rates between BP and ANFIS with FCM clustering method on off-line PD diagnosis of defect models of traction motor stator coil

In this paper, we compared recognition rates between NN (neural networks) and clustering methods as a scheme of off-line PD (partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for recognition were acquired from PD detector. And then statistical distributions were calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP (back propagation algorithm) of NN and ANFIS (adaptive network based fuzzy inference system) using FCM (fuzzy clustering means) methods. So, classification rates of BP were somewhat higher than ANFIS performed preprocessing clustering method. But other items of ANFIS were better than BP; learning time, parameter number, capability on field, simplicity of algorithm.

Evaluation of Electrical Tree Degradation in Cable Insulation using Weibull Process of Propagation Time

The main purpose of this paper is to evaluate electrical treeing degradation for cable 11 insulation. To effectively deal with the currently facing issues, I endeavor to find the most optimal 12 methods by means of applying signal process. First, we made three type models of electrical tree for 13 PD generation to show the distribution characteristics and applied voltage to acquire data by using 14 a PD detecting system. These acquired data presented distribution and four 2D distributions. Hn(q), 15 Hn(Φ), Hqn(Φ), and Hqmax(Φ) were derived from the distribution of partial discharge. From the 16 analysis of these distributions, each PD model is proved to hold its unique characteristics and the 17 results were then applied as basic specific qualities for insulation conditions. In order to recognize 18 the progresses of an electrical tree, we proposed methods using scale parameter by means of Weibull 19 distribution. We measured the time of tree propagation for 16 specimens of each model from 20 initiation stage, middle stage, and breakdown respectively, using these breakdown data, we 21 estimated the shape parameter, scale parameter and MTTF (Mean Time To Failure). The results of 22 this study recognize the sources of PD by applying acquired data from PD signals to pre-acquired 23 data. If the cause of PD is degradation, in other words, electrical tree, we can determine the 24 replacement time of devices at the initiation stage of tree growth progress or no later than the middle 25 stage and use it as a basic methods analysis diagnosis system. That is, pattern recognition and 26 Weibull distribution can be employed to get the reliability of diagnosis. 27

Analysis of PD Distribution Characteristics and Comparison of Classification Methods according to Electrical Tree Source in Power Cable

One of the cause of insulation failure in power cable is well known by electrical treeing discharge. This is occurred for imposed continuous stress at cable. And this event is related to safety, reliability and maintenance. In this paper, throughout analysis of partial discharge(PD) distribution when occurring the electrical tree, is studied for the purpose of knowing of electrical treeing discharge characteristics according to defects. Own characteristic of tree will be differently processed in each defect and this reason is the first purpose of this paper. To acquire PD data, three defective tree models were made. And their own data is shown by the phase-resolved partial discharge method (PRPD). As a result of PRPD, tree discharge sources have their own characteristics. And if other defects (void, metal particle) exist internal power cable then their characteristics are shown very different. This result Is related to the time of breakdown and this is importance of cable diagnosis. And classification method of PD sources was studied in this paper. It needs select the most useful method to apply PD data classification one of the proposed method. To meet the requirement, we select methods of different type. That is, neural network(NN-BP), adaptive neuro-fuzzy inference system and PCA-LDA were applied to result. As a result of, ANFIS shows the highest rate which value is 98 %. Generally, PCA-LDA and ANFIS are better than BP. Finally, we performed classification of tree progress using ANFIS and that result is 92 %.

Application of NN as a scheme of off-line PD diagnosis method on traction motor stator coil

Insulation failure of traction motor stator coil depends on the continuous stress imposed on it and knowing the insulation condition is important for safe operation. In this paper, application of NN (neural network) as a scheme of off-line PD (partial discharge) diagnosis method, which occurs at the stator coil of traction motor, was studied. For PD data acquisition, three defective models are made; internal void discharge model, slot discharge model and surface discharge model. PD data for recognition were acquired from PD detector. Statistical distributions and parameters are calculated to distinguish between model discharge sources. Also these statistical distribution parameters are applied to classify PD sources by NN, with a good recognition rate on the discharge sources.

An Application of NN on Off-line PD Diagnosis to Stator Coil of Traction Motor

In this study, PD(partial discharge) signals which occur at stator coil of traction Motor are acquired these data are used for classifying the PD sources. NN(neural network) has recently applied to classify the PD pattern. The PD data are used for the learning process to classify PD sources. The PD data come from normal specimen and defective specimens such as internal void discharges, slot discharges and surface discharges. PD distribution parameters are calculated from a set of the data, which is used to realize diagnostic algorithm. NN which applies distribution parameters is useful to classify the PD patterns of defective sources generating in stator coil of traction motor