• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.3
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• pp.282-289
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• 2005

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also speed estimation using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1375-1384
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• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.28 no.5
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• pp.320-325
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• 2015

This paper dealt with a defect identification algorithm which is based on single partial discharge (PD) pulse analysis in gas insulated structure. Four types of electrode systems such as a needle-plane, a plane-needle, a free particle and a crack inside spacer were fabricated to simulate defects in gas insulated switchgear (GIS). We measured single PD pulse by an oscilloscope with a sampling rate of 5 GS/s and a frequency bandwidth of 1 GHz. Data aquisition and signal processing were controlled by a LabVIEW program. Physical shapes of PD pulses were compared with kurtosis, skewness and time-based parameters as rising time, falling time and pulse-width. These parameters were analysed by an algorithm with a back propagation algorithm (BPA). By applying the algorithm, the identification rate was 97% for the needle-plane electrode, 96% for the plane-needle electrode, 91% for the free particle and 93% for the crack inside spacer. The results verified that the algorithm could identify the type of defects in GIS.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.77-82
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• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.389-398
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• 2006

The present study compositely applied the self-organizing map (SOM), which is a kind of artificial neural networks (ANNs), and the back propagation algorithm (BPA) for the rainfall-runoff prediction model taking account of the irregular variation of the spatiotemporal distribution of rainfall. To solve the problems from the previous studies on ANNs, such as the overestimation of low flow during the dry season, the underestimation of runoff during the flood season and the persistence phenomenon, in which the predicted values continuously represent the preceding runoffs, we introduced SOM theory for the preprocessing in the prediction model. The theory is known that it has the pattern classification ability. The method proposed in the present research initially includes the classification of the rainfall-runoff relationship using SOM and the construction of the respective models according to the classification by SOM. The individually constructed models used the data corresponding to the respectively classified patterns for the runoff prediction. Consequently, the method proposed in the present study resulted in the better prediction ability of runoff than that of the past research using the usual application of ANNs and, in addition, there were no such problems of the under/over-estimation of runoff and the persistence.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also this paper is proposed. speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. The back Propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.216-218
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• 2006

The paper is proposed high performance control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper is proposed speed control of induction motor using AFLC and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and ANN controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.