• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.423-431
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• 2022

The UPS(Uninterruptible Power Supply) system operates in the battery charging mode when the grid is normal, and in the UPS mode, which is the battery discharge mode when a grid error occurs. Since the UPS must supply the same voltage as the grid to the load within 4 [ms] in case of a grid error, the switching time and power recovery time should be short when controlling the output voltage and current of the UPS, and the power failure detection time is also important. The power outage detection algorithm using DFT(Discrete Fourier Transform) proposed in this paper compares the grid voltage waveform with the voltage waveform including the 9th harmonic generated through DFT using Schmitt trigger to detect power outage faster than the existing power outage monitoring algorithm. There are advantages. Therefore, it is possible to supply instant and stable power when switching modes in the UPS system. The multi-power-applied UPS system proposed in this paper uses DFT, which is faster than the conventional blackout monitoring algorithm in detecting power failure, to provide stable power to the load in a shorter time than the existing power outage monitoring algorithm when a system error occurs. The detection method was applied. The changeover time of mode switching was set to less than 4 [ms], which is 1/4 of the system cycle, in accordance with KSC 4310 regulation, which was established by the Industrial Standards Council on the regulation of uninterruptible power supply. A 10 [kW] UPS system in which commercial voltage, vehicle generator, and auxiliary diesel generator can be connected to each of the proposed transfer devices was constructed and the feasibility was verified by conducting an experiment.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.144-155
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• 2013

This paper presents a low-cost remote power-quality-failure monitoring system (RPMS) using Android App and TI MCU (micro-controller unit), which is appliable to a micro-grid. The designed RPMS testbed consists of smart nodes, a server, and Android APPs. Especially, the C2000-series MCU-based RPMS smart node that is low-cost compared to existing monitoring systems has both a signal processing function for power signal processing and a data transmission function for power-quality monitoring data transmission. The signal processing function implements both a wavelet-based power failure detection algorithm including sag, swell, and interruption, and a FFT-based power failure detection algorithm including harmonics such that reliable and real-time power quality monitoring is guaranteed. The data transmission function implements a low-complexity RPMS transmission protocol and defines a simple data format (msg_Diag) for power monitoring message transmission. We may watch the monitoring data in real time both at a server and Android phone Apps connected to the WiFi network (or WAN). We use RS-232 (or Bluetooth) as the wired (or wireless) communication media between a server and nodes. We program the RPMS power-quality-failure monitoring algorithm using C language in the CCS (Code Composer Studio) 3.3 environment.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.80-83
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• 2022

This paper proposes an initial rotor polarity detection algorithm of a single-phase permanent magnet synchronous motor (SP-PMSM) related to stable open-loop starting for sensorless operation. Generally, the SP-PMSM needs an asymmetric air-gap structure to can avoid the initial starting failure at zero torque point. Therefore, the rotor polarity information can be obtained by using the DC offset current direction of a stator current through a high frequency voltage injection into an SP-PMSM with an asymmetric air gap. In this paper, the proposed rotor initial polarity detection algorithm is verified through several experimental results.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.7
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• pp.508-516
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• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. Failure modes in this study include refrigerant leakage, decrease in mass flow rate of the chilled water and cooling water, and sensor error of the cooling water inlet temperature. It is possible to detect and diagnose faults in this study by adopting FDD algorithm using only four parameters(compressor outlet temperature, chilled water inlet temperature, cooling water outlet temperature and compressor power consumption). Refrigerant leakage failure is detected at 20% of refrigerant leakage. When mass flow rate of the chilled and cooling water decrease more than 8% or 12%, FDD algorithm can detect the faults. The deviation of temperature sensor over $0.6^{\circ}C$ can be detected as fault.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.376-382
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• 2014

E-Actuator is an essential part of an eVGT, it receives the command from the main ECU and controls the vane. An e-Actuator failure can cause an abrupt change in engine output and it may induce an accident. Therefore, it is required to detect anomalies in the e-Actuator in real time to prevent accidents. In this paper, an e-Actuator fault detection method using on-line parameter identification is proposed. To implement on-line fault detection algorithm, many constraints are considered. The test input and sampling rate are selected considering the constraints. And new recursive system identification algorithm is proposed which reduces the memory and MCU power dramatically. The relationship between the identified parameters and real elements such as gears, spring and motor are derived. The fault detection method using the relationship is proposed. The experiments with the real broken gears show the effectiveness of the proposed algorithm. It is expected that the real time fault detection is possible and it can improve the safety of eVGT system.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.53-62
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• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.9
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• pp.449-456
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• 2005

The preventive diagnostic technique prevents transformers from power failure through giving alarm and observing transformers in service. And it helps to establish the plan for optimum maintenance of the transformer as well as to find location or cause of fault using accumulated data. Data detection and experience of the preventive diagnostic system need to establish the preventive diagnostic algorithm regarding interrelationship between detected data and deterioration of equipment. Therefore in-depth analysis about the preventive diagnosis system is required. KEPCO has adopted the preventive diagnostic system at nine 345kV substations since 1997. Techniques for component sensors of the preventive diagnosis system were settled but diagnosis algorithm, diagnostic criteria and practical use of accumulated data are not yet established. This paper, to build up the base of preventive diagnostic algorithm for the Power transformer. investigated the preventive diagnostic criteria for the power transformer.

• Nuclear Engineering and Technology
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• v.36 no.6
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• pp.540-548
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• 2004

Check valve failure is one of the worst problems in nuclear power plants. Recently, many researches have been based on new technology using accelerometers and ultrasonic and magnetic flux detection have been carried out. Here, we have suggested a method that uses acoustic emission sensors for detecting the failures of check valves through measuring and analyzing backward leakage flow, a system that works without disassembling the check valve. For validating the suggested acoustic emission sensor methodology, we designed a hydraulic test loop with a check valve. We have assumed in this study that check valve failure is caused by disk wear or by the insertion of a foreign object. In addition, we have developed diagnostic algorithms by using a neural network model to identify the type and size of the failure in the check valve. Our results show that the proposed diagnostic algorithm with acoustic emission sensors is a good solution for identifying check valve failure without necessitating any disassembly work.