• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.7
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• pp.449-453
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• 2016

The amount of electrical energy has been increased with the rapid development of the industrial society. Accordingly, operating voltage of the power equipment and facility capacity are continuously increasing. Development trends of recent high-voltage electrical equipment are ultra high-voltage, large-capacity and compact. Early diagnosis of a failure of the power plant has been emerging as an important task as to supply high quality power to users. In this study, we have tried to develope an algorithm for distinguishing an arc fault signal generated in the power plant by using UV sensor.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.673-681
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• 2005

As it is the most important to make sure security and reliability for nuclear Power Plant, it's considered the most crucial issues to develop a fault detective and diagnostic system in spite of multiple hardware redundancy in itself. To develop an algorithm for a fault diagnosis in the nuclear PWR steam generator, this paper proposes a method based on ART2(adaptive resonance theory 2) neural network that senses and classifies troubles occurred in the system. The fault diagnosis system consists of fault detective part to sense occurred troubles, parameter estimation part to identify changed system parameters and fault classification part to understand types of troubles occurred. The fault classification part Is composed of a fault classifier that uses ART2 neural network. The Performance of the proposed fault diagnosis a18orithm was corroborated by applying in the steam generator.

• International Journal of Safety
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• v.11 no.1
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• pp.26-32
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• 2012

This paper introduces a feasibility evaluation method for prognosis systems based on an empirical model in nuclear power plants. By exploiting the dynamical signature characterized by abnormal phenomena, the prognosis technique can be applied to detect the plant abnormal states prior to an unexpected plant trip. Early $operator^{\circ}{\emptyset}s$ awareness can extend available time for operation action; therefore, unexpected plant trip and time-consuming maintenance can be reduced. For the practical application in nuclear power plant, it is important not only to enhance the advantages of prognosis systems, but also to quantify the negative impact in prognosis, e.g., uncertainty. In order to apply these prognosis systems to real nuclear power plants, it is necessary to conduct a feasibility evaluation; the evaluation consists of 4 steps (: the development of an evaluation method, the development of selection criteria for the abnormal state, acquisition and signal processing, and an evaluation experiment). In this paper, we introduce the feasibility evaluation method and propose further study points for applying prognosis systems from KHNP's experiences in testing some prognosis technologies available in the market.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.281-286
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• 1992

This paper describes a duplexed process control system for a boiler-turbine system of power plant, which is composed of supervisor, controller, and simulater subsystems. Its embeded POL(Problem oriented language) as a structured control language enables this system to do a real-time distributed control and fault diagnosis by simple programming with the actual implementation of advanced control algorithms such as PID autotuning and GPC, etc, the performance of overall system has been greatly enhanced.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.4
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• pp.168-172
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• 2007

The global environment deteriorating which originated from using of fossil fuel is an serious problem for human being to solve. The photovoltaic energy has been considered as a solution. In advanced countries, research and development for photovoltaic(PV) energy system is carrying on. Once installed, a PV array requires maintenance and fault diagnosis other than an occasional cleaning. In this research, the proposed system monitor and diagnosis the output of PV array by on-line for maintenance of PV power plant. The validity of proposed system is verified using sample system.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Journal of KSNVE
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• v.10 no.1
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• pp.129-137
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• 2000

This paper presents the vibration characteristics of reactor internals of Ulchin-1 nuclear power plant, which are identified by using the conventional and the phase separated spectral analysis of the pressure vessel acceleration and ex-core neutron signals. These identified vibration characteristics show excellent agreement with those of Tricastin-1 nuclear power plant that is the prototype of Ulchin-1. And the trend of ex-core neutron signals has been observed during one reactor cycle. These results can be used as basic data for fault diagnosis of reactor internals.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.86.5-86
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• 2002

The measurement sensor may contain unknown bias in addition to the white noise in the measurement sequence. In this paper, fault detection and diagnosis scheme for the measurement sensor is developed based on the adaptive estimator. The proposed scheme consists of a parallel bank of Kalman-type filters each matched to a set of different possible biases, a mode probability evaluator, an estimate combiner at the outputs of the filters, a bias estimator, and a fault detection and diagnosis logic. Monte Carlo simulations for the PWR steam generator in the nuclear power plant are provided to illustrate the effectiveness of the proposed scheme.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.644-647
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• 2003

The predictive maintenance can help to avoid the serious plant breakdowns and catastrophies. This paper deals with the fault diagnosis of the rotor of the induction motor which is widely used in the plants. In order to detect the broken bar, the Extended Kalman Filter is adopted to estimate the rotor resistance on the base of model-based method. The proposed estimation method is simulated with the aid of Matlab.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3512-3517
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• 2007

Nuclear power plant has many external fire hydrants that have to operate in the state of emergency such as facility fire, forest fire. The valve stem of one among them was broken 3 times for 4 years. It had long valve stem and operated under high water pressure. The elongation and the tensile strength for the broken valve stem was measured to examine the defect of material property. And the vibration level and the natural frequencies was detected to check the resonance. As the result of a diagnosis, the cause of this fault is proven buckling of long valve stem.