• Nuclear Engineering and Technology
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• v.44 no.5
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• pp.471-482
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• 2012

To assess the risk of nuclear power plant operation and to determine the risk impact of digital systems, there is a need to quantitatively assess the reliability of the digital systems in a justifiable manner. The Probabilistic Risk Analysis (PRA) is a tool which can reveal shortcomings of the NPP design in general and PRA analysts have not had sufficient guiding principles in modelling particular digital components malfunctions. Currently digital I&C systems are mostly analyzed simply and conventionally in PRA, based on failure mode and effects analysis and fault tree modelling. More dynamic approaches are still in the trial stage and can be difficult to apply in full scale PRA-models. As basic events CPU failures, application software failures and common cause failures (CCF) between identical components are modelled.The primary goal is to model dependencies. However, it is not clear which failure modes or system parts CCF:s should be postulated for. A clear distinction can be made between the treatment of protection and control systems. There is a general consensus that protection systems shall be included in PRA, while control systems can be treated in a limited manner. OECD/NEA CSNI Working Group on Risk Assessment (WGRisk) has set up a task group, called DIGREL, to develop taxonomy of failure modes of digital components for the purposes of PRA. The taxonomy is aimed to be the basis of future modelling and quantification efforts. It will also help to define a structure for data collection and to review PRA studies.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.1
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• pp.93-99
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• 2005

Up to date, the life extension of industrial plant structures has been strongly required in the field of iron-manufacturing company, atomic or power generation company and so on. Fault monitoring, maintenance of aging structural components, safety assessment and residual life prediction may be recognized as typical and/or practical methods in terms of life extension methods. Based on the construction of damage scenario, precise analysis method and development of the risk or reliability assessment, a number of studies have been carried out in this viewpoint. In conjunction with the finite element analysis technique, a practical procedure for the safety assessment of iron-manufacturing plant structures was developed in this paper with a particular interest in furnace. By virtue of the detailed finite element analyses for blust furnace under an operational condition, the validity of the proposed procedure for safety assessment was presented.

• Journal of the Korea Convergence Society
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• v.2 no.2
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• pp.47-56
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• 2011

Korea Ministry of Knowledge Economy has estimated that wind power (WP) will be occupied 37% in 2020 and 42% in 2030 of the new energy sources, and also green energies such as photovoltaic (PV) and WP are expected to be interconnected with the distribution system because of Renewable Portfolio Standard (RPS) starting from 2012. However, when a large scale wind power plant (over 3[MW]) is connected to the traditional distribution system, protective devices (mainly OCR and OCGR of re-closer) will be occurred mal-function problems due to changed fault currents it be caused by Wye-grounded/Delta winding of interconnection transformer and %impedance of WP's turbine. Therefore, when Double-Fed Induction Generator (DFIG) of typical WP's Generator is connected into distribution system, this paper deals with analysis three-phase short, line to line short and a single line ground faults current by using the symmetrical components of fault analysis and PSCAD/EMTDC modeling.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.106-117
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• 2016

Recently, solar power plants have spread widely as part of the transition to greater environmental protection and renewable energy. Therefore, regular solar plant inspection is necessary to efficiently manage solar-light modules. This study implemented a test that can detect solar-light module faults using an UAV based thermal infrared camera and GIS spatial analysis. First, images were taken using fixed UAV and an RGB camera, then orthomosaic images were created using Pix4D SW. We constructed solar-light module layers from the orthomosaic images and inputted the module layer code. Rubber covers were installed in the solar-light module to detect solar-light module faults. The mean temperature of each solar-light module can be calculated using the Zonalmean function based on temperature information from the UAV thermal camera and solar-light module layer. Finally, locations of solar-light modules of more than $37^{\circ}C$ and those with rubber covers can be extracted automatically using GIS spatial analysis and analyzed specifically using the solar-light module's identifying code.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.675-680
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance. CMS(Condition Monitoring System) can be used to aid plant operator in achieving these goals. Its aim is to provide operators with information regarding th e health of their machine, which in turn, can help them improve operation efficiency. In this work, wind turbine fault diagnostic algorithm which can diagnose the mass unbalance and aerodynamic asymmetry of the blades is proposed. Proposed diagnostic algorithm utilizes both FFT(Fast Feurier Transform) of the signal from accelerometers installed inside of nacelle and simple diagnostic logic. Furthermore, to verify the applicability of the proposed system, 3W small sized wind turbine system is tested and physical experiments are carried out.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.28 no.1
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• pp.46-50
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• 2015

The cable degradation process is largely divided into three steps; Step 1 : Thermal degradation, Step 2 : Weibull degradation, Step 3 : Partial discharge. it is progress in step order. This article aims to explain the process of cable degradation using the method of insulation resistance and accordingly to compose and manufacture a system of measuring the life of electrical cable. Before measuring the insulation resistance, a system of measuring the temperature and current of cables was made, and the established system was installed for test on the site of a power plant to collect the measured data. The current sensor was used TFC30P80A-CL420, and temperature sensor was used the DK-1270 PT100 sensor as RTD sensor. When measured the temperature and the load current at the same position, was confirmed that in case of the load current value was high, also temperature value high. Therefore, the correlation between load currents and temperature was verified, and the analysis of diagnostic data was evaluated, which could be utilized in identifying the fault condition of cable systems.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.

• Nuclear Engineering and Technology
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• v.50 no.5
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• pp.690-697
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• 2018

Criticality alarm systems (CASs) are mandatory in nuclear plants for prompt alarm in the event of any criticality incident. False criticality alarms are not desirable as they create a panic environment for radiation workers. The present article describes the design enhancement of the CAS at each stage and provides maximum availability, preventing false criticality alarms. The failure mode and effect analysis are carried out on each element of a CAS. Based on the analysis, additional hardware circuits are developed for early fault detection. Two different methods are developed, one method for channel loop functionality test and another method for dose alarm test using electronic transient pulse. The design enhancement made for the external systems that are integrated with a CAS includes the power supply, criticality evacuation hooter circuit, radiation data acquisition system along with selection of different soft alarm set points, and centralized electronic test facility. The CAS incorporating all improvements are assembled, installed, tested, and validated along with rigorous surveillance procedures in a nuclear plant for a period of 18,000 h.

• Journal of Information Technology Services
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• v.20 no.3
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• pp.1-12
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• 2021

Reactor Coolant Pump (RCP) is core part of nuclear power plant to provide the forced circulation of reactor coolant for the removal of core heat. Properly monitoring vibration of RCP is a key activity of a successful predictive maintenance and can lead to a decrease in failure, optimization of machine performance, and a reduction of repair and maintenance costs. Here, we developed real-time RCP Vibration Analysis System (VAS) that web based platform using NoSQL DB (Mongo DB) to handle vibration data of RCP. In this paper, we explain how to implement digital signal process of vibration data from time domain to frequency domain using Fast Fourier transform and how to design NoSQL DB structure, how to implement web service using Java spring framework, JavaScript, High-Chart. We have implement various plot according to standard of the American Society of Mechanical Engineers (ASME) and it can show on web browser based on HTML 5. This data analysis platform shows a upgraded method to real-time analyze vibration data and easily uses without specialist. Furthermore to get better precision we have plan apply to additional machine learning technology.