• Current Photovoltaic Research
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• v.10 no.3
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• pp.77-83
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• 2022

Renewable energy is receiving attention again as a way to realize carbon neutrality to overcome the climate change crisis. Among renewable energy sources, the installation of Photovoltaic is continuously increasing, and as of 2020, the global cumulative installation amount is about 590 GW and the domestic cumulative installation amount is about 17 GW. Accordingly, O&M technology that can analyze the power generation and fault diagnose about PV plants the is required. In this paper, a study was conducted to diagnose fault using I-V curves of PV strings and deep learning. In order to collect the fault I-V curves for learning in the deep learning, faults were simulated. It is partial shade and voltage mismatch, and I-V curves were measured on a sunny day. A two-step data pre-processing technique was applied to minimize variations depending on PV string capacity, irradiance, and PV module temperature, and this was used for learning and validation of deep learning. From the results of the study, it was confirmed that the PV fault diagnosis using I-V curves and deep learning is possible.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.613-622
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• 2015

The convergence of IT technology and marine facilities monitoring system is needed for effective monitoring systems to marine facilities. Especially the spread of smart device such as smart phone, smart pad, smart TV provide an environment that can check the status of the marine facility for marin facilities manager. However, smart phones and smart pads are used in a variety of OS used. Thus the monitoring system of the various service environments is difficult. In addition, There is inconvenience that must individually developed monitoring system for each device. In order to solve this problem NMMS (N-Screen Marine-facility Monitoring System) is proposed. NMMS is consist of Real-time monitoring system, Fault diagnosis system, Data storage system. To improve variety of smart devices accessibility, we use HTML 5. Through NMMS, marine facilities manager can use smart device such as PC, Notebook, smart phone, smart pad for marine facilities monitoring.

• 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 Korea institute for structural maintenance and inspection
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• v.12 no.1
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• pp.91-100
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• 2008

Multi-stepwise Thermal Prestressing Method(TPSM) is a newly proposed prestressing method, which is combined the external prestressing method and the external bonding method. Multi-stepwise thermal prestressing force is induced by cooling process of cover-plate in the multi-stepwise temperature distribution after the cover-plate being bolted to the girder. In this study, the heating capacity test of the developed heating system for applying the multi-stepwise TPSM effectively and multi-stepwise TPSM inducing test of H-beam is performed. Also, a field test of the rhamen type temporary bridge is carried out to evaluate the effect and application of the multi-stepwise TPSM. Truck load was loaded and compared with the structure analysis results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.205-211
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• 2005

Recently, power system is getting larger and more complex. When the complex power system has a problem, it is very difficult even for the experts to find out where the problem is and to make a timely decision by operators. There have been many studies on these problems but the results are not good enough for applying to real power system. Therefore, power system operators always had to judge the exact state of power system and be preparative for the problems that can occur later. We developed new methods that can be applied to complex power system by dividing the system into small modules. By using 'module', we can combine small modules together to make complex power systems and the knowledge base that is applied to fault diagnosis system. As a result, compared to previously developed diagnosis products, operation time is shortened and the knowledge base is become simpler and clearer, which made online usage capable. This system can be used as a complementary measurement that helps the operator from making any mistakes.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.8
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• pp.18-24
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• 2011

A new problem happens with the evolution of TSV based 3D IC design. The bonding process takes place which follows with the testing of design for proper connectivity in the absence of TSV redundancy. In order to achieve good yield, the design should be tested with redundancy TSV. This paper presents a wrapper cell design for redundancy TSV interconnect test. The design for test technique, in terms of hardware and software perspectives is described. The wrapper cell with hardware design can use original test patterns. However, software design has less area overhead.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.29-30
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• 2018

전 세계적으로 급증하고 있는 에너지 수요로 인해 오일, 가스 등의 에너지 생산 매체를 운송 할 수 있는 수송 수단인 배관에 대한 수요도 증가하고 있는 추세이다. 그러나 이러한 배관을 장시간 사용할 경우 노후화로 인해 발생되는 파공은 배관에 흐르는 유체 누설의 원인이 되고, 나아가서 경제 및 재난 피해를 야기할 수 있다. 따라서 다양한 배관 누설 검출 기술들이 개발되고 있는 추세이며, 이를 위해서는 신뢰성 있는 결함 모사 데이터의 확보가 매우 중요하다. 본 논문에서는 신뢰성 있는 데이터 수집을 위해 배관의 누설 상황을 모사 할 수 있는 테스트베드를 제어하고, 테스트베드에서 데이터를 안정적으로 취득할 수 있는 테스트베드 제어 및 데이터 취득 시스템을 제안한다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.52-60
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• 1999

This paper deals with a fault detection and isolation scheme for a DC motor driven centrifugal pump system. The emphasis is placed on the design and implementation of the residual generatorm, based on parity relation, that provides decision logic unit with residuals that will be further processed to detect and isolate three important faults in the system;brush fault, impeller fault, and the speed sensor fault. Two process faults are modelled as multiplicative type faults, while the sensor fault as an additive one. With multiplicative fault, the implementation of the residual generator needs the time varying transformation matrix that must be computed on-line. Typical implementation methods lack in generality because only a numerical approximation around the assumed fault levels is employed. In this paper, a new implementation method using well tranined neural network is proposed to improve the generality of the residual generator. Application results show that the fault detection and isolation scheme with the proposed residual generator effectively isolates three major faults in the centrifugal pump system even with a wide range of fault magnitude.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.83-89
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• 2022

Internet of Things technology is rapidly developing due to the recent development of information and communication technology. IoT technology utilizes various sensors to generate unique data from each sensor, enabling diagnosis of system status. However, the equipment management system currently in effect is a post-preservation concept in which administrators must deal with the problem after the problem occurs, which could mean system reliability and availability problems due to system errors, and could result in economic losses due to negative productivity disruptions. Therefore, this study confirmed that edge controller control decision algorithms for more efficient operation of rectifiers in the factory by applying intelligent IoT (AIoT) technology and domain knowledge-based modeling for each sensor data collected based on this, outputting appropriate status messages for each scenario.