• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.157.2-157
• /
• 2001

This paper describes the synergism of Artificial Neural Network and Fuzzy Logic based approach to improve the reliability of transformer differential protection, the conventional transformer differential protection commonly used a harmonic restraint principle to prevent a tripping from inrush current during initial transformer´s energization but such a principle can not performs the best optimization on tripping time. Furthermore, in some cases there may be false operation such as during CT saturation, high DC offset or harmonic containing in the line. Therefore an artificial neural network and fuzzy logic has been proposed to improve reliability of the transformer protection relay. By using EMTP-ATP the power transformer is modeled, all currents flowing ...

• Journal of Electrical Engineering and Technology
• /
• 제9권6호
• /
• pp.1822-1831
• /
• 2014

Micro-grid is connected to the main power grid through a static switch. One of the critical issues in micro-grids is protection which must disconnect the micro-grid from the network in short-circuit contingencies. Protective methods of micro-grid mainly follow the model of distribution system protection. This protection scheme suffers from improper operation due to the presence of single-phase loads, imbalance of three-phase loads and occurrence of power swings in micro-grid. In this paper, a new method which prevents from improper performance of static micro-grid protection is proposed. This method works based on artificial neural network (ANN) and able to differentiate short circuit from power swings by measuring impedance and the rate of impedance variations in PCC bus. This new technique provides a protective system with higher reliability.

• 한국해양공학회지
• /
• 제30권2호
• /
• pp.134-140
• /
• 2016

An envelope protection system is a control system that allows a submarine to operate freely using its own operational envelope without exceeding the structural limit, dynamic limit, and control input limit. In this paper, an envelope protection system for the pitch angle of a submarine is designed using a dynamic trim algorithm. A linear quadratic regulator and artificial neural network are used for the true dynamics approximation. A submarine maneuvering simulation program developed using experimental data is used to validate the designed envelope protection system. Simulation results show the effectiveness of the designed envelope protection system.

• 토지주택연구
• /
• 제11권4호
• /
• pp.99-104
• /
• 2020

This paper presents the recent research developments identified through a review of literature on the application of artificial intelligence in developing automated designs of fire protection facilities. The literature review covered research related to image recognition and applicable neural networks. Firstly, it was found that convolutional neural network (CNN) may be applied to the development of automating the design of fire protection facilities. It requires a high level of object detection accuracy necessitating the classification of each object making up the image. Secondly, to ensure accurate object detection and building information, the data need to be pulled from architectural drawings. Thirdly, by applying image recognition and classification, this can be done by extracting wall and surface information using dimension lines and pixels. All combined, the current review of literature strongly indicates that it is possible to develop automated designs for fire protection utilizing artificial intelligence.

• Nuclear Engineering and Technology
• /
• 제54권1호
• /
• pp.269-274
• /
• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• Nuclear Engineering and Technology
• /
• 제30권3호
• /
• pp.222-234
• /
• 1998

The Westinghouse OT$\Delta$T DNB protection logic heavily restricts the operation region by applying the same logic for a full range of operating pressure in order to maintain its simplicity. In this work, a fuzzy neural network method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. Fuzzy system parameters are optimized by a hybrid learning method. This algorithm uses a gradient descent algorithm to optimize the antecedent parameters and a least-squares algorithm to solve the consequent parameters. The proposed method is applied to Yonggwang 3&4 nuclear power plants and the proposed method has 5.99 percent larger thermal margin than the conventional OT$\Delta$T trip logic. This simple algorithm provides a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.

• 대한전기학회논문지:전력기술부문A
• /
• 제49권8호
• /
• pp.405-411
• /
• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper propolsed the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Journal of information and communication convergence engineering
• /
• 제19권1호
• /
• pp.22-28
• /
• 2021

Convolutional neural networks (CNNs) are one of the most frequently used artificial intelligence techniques. Among CNN-based applications, small and timing-sensitive applications have emerged, which must be reliable to prevent severe accidents. However, as the small and timing-sensitive systems do not have sufficient system resources, they do not possess proper error protection schemes. In this paper, we propose MATE, which is a low-cost CNN weight error correction technique. Based on the observation that all mantissa bits are not closely related to the accuracy, MATE replaces some mantissa bits in the weight with error correction codes. Therefore, MATE can provide high data protection without requiring additional memory space or modifying the memory architecture. The experimental results demonstrate that MATE retains nearly the same accuracy as the ideal error-free case on erroneous DRAM and has approximately 60% accuracy, even with extremely high bit error rates.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1637-1640
• /
• 2005

This paper presents a technique for electromagnetic field analysis on surge response due to Mid-span back-flashovers effects in lightning protection system of 500 kV EHV single circuit transmission tower by the neural networks method. These analyses are based on modeling lightning return stroke as well as on coupling the electromagnetic fields of the stroke channel to the line. The ground conductivity influences both the electric field as well as the coupling mechanism and hence the magnitude and wave shape of the induced voltage. The technique can be used to analyzed the corona voltage effect, the effective of stroke to the span tower, the surge impedance of transmission lines. The maximum voltage from flashovers effects in the lines. The model is compatible with general electromagnetic transients programs such as the ATP-EMTP. The simulation results show that this study analyses for time-domain with those produced by a cascade multi-section model, the surge impedance of a full-sized tower hit directly by a lightning stroke is discussed.

• Journal of Radiation Protection and Research
• /
• 제32권4호
• /
• pp.168-177
• /
• 2007

본 논문에서는 노심설계 단계에서 선정된 다양한 노심 장전모형 중에서 압력용기 중성자 조사취화 관점에서 가장 최적의 노심 장전모형을 선정할 수 있도록 신속하게 압력용기 취약위치에 대한 속중성자속을 예측할 수 있는 방법을 제시하였다. 인공신경회로망 기법을 통해 노심 반경방향 및 축방향 출력분포만을 이용하여 압력용기내벽 취약위치에서의 중성자 스펙트럼을 신속하게 평가할 수 있도록 중성자속 가중치를 생산하였고 데이터베이스를 구축하였다. 이 방법은 중성자 수송코드를 이용한 수송계산을 직접 수행하지 않고도 신속하게 압력용기 위치에서의 중성자 조사환경을 평가할 수 있으며 소송코드 결과와 비교하여 상대오차 3.4%이내의 정확도를 보였다.