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나상건(Sanggun Na),이용희(Yonghee Lee),김진성(Jinsung Kim),박성만(Sungman Park),허훈(Hoon Heo) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
In this paper, we describe study on fault diagnosis algorithm of hybrid electric vehicle to improve safety. SVM algorithm is proposed for the diagnosis of electric motor and battery fault. The proposed SVM algorithm is consist of SVDD and SVR techniques. The SVDD technique perform its diagnosis using the accumulated data, regardless of time. And the SVR technique make it possible of diagnosis of system based on continuity of data. The usefulness of the proposed fault diagnosis algorithm has confirmed via fault simulation. This study is performed by using of actual measured data from the operation of a commercial hybrid electric car. The algorithm can be implemented in real time frame and can be used to the fault monitoring system.
보일러 연소 모델의 자동 갱신 기능을 가지는 화력발전소 보일러 연소 최적화 기법 적용 결과
나상건(Sang-Gun Na),이정식(Jung-Sik Lee),맹좌영(Jwa-young Maeng) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
In this study, a combustion optimization technique with a function of automatically updating the boiler combustion model is introduced. Combustion optimization technique controls the combustion of a boiler in a thermal power plant to reduce pollutants and increase combustion efficiency, ultimately reducing fuel consumption, increasing boiler efficiency, and reducing the cost of treating pollutants. Multi-Layer Perceptron (MLP), an artificial neural network model, was used for the boiler combustion model, and Particle Swarm Optimization (PSO) was used for the optimization algorithm. Boiler combustion model is managed by model management module that automatically creates/selects/updates/deletes. This model management module was able to continuously update the model by using the operation data generated in real time. In addition, an output controller that converts the optimum combustion control value derived by this technique into a stable control signal value of the power plant controller was added to enable safe connection with the power plant controller. This technique confirmed the performance by linking with actual control in the form of S/W solution of edge computing server for thermal power plants in India. The plant has a capacity of 660 MW and the boiler is tangential firing type. In addition, the performance of this technique was verified by linking the control to the opposite firing boiler of the domestic USC 1000 MW thermal power plant.
SVDD 기법을 이용한 하이브리드 전기자동차의 고장검출 알고리즘
나상건(Sanggun Na),전종현(Jonghyun Jeon),한인재(Injae Han),허훈(Hoon Heo) 한국소음진동공학회 2011 한국소음진동공학회 학술대회논문집 Vol.2011 No.4
In this paper, in order to improve safety of hybrid electric vehicle a fault detection algorithm is introduced. The proposed algorithm uses SVDD techniques. Two methods for learning a lot of data are used in this technique. One method is to learn the data incrementally. Another method is to remove the data that does not affect the next learning. Using lines connecting support vectors selection of removing data is made. Using this method, lot of computation time and storage can be saved while learning many data. A battery data of commercial hybrid electrical vehicle is used in this study. In the study fault boundary via SVDD is described and relevant algorithm for virtual fault data is verified. It takes some time to generate fault boundary, nevertheless once the boundary is given, fault diagnosis can be conducted in real time basis.
보일러 연소 모델의 자동 갱신 기능을 가지는 화력발전소 보일러 연소 최적화 기법 적용 결과
나상건(Sang-Gun Na),이정식(Jung-Sik Lee),맹좌영(Jwa-young Maeng) 대한기계학회 2022 대한기계학회 춘추학술대회 Vol.2022 No.11
In this study, a combustion optimization technique with a function of automatically updating the boiler combustion model is introduced. Combustion optimization technique controls the combustion of a boiler in a thermal power plant to reduce pollutants and increase combustion efficiency, ultimately reducing fuel consumption, increasing boiler efficiency, and reducing the cost of treating pollutants. Multi-Layer Perceptron (MLP), an artificial neural network model, was used for the boiler combustion model, and Particle Swarm Optimization (PSO) was used for the optimization algorithm. Boiler combustion model is managed by model management module that automatically creates/selects/updates/deletes. This model management module was able to continuously update the model by using the operation data generated in real time. In addition, an output controller that converts the optimum combustion control value derived by this technique into a stable control signal value of the power plant controller was added to enable safe connection with the power plant controller. This technique confirmed the performance by linking with actual control in the form of S/W solution of edge computing server for thermal power plants in India. The plant has a capacity of 660 MW and the boiler is tangential firing type. In addition, the performance of this technique was verified by linking the control to the opposite firing boiler of the domestic USC 1000 MW thermal power plant.
SVDD기법을 이용한 하이브리드 전기자동차 충-방전시스템의 고장검출 알고리듬
나상건(Na, Sang-Gun),양인범(Yang, In-Beom),허훈(Heo, Hoon) 한국소음진동공학회 2011 한국소음진동공학회 논문집 Vol.21 No.11
A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.
In-Wheel Motor 차량의 주행안정성 향상에 관한 연구
이희재(Heejae Lee),박성만(Sungman Park),나상건(Sanggun Na),허훈(Hoon Heo) 한국자동차공학회 2010 한국자동차공학회 부문종합 학술대회 Vol.2010 No.5
Recently electric motors draw lot of attention as one of next generation power source in environment friendly energy. In-Wheel driving vehicle system furnishing electric motor directly to wheel has big advantage of improving its performance by removing all the existing power transmission elements, and provides new concept platform for better efficiency of the vehicle. In-Wheel driving system can implement flexible steering utilizing a speed difference of each wheel. However when motors have same characteristics, the control of complex steering mechanism becomes difficulty in terms of combined control of motor speed and brake distribution. Due to independent wheel driving, the In-Wheel vehicle system is influenced heavily by road condition. In this study, improvement of driving stability on different road conditions such as snowy road, rainy road and unpaved road etc. is focused. Algorithm for the stability control of In-Wheel vehicle driving system is proposed.